Рубрика: AI

AI world news.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Melento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Melento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Melento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Melento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Melento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Melento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Melento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Melento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Memento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Memento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Memento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Memento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Memento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Memento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

Why are Only 5% of Mega GCCs in India Home to 50% of the Talent?

Only 5% of India’s global capability centres (GCCs) qualify as mega GCCs, yet these 88 centres account for half of the country’s entire GCC workforce, according to Zinnov’s new report titled ‘Unlocking the Advantage of Mega GCCs’. And their influence is set to grow.

More than 230 additional companies are expected to join this elite cohort over the next five years.

To understand this shift, it is necessary to revisit what a GCC represents. GCCs are offshore units set up by multinational companies to run strategic, knowledge-intensive functions, spanning technology, engineering, digital operations and shared services, drawing on India’s vast talent pool and operational strengths.

Mega GCCs sit at the top of this hierarchy. These are centres with more than 5,000 employees and supported by parent organisations generating over $1 billion in global revenue. They stand apart because of their scale, influence and global impact.

This brings us to wonder how India’s GCCs are entering a new era defined not by scale alone, but by strategic depth, global ownership and enterprise-wide innovation mandates.

Talking to AIM, Namita Adavi, partner at Zinnov, mentioned that mega GCCs stand apart because “they operate with strategic intent and deep global ownership.”

These centres are no longer mere extensions of global teams. They replicate the enterprise itself, shaping architecture, influencing global decisions and delivering high-value capabilities across areas such as AI, cloud, cybersecurity and digital engineering.

What Makes a Mega GCC Different?

Mega GCCs are distinguished not just by size, but by the criticality of the work they lead.

Spanning BFSI to automotive sectors, these centres anchor global functions, run large-scale platforms and accelerate modernisation agendas.

Piyush Kedia, co-founder and CEO of InCommon, told AIM that “mega GCCs that work well have one thing in common: they focus on ownership, not output—and that shift changes everything.”

Over a three to five-year horizon, he noted, these centres often become the place where enterprises test new bets, incubate cross-functional pods, and groom future global heads of engineering, product and operations.

Adavi highlighted similar trends, pointing out that JPMorgan Chase’s India GCC runs core platforms for payments, risk, and digital operations. Amazon’s India teams drive rapid experimentation for customer experience and personalisation systems deployed worldwide.

Meanwhile, Wells Fargo’s engineering teams shape global risk, fraud and servicing platforms. In the automotive industry, Mercedes-Benz Research and Development India leads key components of the software-defined vehicle roadmap.

Close to 90% of mega GCCs sit in the portfolio or transformation stages, where India teams influence architectural decisions and modernisation programs.

“Leadership models set them apart as well. Target’s India leadership, for example, manages both centre operations and global product or engineering portfolios, turning the GCC into a launchpad for enterprise-wide innovation and leadership,” Adavi added.

Why a Few GCCs Capture Nearly Half of India’s GCC Workforce

Mega GCCs scale because they operate as fully integrated replicas of the global enterprise.

Adavi noted that semiconductor leaders such as Intel, Qualcomm and Texas Instruments run design, verification, silicon engineering and AI-driven tooling under one roof in India. This vertical integration makes India the technical heartbeat of their global R&D.

Furthermore, Cisco leads network automation, cloud operations and advanced security engineering from its Indian centres, while Amazon and Target unify product, engineering, data and operations to strengthen end-to-end execution.

At the same time, HSBC and Wells Fargo maintain tight global alignment while driving India-led innovation and local talent strategies.

Long-term presence also matters. Mature engineering-led GCCs have built deep competency in digital platforms, enterprise systems and complex R&D. With scale, strong governance, and high-value mandates, these centres naturally evolve into engines of global transformation.

Neeti Sharma, CEO of TeamLease Digital, revealed in a conversation, “Most of them have been in India for over a decade, having started with support and technology functions and now gradually becoming innovation and research hubs for their HQs.”

Today, over 1,800 GCCs hire more than 10.4 million employees for the most niche roles. They pay over 25-30% higher than other industries. This makes their roles more attractive to employees. Hence, attrition is also as low as 9-10%.

It takes some time to build brand awareness for smaller or newer GCCs just entering India, according to Sharma. It takes some time to build brand awareness, thereby making it difficult in the initial phase to attract talent.

Sharma explained that for many of them, partnering with service providers under the build-operate-transfer (BOT) model enables them to initiate hiring and then scale as they increase in volume and size. Over 200 centres have also expanded into tier-2 cities, benefiting from 20-30% lower costs and strong digital talent pools.

However, this raises a larger question: Does this level of concentration risk creating innovation silos?

Adavi explained that the concentration is fuelling ecosystem-wide collaboration, not fragmentation. “This talent circulates across the ecosystem,” she said.

High-skilled professionals trained at Intel, Qualcomm, Bosch, Cisco, JPMorgan Chase, HSBC and Wells Fargo often move into emerging GCCs, high-growth startups, global technology service providers, etc.

However, Alouk Kumar, founder and CEO of Inductus Group, mentioned that this concentration of 50% of high-end talent within 5% of mega GCCs poses a systemic concentration risk that might threaten the foundational value proposition of the entire GCC phenomenon in India.

“To secure the future of the $450 billion GCC vision, we must move beyond simply competing for a finite pool and proactively engineer a diversified and self-sustaining talent supply chain,” Kumar added.

Will Mega GCCs of the Future Need 10,000 People—or 1,000 Specialists?

As AI, automation and digital transformation reshape work, the scale equation is evolving.

Yet, the idea that future GCCs will shrink is misplaced.

“The future will not be 1,000 specialists replacing 10,000 employees. There will be 10,000 professionals who operate with specialist-level depth and global impact,” Adavi said.

Industry trends reflect this shift as companies deepen their capabilities in India.

Continental is expanding in model-based engineering, digital twins, simulation, and performance computing, while Texas Instruments is driving AI-assisted design and next-generation verification flows.

Cisco’s India teams are scaling network intelligence, cloud management platforms and advanced security engineering.

BFSI leaders like JPMorgan Chase, Wells Fargo and HSBC are rebuilding talent around AI-led risk systems, fraud analytics, data engineering and platform modernisation.

These examples make one point clear. Mega GCCs are not becoming smaller; they are becoming deeper, more technical, and more globally consequential.

The post Why are Only 5% of Mega GCCs in India Home to 50% of the Talent? appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Memento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Memento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.

AI Redefining Compliance Verification for India’s Gig Economy

India’s gig and quick-commerce sectors have surged to nearly 12 million workers in FY 2024–25, up from 7.7 million in 2020–21. It is expected to double by 2030, according to industry estimates. With blue-collar gig hiring rising 92% in 2024, delivery and mobility platforms are onboarding workers faster than their compliance teams can process them.

The rapid hiring cycle has raised concerns about safety, fraud, and identity-based risks, pushing companies toward AI-led verification that can validate identities, detect anomalies, and monitor compliance in real time. However, adoption remains uneven, creating significant vulnerabilities in a workforce that underpins India’s hyperlocal economy.

Companies like Melento (formerly SignDesk), Ongrid and Unstop are showcasing how AI is changing compliance and verification workflows and why digital maturity remains a bigger challenge.

The Push to AI-Native Compliance

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has seen AI reshape its contract management engine.

The company’s founder and CEO, Krupesh Bhat, said AI now performs the first layer of contract reviews. “It helps identify the template and runs the first review based on the playbook that’s created. It does redlining, highlights risks in contracts, and even tracks milestones and deliverables,” he said.

Bhat added that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices. Yet the hesitation does not come from the fear of AI. “Clients don’t fear AI, they just want clarity on its purpose, boundaries and oversight,” he said.

The regulatory landscape contributes to that uncertainty. There is still no consistent rulebook for gig-worker compliance, and state-wise enforcement is fragmented. Instead, the sector is informally guided by emerging principles such as algorithmic accountability, fairness in worker classification, transparent decision-making and minimal data use.

Memento is already reorienting itself for this shift. “We are transforming into more of an AI-native product company,”Bhat said. “AI allows us to launch new products and new features quickly. On a net headcount basis, I may not be able to reduce the number of people, but I’ll be able to offer more services and solutions in the market.”

Adoption Bottleneck

While AI tools are becoming stronger, Bhat said adoption depends entirely on an organisation’s existing processes. “Many internal processes still rely on email and spreadsheets. They negotiate contracts manually and lack a contract repository. AI is two steps ahead of where many companies are in their automation journey,” he noted.

The gap is particularly evident in the gig economy, where many partners are small vendors or franchise operators with limited digital infrastructure. This mismatch between AI capability and operational readiness remains India’s most significant compliance risk.

Beyond compliance platforms, AI is now handling hiring at scale. At Unstop, which works extensively with campus and early-talent applicants, AI now powers nearly 80% of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, said hiring volumes have grown three to five times, making AI unavoidable. “It flags anomalies in resumes, identity mismatches, and other risks with over 92% accuracy, he said. “AI automates the first layer of verification, cutting manual review time by 70% and ensuring every candidate goes through a uniform, skills-first evaluation.”

To strengthen fairness, Unstop trains its models on anonymised, balanced datasets across demographics and income groups. “Bias prevention starts with data hygiene,” Agarwal said. The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits and routes a case for manual review if AI confidence drops below 85%.

Selective Automation

At Ongrid, a background verification company, AI is applied more selectively, often as a complement to human oversight. Its chief technology officer, Ajay Rao, said AI helps accelerate parts of the process. “AI helps flag potential discrepancies and speeds up verification, but human review is still critical, especially when dealing with sensitive PII and legal compliance,” he said.

Rao added that generative AI models are increasingly useful for extracting structured data from documents, analysing risk patterns and identifying missing information.

Cost, Infrastructure Remains a Barrier

Meanwhile, Memento’s Bhat highlighted a persistent challenge. “The trust associated with AI is one of the main hurdles. Even though we don’t use customer data to train our models, some clients worry about data privacy,” he said.
Another challenge is the cost of AI systems. For many Indian companies, it’s easier to hire entry-level staff than pay for AI tools. “AI is expensive, usage-based and still requires complementary digital tools,” Bhat explained.

Advanced AI systems, especially LLM-driven workflows, require strong infrastructure, reliable data flows and dedicated compliance governance. For companies operating on thin margins, these overheads can outweigh the benefits, slowing adoption even when risks are high.

Melento’s has built its internal AI playbook around explainability, human oversight, and auditability. Every reviewer action or AI decision is logged, traceable, and reviewable, a requirement that many clients insist on before giving a go-ahead for AI deployment.

Growing Marketing and Widening Gaps

India’s identity-verification and background-check industry is expanding rapidly, supported by the rise of gig platforms, BFSI and shared-services firms.

The country’s identity verification market size reached $451.1 million in 2024. IMARC Group, a leading market research company, expects the market to reach $1.72 billion by 2033, with a growth rate (CAGR) of 16% from 2025-2033.

Industry analyses also indicate rising identity-related discrepancies in logistics and delivery-led sectors. These trends have intensified demand for AI-assisted verification, particularly in high-velocity environments where human teams struggle to keep pace with scale.

Despite the barriers, the advantages are driving adoption. “AI is transformative when combined with human expertise. It allows our teams to focus on higher-value tasks while AI handles routine data processing,” Rao said.

As India’s gig economy continues to expand, the pressure to build faster, safer and more transparent verification systems will only grow. AI may not replace compliance teams, but it is rapidly becoming their most critical tool, especially in a labour market where trust, speed and scale are inseparable.

The post AI Redefining Compliance Verification for India’s Gig Economy appeared first on Analytics India Magazine.