Рубрика: AI

ChatGPT has announced the global launch of group chats for all users on Free, Go, Plus, and Pro plans, according to OpenAI on November 20. This rollout follows a successful pilot programme conducted in select regions, such as Japan and New Zealand, just a week earlier.

“Early feedback from the pilot has been positive, so we’re expanding group chats to all logged-in users on ChatGPT Free, Go, Plus and Pro plans globally over the coming days. We will continue refining the experience as more people start using it,” the company said in a statement updated on Thursday.

The feature enables users to engage in joint conversations with each other and ChatGPT. OpenAI claims that this launch transforms ChatGPT from a personal assistant into a platform where friends, family, or colleagues can come together to plan, create, and make decisions collaboratively.

The company views group chats in ChatGPT as an opportunity for individuals to organise trips, co-author documents, resolve disputes, or collaboratively conduct research. At the same time, ChatGPT assists in searching, summarising, and evaluating options.

You can invite one to 20 people by sharing a link, which anyone in the group can also share. When you create your first group chat, you’ll need to set up a short profile with your name, username, and photo. Group chats are easily accessible in a designated section of the sidebar.
Responses are powered by GPT-5.1 Auto, which selects the best model based on the user’s plan. Features like search, image upload, image generation, and dictation are enabled. Rate limits apply only to ChatGPT’s responses and not to user messages in group chats. Responses count toward the limit for the user receiving them.

“Group chats are separate from your private conversations. Your personal ChatGPT memory is not used in group chats, and ChatGPT does not create new memories from these conversations. We’re exploring offering more granular controls in the future so you can choose if and how ChatGPT uses memory with group chats,” OpenAI said.

The post OpenAI Launches ChatGPT Group Chats Globally to All Subscribers appeared first on Analytics India Magazine.

Zoho’s Arattai is getting its biggest upgrade yet. End-to-end encryption is finally rolling out, and co-founder Sridhar Vembu is asking users to update the app. The green shield icon is ready. Chats will move to a new encrypted thread, while older conversations will be archived.

In two weeks, chat backups will arrive. Group encryption will follow. Everything is lined up. But there’s one problem: there’s almost no one left to use it.

A few weeks ago, Arattai looked like it was on the rise. The app briefly shot to the top of India’s social charts. It even overtook WhatsApp, Signal and Telegram. Government ministers praised it. Nationalist sentiment fuelled its momentum. Download surged and many opened it. And then, most never returned.

Countless posts on X and Reddit described users uninstalling the app owing to a lack of activity. From 1.36 crore downloads in October, Arattai fell to just two lakh downloads in November.

Today, it doesn’t appear even in the top 200 apps on the Play Store and has dropped to 11th in the Communications category. WhatsApp has absorbed nearly every meaningful feature. Multi-device. Encryption. Scheduling calls. Chat locks. Backups. The gaps closed, and Arattai ran out of incentives. A chat app without people ultimately becomes an empty room.

What’s Wrong?

This is not the first time India has tried to build its own messaging platform. Hike tried and fell. Koo tried and stalled. Network effects crush new entrants long before they find footing.

WhatsApp’s dominance is not just a story about features. It is a story about habit, and about identity.

To understand why, you have to begin where everyone begins: with phone numbers. Reverie Language Technologies co-founder Vivekanand Pani explained it best to AIM.

He explained that Yahoo! Messenger, MSN Messenger, AOL and ICQ were all wildly popular once. They each required sign ups. They each created separate identities. None could talk to the other. They hoped email IDs would help, but most phone users did not have email IDs. They had mobile numbers. SMS was expensive. IP messaging was cheap. The only missing link was identity. WhatsApp connected those dots.

WhatsApp did not ask users to change their behaviour. It sat on top of the phone address book. It used the phone number as the handle. It removed friction and offered free media sharing. It offered no need for extra sign ups. It simply rode the rush of the mobile internet adoption. Everything else came after.

That is the wall Arattai is up against. Encryption may be important, but most people do not act on it until something goes wrong. Blackberry proved that. It was secure. It was reliable. Only business users cared.

Pani believes Gen Z might still save Arattai. “They understand the need for encryption and privacy a lot better. They do not have too many elders to be in their groups. They are quick to adopt and form habits that would last. Their groups are still forming. Arattai may actually end up discovering a lot of specific usage needs from their behavior and differentiate,” he said.

He added that his first startup, Reverie Language Technologies, also developed a phone number-based messaging app in 2007. “That was our first product. But, as bootstrapped founders, we could not fund the servers in those days for a free messaging app for long and had to take it down,” he said.

What Happens Then?

Arattai’s rise earlier this year showed that people wanted to try it. The issue was not curiosity, but depth.

Inside the app, there was a steady list of features that Zoho had built for years: built-in meetings like Zoom, a ‘Pocket’ to save messages, a ‘Mentions’ tab to track conversations and a ‘Till I reach’ option for sharing travel. Moreover, there were no ads, no external clouds. All data stored in India. Zoho built everything in-house.

CEO Mani Vembu told AIM earlier that Arattai runs on Zoho’s oldest infrastructure, dating back to 2005. It powers Zoho Cliq internally. It has been scaled for years. The team knows how to run it. The spurt of new downloads was not stressful. They only had to add servers. The architecture remained intact.

Zoho prefers the long game. They do not push features in panic. They care about repeat usage, whether people open the app again tomorrow. This approach works in SaaS. Messaging demands something else. It demands universality. Messaging is not a place where slow and steady always wins.

The irony is striking. For a country that talks endlessly about digital sovereignty, Arattai should have been a stronger contender.

The timeline of Arattai’s rise looked promising. A privacy backlash hit WhatsApp and Telegram. Users looked for local alternatives. Arattai spiked. Then the wave receded. People opened the app and saw an empty contact list. There is no network effect for handshakes. If no one replies, a chat app collapses.

Commentary online shows the gap. Users welcomed the new encryption update. They asked for a lock icon; the team added a shield. They asked for theme changes; the team promised consistency. They asked for video call encryption; the team confirmed it. These are signs of momentum.

But the numbers tell a different story. People are simply not there.

Arattai’s team is still pushing updates. Still rolling out new features. Still improving the UI. Still engaging on social media. Still promising that more is coming. They are not giving up. As Sridhar said in an ANI interview, “Nothing goes straight to the moon. Real companies ride through rises and dips, and only those with a long-term mindset survive.”

The future of Arattai now hinges on one question: can an app that people have already abandoned make a comeback?

The turtle keeps walking. The rabbit keeps sprinting. The race is no longer the point. The point now is whether Arattai can stay long enough for a new generation to care.

The post Arattai is Getting Better. Sadly, No One is Using It. appeared first on Analytics India Magazine.

In 2017, Google introduced Transformers, the architecture that powers today’s large language models.

But, the mindset required to navigate the AI age was perhaps articulated 500 years earlier by an Italian polymath. Leonardo da Vinci, while painting The Last Supper in Milan, was also sketching flying machines driven by human muscle.

As he worked on the Mona Lisa, he dissected cadavers to understand how facial muscles form a smile.

The same notebooks that studied sfumato also mapped water flows, canal systems, and hydraulic devices.

By 1502, he even proposed a single-span bridge for Istanbul’s Golden Horn, centuries ahead of its time. None of this began with tools for their own sake. It began with questions: how bodies move, how cities function, how water behaves?

His strength wasn’t just designing solutions, but observing the world well enough to know what was worth solving.

Walter Isaacson describes in his book on da Vinci that the man had a curiosity that was “pure, personal, and delightfully obsessive.”

That principle matters more today than ever. AI can generate code, models, and designs on command. What it cannot do is decide which problems are worth solving. It mimics patterns; it does not discover purpose.

The true advantage now belongs to engineers who can do what da Vinci did intuitively, to read human needs, interpret how society changes, and turn observation into invention.

And that raises the demand for a polymath — engineer mindset.

These are people who can see what society lacks at scale, understand how human needs evolve, and apply AI to their domain with that perspective.

Amid many in the industry arguing that the future engineer must be a polymath, AIM spoke with Rob Vatter, executive president of Quest Global, an engineering firm serving aerospace, automotive, industrial design, and other domains.

Vatter links Da Vinci’s legacy to the future of engineering, not by suggesting we need more prodigies like him, but by arguing that the discipline must reward thinkers who refuse to stay in a single lane.
With AI taking over routine tasks, Vatter argues that the field’s most urgent challenge is no longer technical capacity, but intellectual drive. “AI will do all the other work, and so we have to return to what we refer to as a crisis of curiosity,” said Vatter.

And thus, leads us to the idea of engineers understanding people, discovering real problems, and interpreting the needs of society.

Because the challenge isn’t just building what customers ask for; it’s recognising that people often don’t know what they want in the first place. Henry Ford is often credited with the line: “If I had asked people what they wanted, they would have said faster horses.”

Remember, LLMs are Still Probabilistic Machines

Vatter states that it needs more engineers who obsess over problem definition. “Because that’s the one thing AI can’t do right now anyway — it’s all just patterns, right?”.

In his recent conversation with podcaster Dwarakesh Patel, reinforcement learning pioneer Richard Sutton reminded us yet again that large language models (LLMs) still mostly mimic people, rather than truly understand the world.

They predict what words should come next, not what will actually happen next, and they operate without grounded goals or a clear notion of “the right thing to do.”

“It [AI models] can help you catch a lot of stuff, but it won’t allow itself to break the frame and think differently about solving the problem, because it just has those data sets and processing,” said Vatter.

And then there’s only so much real-time information that you can embed within limited input context capabilities.

Citing an example from Quest Global, Vatter noted that engineers with business degrees or industry experience often stand out because they can move easily between nuanced business problems and technical work.
By understanding both the underlying engineering and the business logic, they make translation between business requirements, functional needs, and technical solutions far smoother.
One role that reflects this shift is the forward-deployed engineer, who works directly with customers, evaluates their needs and feedback, and converts that into engineering goals.

Another example is an AI product manager who can translate complex data models into business value, define the right KPIs, guide engineers, and shape a roadmap that connects AI capabilities with market needs and customer behaviour.

Opportunities for more roles like these aren’t clearly defined yet, but they’re poised to grow quickly.

The Harsher Reality

The argument for “Da Vinci-like engineers” is easy to admire. The harder question is that in a market where AI is compressing entry-level work and companies rarely reward breadth, how does anyone even prove they’re more than a narrow specialist?

The idea sounds like a luxury when jobs are scarce. Vatter doesn’t deny the mismatch. Most workplaces still don’t know how to measure interdisciplinary skills.

“If you go into most companies, you’ll find that there are polymaths, but they’re far and few between. It’s that person that everybody goes to when there’s a problem,” said Vatter.

“And that person may only have been a mechanical engineer, but by the time 25 years have passed by, they kind of know everything.”

He argued that the system won’t automatically provide a platform for this kind of thinking, so engineers have to earn it by consistently showing value, not by jumping straight to solutions, but by first identifying overlooked problems and demonstrating how they could be solved.
“If I were a 24- or 25-year-old engineer… I would be looking for things that can be fixed and problems that can be defined, and then bring them up to their bosses. Whether or not the bosses will say wow is another question,” stated Vatter, highlighting how companies should value such volunteering efforts.

So, it ends up being a multiplayer game where the employer builds a workplace that recognises problem-finding, and the engineer takes the initiative to solve it before being asked.

The post We Need More Polymath Engineers appeared first on Analytics India Magazine.

India is emerging as one of the most prepared AI workforces globally, according to Udemy’s latest research.

The report shows that 29% of Indian workers already believe they have adequate AI skills, while only 14% have avoided AI training, the lowest avoidance rate among the four surveyed countries. This contrasts sharply with the UK, where 55% have done no AI training, and the US, where 47% have avoided it.

These numbers set the context for Udemy’s growing focus on India, a market that the company’s chief customer experience officer, Neeracha Taychakhoonavudh, describes as both eager and prepared.

“Some of our biggest customers are here. The growth here is very good,” she said.

“I think there’s a learning culture in India that means that people are interested in acquiring skills and improving, and that is obviously a very good fit for Udemy,” she said during an exclusive interaction with AIM.

India’s Learning Culture Stands Apart

Udemy’s global research highlights a striking divide in AI readiness. Across the US, UK and Brazil, 40 to 44% of workers lack essential AI capabilities, particularly in learning AI tools and incorporating them into workflows. Western economies also show strong optimism bias, with workers far more worried about economy-wide job disruption than their own roles.

India does not follow that pattern. Udemy’s study found near-equal levels of societal concern (72%) and personal concern (66%) about AI’s impact. This balanced assessment is matched with action, explaining the relatively higher skill confidence in the country.

Taychakhoonavudh noted that this preparedness shows up in Udemy’s customer conversations too. She observed that across Europe, where she visited last month, there is a noticeably more conservative approach, particularly in relation to EU AI regulations which restrict certain actions. Consequently, the enthusiasm and demand for AI adoption are significantly lower there.

This difference positions India remarkably well, as the country demonstrates a strong willingness to embrace the undeniable technological shift that is currently taking place.

Even with this enthusiasm, she emphasised that budgets remain tight. “I would say in a world of macroeconomic uncertainty, budgets are either stagnant or being cut, so everyone has to make sure, I mean we’re all to be told to do more with less.”

In her view, the motivation for providing employee learning goes beyond altruism; it’s about developing specific, workplace-applicable skills and capabilities, not just for AI-related fields but generally.

She added, “I think ROI is true for AI, but it’s true for everything, and the learning and development teams need to move beyond consumption and activity metrics to actually say how does this actually improve the business.”

Abhishek Raj, head of marketing, Udemy India & South Asia, agreed, adding that AI adoption initially suffered from herd mentality inside Indian enterprises. Many jumped in without a plan.

He mentioned that the companies need a clear framework for their AI adoption, starting with defining their goals.

For mature companies aiming to be truly AI-driven, their adoption needs to be evaluated against the tangible results and business outcomes produced.

If the goal is simply to achieve a foundational level of AI literacy, that aim must still be directly linked to a specific business outcome for successful evaluation.

AI Fluency, Role-Specific Learning and Udemy’s New Tools

Udemy’s report shows why structured learning design is becoming essential. Even in India, many workers remain unclear about practical AI use, with 44% needing help learning when to use AI tools and 38% struggling with incorporating AI into workflows.

Rather than pushing generic content, Udemy is shaping programmes around functions. The platform already hosts more than 5,000 GenAI courses, but Taychakhoonavudh explained that course volume alone is not a solution.

She explained that the customer success team advises learners to first define specific, measurable AI learning objectives, moving beyond vague goals like “everyone can use AI.”

The goal could be basic training in ChatGPT prompt engineering for all employees or mastery of tools like LangChain for the development team. The entire learning program, including steps, incentives, consequences, and critical measurable metrics, is designed around this specific objective.
The company has enhanced its platform offerings with several AI-driven features, including AI Search and Recommendations to identify and suggest pertinent learning modules quickly. It also offers AI-Assembled Learning Paths, which generate customised learning journeys by combining relevant segments from various courses, eliminating the need to complete full courses.

Furthermore, AI Role Plays provide a safe, non-judgemental environment for users to repeatedly practice and refine behavioural and customer-facing skills.

Taychakhoonavudh described how her own team uses role play tools. Taking transcripts from customer calls and turning them into micro-learning modules now fits naturally into weekly workflows. “It is learning in the flow of work,” she said.

Raj added that even simple AI use cases create meaningful productivity gains in marketing. Tools like Canva now support faster iterations, reduced review cycles and more agile creative development. “It is not earth shattering, but it has made life substantially easier,” he said.

The Udemy report supports this observation. Across all four surveyed countries, the top AI gaps are learning AI tools, workflows integration, and system integration, signalling that everyday practical use cases are where most employees need support.

Competing in a World Where Learning Starts With AI Search

Another challenge highlighted in Udemy’s report is the avoidance of formal AI training in Western markets. Only 14% of Indian workers have avoided AI training, compared to 55% in the UK and 47% in the US.

This informs Udemy’s product direction. Workers often start with ChatGPT or Perplexity before thinking of a course. Instead of resisting this shift, Udemy is building for it. The Model Context Protocol (MCP) allows Udemy learning signals to appear inside whichever platform a professional already uses, including ChatGPT, Claude, Perplexity, Slack or internal agents.

Taychakhoonavudh said, “If you think that just going to ChatGPT will mean that you can learn things, I think you’re fooling yourself.”

She added that structured learning still matters. Platforms like ChatGPT cannot replace frameworks, practice scenarios or the diversity and freshness of real content on Udemy.

A Market Moving Quickly, and Udemy Moving With It

India continues to be one of Udemy’s fastest-growing markets. The company is stabilising its GCC operations and planning next-year initiatives with a stronger focus on role-specific upskilling and AI-powered learning delivery.

The Udemy report makes the stakes clear. Skill half-life is shrinking. In the US and UK, workers prioritise hobbies and financial goals over job-related learning. In India, 87% say they are motivated to build job skills, on par with other personal priorities. This alignment between belief and action is what differentiates the Indian learner base.

For Udemy, this means India is not only a growth market but also a demonstration of what a prepared workforce can look like.

“We have to stay really close to the market dynamics,” Taychakhoonavudh said. “Things are changing, and you have to be ready.”

The post India Leads the World in AI Readiness, and Udemy Says Its Learning Culture Is the Reason appeared first on Analytics India Magazine.

At the Bengaluru Tech Summit 2025, a long conversation unfolded around India’s push for sovereign AI. The session brought together Ankush Sabharwal of CoRover, which runs BharatGPT, and Karan Kirpalani of Neysa, a rising AI cloud provider empanelled under the IndiaAI Mission.

The discussion, moderated by Ravi Jain of TDK Ventures, touched on how the last three years have seen more transformation than all previous cycles of AI development combined. Jain noted that the landscape now spans giant foundation models and tiny edge-ready ones, new clouds and established incumbents, and even the early rumble of quantum technologies.

Meanwhile, India’s need for sovereign AI has never been more pressing.

Sabharwal explained that CoRover is advancing this mission by making life easier for citizens through enterprise AI, with its B2B2C model. The company began with conversational systems, but now offers full-stack solutions—from language models to ready-made agentic platforms for enterprises.

He added that their tools are already serving large volumes. “50,000 developers, researchers and enterprises have used our platform and created thousands of AI agents,” he said. Most recently, CoRover announced a partnership with Intel to roll out BharatGPT Mini for edge use cases, a move Sabharwal said would further help scale this sovereignty.

What is Neysa Up To?

Kirpalani described Neysa as a new kind of AI cloud. On the surface, its job is simple: renting GPUs in the cloud so enterprises and developers can train models or deploy them at scale. But behind that simplicity, the ambition is wider. Its motto, he said, is to help customers “use their AI as quickly as possible, as often as possible, as efficiently as possible, and in as widespread a way as possible.”

Kirpalani noted that the first reason Indians should use Neysa is sovereignty—every nation wants control over its AI stack. The second is cost, especially as scaling AI use cases on global clouds like Azure, Google Cloud or AWS becomes increasingly expensive.

He cited one Neysa customer that processes 410 billion tokens a month, an amount that the corresponding bill on a global hyperscaler would have been impossible for them to sustain. Neysa matched the performance, offered better guarantees on token latency, and delivered it at a lower cost.

Earlier during Cypher 2025, Kirpalani told AIM that through its Velocis cloud platform, the company aims to reduce India’s dependency on foreign models and data centres. “For India, investing across the stack and reducing dependency on foreign models, hardware and data centres is vital,” he said.

To support this mission, Neysa is building a massive 400 MW AI data centre with NTT, equipped to house around 25,000 GPUs. Kirpalani said the goal is to protect India from the shocks in global supply chains. Embargoes or geopolitical tensions, he noted, can slow GPU supply, but only temporarily.

The Mix of Hardware and Software

Models from global AI firms like OpenAI and Google are increasingly supporting Indic languages, and these companies are now eyeing India as their next big market for AI.

Sabharwal said BharatGPT supports more languages and far more slang. Global models get the broad strokes right, but often miss the layers that matter in India. He added that they do not claim superiority across the board. Instead, they focus on domains where they have practical experience.

He shared an example: when someone asked whether the platform supported Bhojpuri, the team assumed the answer was no. During testing, however, their Hindi model performed well enough to handle Bhojpuri because it had already captured the necessary variations needed for it to work. Sabharwal said this comes from solving real problems across Indian use cases rather than relying on general-purpose training. As a result, even global platforms sometimes use CoRover’s APIs for Indian deployments.

To prevent AI adoption from slowing in India, the entire chain must move in sync: developers, model builders, clouds and data centres.

Since data centres have long build cycles, they must be built ahead of demand. Neysa remains silicon-agnostic in theory. In practice, however, almost everything still runs on NVIDIA because of CUDA, as that parallel computing software is the real moat for the company.

He said global companies may enter India with force, but Indian builders understand local problems better and have more room to win.

Sabharwal said India is the best place to build AI platforms because “we produce data by just living life.” The volume of usage and the hunger for solutions give builders a natural advantage. He said India adapts to new tech faster than most places. The scale of problems is huge and the willingness to try new tools is high.

Every nation sees AI as a sovereign asset, and India is no exception. Kirpalani said the entire stack, from data centres to models to applications, must be built domestically.

Sabharwal believes AI will become as ordinary as a phone or laptop. People will use it without thinking about it. He wants CoRover to hold a significant share of that shift, supporting enterprises and improving everyday life.

Kirpalani said AI will become an invisible layer in daily tasks. Giving an example of how 4G became 5G and the internet just became fast, he said that a normal India might not have noticed it, but the tech shift was huge.

“The architectural technology change that had to happen in the backend was gargantuan. I think the measure of success of a technology—and this will be the measure of success of AI—is that the average person will wind up using it on a day to day basis,” he said.

The average person will use it without knowing they are using AI. From banks to call centres to basic digital services, AI will sit inside everything. And Neo clouds like Neysa will power that shift from behind the scenes.

The post BharatGPT & Neysa Lay Out India’s Strong Push for AI Independence appeared first on Analytics India Magazine.

Pibit.AI, an insurtech company building AI systems for underwriting, has raised $7 million in a Series A round led by Stellaris Venture Partners, with participation from existing investors Y Combinator and Arali Ventures.

The funding will be used to strengthen product development and accelerate industry-wide adoption of the company’s flagship Centralised Underwriting Risk Environment (CURE).
“Pibit.AI was built around one idea: that AI should empower underwriters, not replace them,” said Akash Agarwal, Founder and CEO of Pibit.AI. “Too many systems prioritise speed over trust. We’re building something transparent, explainable, and decision-ready, a system that gives underwriters confidence in every output while helping them move faster than ever before.”

Pibit.AI’s flagship is CURE, a unified, intelligent system that streamlines the entire underwriting lifecycle by handling submissions, document parsing, research, risk analysis, and workflow orchestration in a single platform.

ClearCURE streamlines triage, DocumentCURE extracts and structures submission data, and ResearchCURE enriches it with real-time insights, turning raw submissions into decision-ready outputs in a fraction of the time it takes today. RiskCURE adds portfolio-specific risk signals, while WorkflowCURE unifies all tasks, insights, and collaboration into a single workspace.

According to the press release, CURE users have reported up to 85% faster underwriting cycles, a 32% increase in gross written premium per underwriter, and up to 700 basis points of improvement in loss ratios. For insurance providers, this translates to higher capacity, faster growth, and sharper risk selection, it said.

Alok Goyal, partner at Stellaris Venture Partners, said, “Underwriting has long been constrained by manual reviews, inconsistent data and tools that haven’t kept pace with rising submission volumes.”

In the next 12-18 months, the company plans to enhance the CURE platform with advanced risk models, API layers, and stronger data partnerships to better address new business lines and emerging risks.

The post Insurtech Startup Pibit.AI Raises $7 Million from Stellaris, YC & Arali appeared first on Analytics India Magazine.

Google has introduced Nano Banana Pro, a new image generation and editing model built on Gemini 3 Pro. The company said the model brings reasoning, real-world knowledge and more accurate visual output, expanding on the earlier Nano Banana model released a few months ago.

According to Google, Nano Banana Pro can help users generate visuals from ideas, prototypes, notes and real-time information. The model can also access Google Search’s knowledge base.

“Nano Banana Pro doesn’t just create images. It helps you create helpful content,” the company said, adding that users can produce infographics, diagrams, recipes or snapshots using grounded information.

The company claims the model can render text inside images with greater accuracy and legibility across multiple languages. This includes longer text, stylised fonts, mockups and localised content.

The company also emphasised improved consistency when blending multiple elements. Google says the model can combine up to 14 images and maintain the likeness of up to five people.

The upgraded system introduces new controls for creators, including localised editing, camera angle adjustments, lighting changes and depth-of-field modification. Users can also export creations in multiple aspect ratios and resolutions, including 2K and 4K.

Google is rolling out Nano Banana Pro across its consumer and professional products. In the Gemini app, the model appears under the ‘Thinking’ option within image creation. Free-tier users will get limited access before reverting to the original Nano Banana. AI Plus, Pro and Ultra subscribers will receive higher quotas. In Search’s AI Mode, the model is available in the US for Google AI Pro and Ultra users.

For professionals, the model will be integrated into Google Ads, Workspace tools such as Slides and Vids, and Flow for filmmaking. Developers can access it through the Gemini API, Google AI Studio, Antigravity and Vertex AI.

Google also announced new ways to verify AI-generated content. All images produced by Google tools will continue to include SynthID watermarking. Users can now upload an image in the Gemini app and “ask if it was generated by Google AI,” based on SynthID signals. Free and Pro-tier images will also include a visible Gemini watermark, which will be removed for Ultra subscribers and Google AI Studio developers.

The company said the goal is to support transparency. “We believe it’s critical to know when an image is AI-generated,” Google said.

The post Google Unveils Nano Banana Pro as Its Next Major Image Generation Model appeared first on Analytics India Magazine.

LTIMindtree has announced the expansion of its global collaboration with Microsoft to accelerate Microsoft Azure adoption and drive AI-powered business transformation for enterprises.

As a part of the collaboration, LTIMindtree will enable faster cloud adoption and unlock enhanced business value for joint customers through advanced AI solutions.

As a Global System Integrator (GSI) partner for Microsoft, LTIMindtree is deepening its commitment to enable global enterprises to maximise their cloud investments, Azure commits and achieve faster time-to-value, the company said.

This collaboration underscores LTIMindtree’s ambition to deliver significant growth in Azure-related engagements, leveraging the strength of its 360 degree relationship with Microsoft across all the solution areas.

It combines LTIMindtree’s industry expertise with Microsoft’s advanced AI capabilities, including Azure OpenAI in Microsoft Foundry, Microsoft 365 Copilot, and Fabric.

Additionally, it will enable intelligent decision-making and automation across sectors, deliver secure and scalable cloud modernisation through Azure migration programs, and accelerate Copilot adoption to boost workplace productivity and enhance customer engagement.

As a strategic partner, LTIMindtree has deployed the full Microsoft Security stack, Defender XDR, Sentinel, Intune, Windows Autopatch, and Entra ID, across multiple endpoints, ingesting comprehensive security data monthly for automated threat response.

The security-first approach positions LTIMindtree as a model for secure hybrid and multi-cloud environments.

Complementing this, LTIMindtree claimed that it is leading the way in enterprise AI with internal adoption of Microsoft 365 Copilot.

Guided by a governance-first rollout, Copilot is now embedded across workflows to enhance productivity and accelerate decision-making.

“By accelerating Azure adoption and embedding AI into every business process, we are helping customers move from pilots to productivity, unlocking innovation, resilience, and growth at scale,” said Venu Lambu, CEO & managing director, LTIMindtree in a press statement.

Stephen Boyle, vice president, global system integrators and advisory partners at Microsoft, said that by adopting Microsoft AI across its own enterprise and applying those insights to help customers modernise and scale responsibly, LTIMindtree is setting the standard for transformation.

The post LTIMindtree Expands Partnership with Microsoft to Accelerate Microsoft Azure Adoption, Drive AI-Powered Transformation appeared first on Analytics India Magazine.