Enterprise World Model – Next Deepseek Moment?

The Deepseek Moment from India?

What if, the Enterprise World Model – Next Deepseek Moment becomes reality? What are asymmetric Opportunities, Readiness & next steps?

We take a look at technology, understanding what a World Model is with examples in Banking & draw a conducive environment for Deepseek-like moments.

Fundamental anatomy of Deepseek-moment

At a fundamental level, Deepseek moment was created when a Chinese company could align their AI Reasoning model releases around big US investments & technology releases & timing.

Another fact is that it was created considering the realities of the Chinese ecosystem. Which has now grown, but not as mature as that of US.

What if, World Models could be sneaked in, aligned with releases by Deepmind, Meta, NVIDIA?

We examine – Why, How, What? Why only the Enterprise Model, why not for the physical world?

What’s an Enterprise World Model?

Have you heard someone say – “This is how the world works nowadays”?

So, they are pointing to “Some hidden relationships including 2nd order & 3rd order impact of data & systems, state of the world & how the world would react to your action (dynamics). Further Decisions & Policy making based on the above have to be tailored.

It sounds simple enough now?

Not really. Above are 4 different axes & whole fields of Mathematics & Computer science in themselves, & people can do multiple PhDs in each of the above.

Why Enterprise World models are billions of dollars of opportunities each?

Baselining the size of a widely accepted opportunity

Ashu & Jaya’s article – & Valuation of Context/Knoweldge graphs Startups

Ashu & Jaya Gupta’s article “AI’s Trillion-dollar Opportunity” was taken with enthusiasm. If we use the same baseline, per sector, there are several billion-dollar opportunities.

Proving their thesis is Atlan – which reached $800M valuation building Context graphs alone.

But we argue, Context or Knowledge graphs aren’t enough. & barely scratch the surface. We propose that value of System records is projected based on past Enterprise Software market evolution.

Recent developments in World Model, JEPA, AGI etc may cause the Decision traces as a system of records have limited value.

Still you may use above as baseline for size of widely accepted opportunity

Palantir as baseline

Another important reference benchmark is Palantir. While its focus spans government and large enterprises across different sectors, Palantir operates in a closely adjacent category: turning fragmented enterprise data into operational intelligence and decisions.

Palantir now expects approximately $8.15B in 2026 revenue, following another major guidance increase, making it one of the strongest examples of how large the enterprise AI / decision-intelligence layer can become.

Why do we say, billion-dollar? Because from Founders’ & the view we have rather broken it down to specific sector & their niches & even built some products, got alpha users in mid-corporates & shown it to large enterprise CEOs.

Enough is Enough. LLMs/SLMs/Context Graphs aren’t enough

The biggest shortfall of “Decision traces” thesis of Foundation capital – Ashu & Jaya Gupta’s thesis is this –

All other solutions make decisions faster not optimal

Take, for example, your employees in Enterprise – always saying – Because ABC discount was given in so & so situation, it should be given in this case.

While decision traces have value in their own right. But these aren’t of much value for very high value use cases, as below

• To take more optimal Decisions, which align with end goals of enterprise, such as Revenue & Profit while safeguarding the enterprise interests
• To take optimal actions actions
• To make an Enterprise policy
• To predict future state of decisions today, Including 2nd order & 3rd order impacts

So that means decisions & reasoning, the relations of the various factors remain in heads of people. The world model of enterprise is still in heads, based on intuition than data & simultations.

Why World Models can be Deepseek Like

Bigtech are focusing on Humanoids, Autonomous Cars & Robotics

Bigtech funding is focusing on larger markets & wider impact. This has left space in niches wide open. Additoinally since tech is so underdeveloped & needs , skills – it specialised

Enterprise world models are underserved, overal

Deeptech Enterprise Decision Intelligence Infra for Deeepskee moment

You may not realise the power of the world model from the definition we gave at at the start until, you find & relate to a real-world use case in enterprises & how scarce is the real work on it combined with who else is working on it.

So, in this section we cover these aspects.

An example – Vaayu World Model for Banking

We have presented to some of the largest banks in India & a few worldwide also. All these firms have direct access to OpenAI, Facebook, Google. If they wish, they can even talk to CEOs.

Systemic Risk & Micro financing with World Models

Despite existing Early warning systems, Many banks in India faced Systemic risk in microfinancing in recent years. Micro financing in India is so lucrative, while it has risks to spiral out. Cracking this optimally with right decisions & early warning systems with world model.

When such Systemic Risk in making, it’s very hard for current systems to catch. Nor the LLMs/SLMs can catch.

Portfolio allocation – maximizing yield & minimizing NPAs

Even bigger impact on P&L. Let’s see a company that has to allocate loan books for various pin codes. Situations are changing daily. Eg. new fintechs may be entering that market. A market may be dependent on retail footfalls & which may be dependent on another industry in the nearby. These signs may be reflecting in the bank with UPI transaction data, but no one ever noticed such relationships.

A world model can detect potential portfolio stress in particular areas which may be showing up only after 6 months.

World model differentiation over LLMs/SLMs/Knowledge Graph & Traditional methods like Decision trees

You may speak to bankers about Opex Cost. We have spoken to many. Many decisions in Banks are still manual. Eg If the loan size exceeds merely 50L ($600k), the process becomes too manual. Apart from Retail Banking, decisions have been largely driven by human intelligence.

This means lower profits, high opex.

World Model for Strategic Goals of the Bank

Quite a few PSUs have a mandate for privatisation. Other banks/fintechs are raising funds. When banks have tried to do that, the tough questions have come up.

• What is the moat or differentiator in terms of AI/Digital stack or Proprietary Data?
• Operating Efficiency, Impact on Profits, Yield, NPAs
• Alpha in terms of Digitazation or AI that a bank can show for long term scale

These directly impact the ability of the bank/fintech to negotiate the fundraising valuation & timelines. In fact there’s public news that few banks & fintechs had broken conversation on pricing value. One of the factors impacting price was preparedness on AI.

World Models as Alpha – Potential Deepseek moment for Bank, Vaayu & Investors

Each bank is finally going to have Voice AI Agents. That’s not a moat anymore. Legacy bank may take little longer, but that will still not be a moat.

This impacts the banks on long term operating efficiency & its ability to scale.

While LLMs/SLMs are at least 12yr old technology. World Models are still developing.

It got renewed interest only in 2018 & still very underdeveloped.

There are only around 1700 papers on JEPA World Models. While LLMs have ~2,50,000-300,000 papers published.

So, when a bank, Vaayu & Investors can leverage new emerging tech, they have created an alpha.

Understanding World model with Visual examples

An Arbitrage & Cash Flow Monitoring by Vaayu

Visualizing Hidden Relations

Below shows the simplest of examples of how a model can learn the hidden signals without even specifying what to learn. First, second & 3rd order impact.

Vaayu World Model Gym – 1M parameter model

At Vaayu World Model Gym, we built a 1M parameter model to teach it to read hand written letters.

The goal of exercise is to showcase how hidden relations can be taught to a model on the simplest of problems & compare various approaches. Thusthe readers understand why understanding dynamics of, hidden signals can be so powerful.

First let’s see, if humans had to do feature engineering. So, we try to bring dots of letters which are similar nearby. Eg. If there’s ink density around a particular part of letter, they are likely to be similar.

You see, such an approach results in unusable output or at least is very costly.

In the second example, we used PCA(Principal Component Analysis) to represent an image of letter in 3D space. Again,a hoping the similar letters come nearby.

Looks somewhat better.

1M parameter model learning the hidden signals

Here, if you see, similar color dots tend to get in a similar zone. In reality, in an enterprise, there may be 1000s of learned parameters across 10s of data streams. Below is the simplest of demonstration of the simplest of parts of entire Deeptech infra.

But it gives you a gist & visual representation of how hidden signals are learnt by model.

In the human world, you would have called it experience or gut feeling. Humans make sense of various domains & trying to interrelate them with their internal world models.

The gut feeling & intuition-based decisions by humans reflect similarity in usage of their world models to understand it easily.

But, what if – this intuition can be data-backed. Augmenting human CEOs capabilities.

Right positioning for Enterprise World Model & Deepseek-like moment

Avoiding head-on Competition with Bigtech on Physical AI

I met a founder who provides data from India to US Humanoid companies. There are tons of data exported India – from manufacturing units to Resturants to even temples.

So, basically, once those models by US BigTech companies are built, things can swing in both ways – A smaller company may become too huge or will be simply killed.

At Vaayu, we avoid head-on competition with BigTech. It’s better to be in a niche, under the radar & wait for right moment.

Just like Deepseek released AI reasoning model around OpenAI release. Cursor got lucky after windsurf was acquired.

While most companies working on the World model are either Physical AI or are focused on Humanoids or Robotics. We choose it for Banking, Cleantech – Decision-heavy, high opex, High data industries.

That’s when Achal & myself decided to come back from Germany & US respectively, to build Vaayu.

Rarity – 20-40 companies/Institutes building world models in modern ways worldwide

List of significant companies/institutes working on World models

We also include a few adjacent companeis to showcase distinction.

Organization / Lab	Category	What they represent
Atlan	🟦 Knowledge / Context	Data catalog and enterprise context layer — connecting metadata, data relationships and organizational knowledge.
Attraian	🟦 Knowledge / Context	Knowledge graphs / enterprise context — connecting information and relationships for AI.
Aily Labs	🟩 Decision Intelligence	AI-powered decision intelligence and business insights for enterprise decision-making.
Palantir	🟩 Operational Intelligence / Decision Support	Leading enterprise operational-intelligence platform, integrating data, ontology, analytics and AI to support real-world decisions and operations.
Vaayu	🟪 Enterprise World Model	Enterprise World Models & Decision Intelligence — modeling enterprise state, relationships and dynamics to predict outcomes, simulate decisions and drive actions.
IIDA Lab	🟪 Enterprise World Model Research	Research into Enterprise World Models, Decision Intelligence and AI infrastructure, extending world-model concepts to complex enterprise systems.
Wayve	🟥 Physical World Model	World models for autonomous driving and simulation.
Waabi	🟥 Physical World Model	Generative AI and world models for autonomous trucking and physical-world simulation.
World Labs	🟥 Spatial World Model	Spatial intelligence and models for understanding and generating 3D environments.
Google DeepMind	🟥 Physical / General World Model	World models, reinforcement learning, simulation and embodied intelligence.
NVIDIA Research	🟥 Physical World Model	Physical AI, simulation and world-foundation models for robotics.
Meta FAIR	🟥 World Model Research	JEPA and predictive representation learning for modeling the physical world.
OpenAI	🟥 World Model Research	Generative models and research exploring physical/spatial understanding and simulation.
UC Berkeley	🟨 Academia	World models, model-based RL, robotics and planning.
Stanford	🟨 Academia	Robotics, simulation, perception and embodied intelligence.
MIT CSAIL	🟨 Academia	Robotics, learning, planning and physical-world intelligence.
Carnegie Mellon	🟨 Academia	Robotics, autonomy and learning-based world models.

Short-circuiting the conventional wisdom laterally

Assymetric outcomes come with unconventional execution on non-obvious opportunities.

After I got exposure to Researchers in London & Germany at MNCs, I figured out the Business & dev structure of how Innovation, Productization & business models work. Contrary to general beliefs, Deepseek moments can be built from India.

Achal was working in US at that time on continent scale data aggregation.

Both of us then started Vaayu with this renewed understanding to build new way, new path.

Further, Vaayu played an instrumental role in building the registered non-profit Innovators & Industry Deeptech AI Alliance(https://iidalabs.org/). Which is sort of a top-notch Innovation Club where people collaborate, practice, build on top-notch business & tech work.

Enterprise World Models & Deepseek like moment

So, like Deepseek achieved with frugality, creativity under constraints with the right timing & contrarian thinking.

Similarly, it needs contrarian thinking. Vaayu is in talks with VCs & large Enterprises. The real win is category leadership, where a single shot should have the potential to make it to the leading category list.

Vaayu already ranks 229 worldwide among 1300 companies in Decision Support Systems. Where Palantir is the worldwide category leader. While for the niche of World Models & Advanced AI tech – Vaayu features in top 20-40 companies.

Beyond Obvious – Decision Intelligence backed by Deeptech AI infra

AI Co-workers backed by Decision Intelligence, Applied AI reasoning