A shift Beyond LLMs & AI Systems!!

Enterprises are not limited by data or even agent orchestration. They are limited by decisions, reasoning & speed under constraints in dynamic environments. This calls for Deeptech AI infra.

Though building agents is becoming easier, Decisioning, Reasoning across multiple domains needs deeper engineering & research & systems beyond LLMs.

We can get clue from Meta in emerging category & whitespace opportunities. Meta is exploring AI models that simulate how brain-like signals respond to sound, images, and the environment.

As these models mature, they open up unprecedented opportunities for enterprise stacks— This is path less taken yet.

Vaayu AI Research Team is working on it for niches for Cleantech & finance. More specifically, Lead-To-Revenue, Arbitrages in Invoice financing, Latent Impacts, Reasoning under constraints & dynamic environment.

Understanding Meta’s TRIBE Model & emerging shift from LLMs

Meta AI models, especially TRIBE v2 simulate how brain signals respond to sound, images, and the environment (See Video).

While LLMs predict next token, human brain constructs entire scenario by having multiple brain parts activated by sensory data. A brain part called Hippocampus builds entire scene or call it perception. Then comes reasoning, decision & Control.

Instead of just predicting text, TRIBE-like approaches aim to:

• Learn representations of how the world behaves
• Understand relationships between signals (not just sequences)
• Simulate how situations evolve over time

Think of it as a shift from:

• “What is the next answer?”
  👉 to
• “What is likely to happen next in this environment?”

Understanding the emerging category for Enterprise Decisioning

For Enterprises – A C-suite decides & takes decisions by perceiving a context, predicting next environment or business scenario & deciding action plan or strategy.

In a similar direction, Enterprise stacks for Fintech, Cleantech open new unprecedented opportunities once these models mature.

Instead of one model giving an answer, intelligence begins to emerge from interacting units responding to context & goals & percieving the context all in one go. This is closer to how Human brains work.

Distributed Artificial Intelligence experts & intelligence & deciding action was the core themes in our patent

Vaayu’s work for enterprises in similar yet distinct & niche direction

Deeptech AI Infra

We define this as decision or cognitive infrastructure: systems that continuously observe enterprise data, reason under constraints, evaluate strategy against periodic dynamic context of enterprise & market and orchestrate execution & provide feedback loop for any adjustments.”

This can lead to a 5-20% increase in revenue. (Depending on the use case & effectiveness of implementation & environment. Reference – our internal case studies with Solar clients, PwC, BCG)

At Vaayu, we’ve been working on a small, applied slice of this broader direction, but for enterprises. More specifically for Cleantech & Financing. If you take very narrow field – then even smaller slices as I mentioned above.

When Vaayu AI Research Team(Founders only at that time) spotted a gap it looked simple at surface: companies already have data, but lack systems that can continuously observe, reason across signals, and act. More so, many important decisions can only be taken by C-suite who understand the market & enterprise context deeply

It consists of scoped infra such as Data, Small Reasoning Model specialised to Cleantech, Strategy to Execution orchestration layer & model improvements backed by research to improve reasoning, decisions & controls for Cleantech.

Our focus of research is Latent Impacts under constraints, Distributed Artificial Intelligence. Enterprise Decisions have latent impact of one decision on another & impact the end outcome. Also these are taken under constraints of enterprise & market context.

Vertical intelligence & niche focus

Our approach is to build a “Semi & fully Autonomous Decision making at Enterprises”—focused on cleantech-linked vertical flows for Lead-to Revenue, Production planning, Solar Deployment by EPCs, and responding to proposals for production & identifying arbitrages, especially in invoice financing.

While Meta is exploring this at a foundational level more so in mulit modality of image, videos & sound, we’re building Infra backed by focused research & an Enterprise stack(Revenue Engine) to operationalize parts of it within enterprise environments.

A truly Autonomous Enterprise

A truly autonomous enterprise will achieve its goal of Revenue Efficiency. Even a 2–6% improvement in gross profit can be materially significant for a listed company.

Vaayu Enterprise AI systems can observe, reason & do decsion making closer to how C-suite leaders evaluate enterprise and market conditions to build a strategy. Our Infra enables reasoning a likely optimal strategy with C-suite in loop, operationalize it with human & AI co-workers, evaluate that strategy as it’s operationalised & enterprise & market context dynamically changes & provide much faster feedback loop & response to changed context.

It can thus take swift actions, much faster & intelligenct Lead-to-revenue & ops workflows that I mentioned, they can spot arbitrages and improve profitability. This intelligence decision-making loop becomes much faster with AI Model

Enterprises already have a lot of data—about themselves, partners, and a constantly changing internal and external environment. Need is how to make sense out of this with intelligent decision making.

Example of Enterprise Decision making coming together for Cleantech

A CEO of Solar OEM in Gujarat has a goal to uplift revenue in 2026 by increasing business. He decides to expand to other states, and allocate marketing budget to various channels.

His strategy has to include constraints of resources, marketing budget allocation strategy, cash flows fluctuations, pipeline, and production planning, and market conditions. All these are dynamic. So the strategy once decided has to be observed, reasoned for changes required & orchestrated.

See pic below

Low-mid capex Deeptech

Foundational models like Thinking Machine Labs or General Intuituion work on frontier AI. However Adept, Manus even to some extent Deepseek shows that it’s possible to work on Small Reasoning models, orchestration.

Small AI Reasoning model, orchestration – Vaayu’s work

We propose a pragmatic & well rounded path to execution. This we have been slowly building. We are proposing an AI Pvt lab on Applied AI Reasoning along with Vaayu Enterprise stack already used by Cleantech companies.

A team of AI Researchers under Vaayu’s founders. We call it Engineering & market-led research —that makes Deeptech infra more viable by shortening the pay back period of investments)

Our plan is to setup business office in San Jose & a research & tech team in Pune to collaborate & expand GTM in US & India.

Deeptech Infra for Cleantech, Co-workers with Decision intelligence are some of the powerful areas of innovation. If working in a similar field, let’s exchange notes, collaborate or have a roundtable with experts.

Read more – Beyond Context Graphs – AI Reasoning & World models

Read more – Sir Bikhchandani’s thesis applied to Deeptech & AI