Finance, operations, sales, and the field each run their own system. Each one records its own slice. None of them holds how the whole company actually works, so no one sees a decision’s downstream effects until they land. What’s missing isn’t data. It’s a single environment that holds the company as one structure. The gap is architectural. Vyom is the layer that closes it.
Companies don’t fail to coordinate because they lack data. They fail because their data lives in separate systems that each optimize one domain, and none of them represents how the domains depend on each other. A supplier slips a date. Somewhere downstream a delivery commitment breaks, a production schedule needs resequencing, and a cash forecast moves. The connections are real, but no system holds them, so a person has to reconstruct the chain by hand (usually after it has already cost something).
This is true in every company we’ve looked at past roughly $25M in revenue. The failure shows up in three forms, but they share one root. Fragmentation: each system optimizes its own domain, and the gaps between them are where coordination breaks. Causality blindness: systems record what happened, not why or how a change propagates. Static logic: rules configured by hand stay fixed while the business moves, so reality and the system drift apart.
None of these is a data shortage. Every one traces back to the same absence: nothing holds the company as one structure.
Nothing holds the company as one structure. That is the thing to build.
Vyom builds a structural causal model of how one specific company operates. Not the org chart, not what the ERP records, but how it actually behaves. It runs continuously from that model: every cause and effect, every role, every dependency, updated as the company moves.
Six things run on that model at once, and the value is in all six together, not in any one. From the same live model, all at once, it surfaces what matters to each role before anyone asks, answers what they do ask, simulates a decision before it’s made, does real work through the systems already in place and inside regulated boundaries, remembers every lesson permanently, and gets sharper every cycle. These aren’t six products to weigh against each other. They’re one structure showing six faces, and taking any one away stops it being Vyom. Strip the language away and it still ingests events, traverses the model, propagates effects, simulates interventions, executes through audited tools, and remembers.
The structure is the product. The language layer only talks.
The obvious move is to bolt intelligence onto the stack a company already runs: read from the ERP, read from the BI tool, wire in the workflow engine, and call it smart. It isn’t, and the reason is structural, not a matter of which tool is better.
Each of those tools was built to see one slice. ERPs record state and process transactions; they can’t reason about why one thing causes another. BI shows what happened and leaves a human to connect the dots, by which point the moment to act has passed. Workflow automation runs rules you configured by hand and breaks the moment conditions change. AI-search and knowledge tools (Glean, Guru) index what a company has written down, not how it behaves or why things break. Stack a reasoning layer on top of any of them and you don’t escape the slice. You inherit it. What you get is a more capable version of the same fragmented picture.
The structure that holds the whole company can’t be assembled on top of tools that each hold a part. It has to be built as its own layer, underneath everything, reading from those systems and acting through them but not depending on any one of them to be the model. That underneath layer is what Vyom is.
None of this was buildable a few years ago. Three changes landed together, and Vyom needs all three.
These aren’t six separate positions. They’re one conviction seen from six angles, and each has cost us something in how the system is built, and each keeps costing as the product grows.
Read them together and they say one thing: build the structure first, let it learn, make it explain itself, and let it stand underneath many systems at once.
Vyom is pre-seed and pre-revenue. No live customer deployments today. Solo-founder-led, started in late 2025, working with a small number of design partners to check the model against how their operations actually run.
What is built: the structural causal model and the math over it, causal discovery, intervention simulation, action propagation, institutional memory, continuous learning, and the agent workforce, all tested against realistic synthetic data at scale. We generated that data, so we know the correct answers and can prove what works.
What is genuinely outstanding is narrow and specific: the large-GPU spend to pretrain the foundation model from scratch, and the live hookups into a partner’s real systems. Everything up to those two lines is built and provable. Companies compounding each other’s learning is how the system is built to work over time. It is the architecture, not something live across customers today.
Vyom is built by Vyapti, the venture Aditya Sinha started solo in late 2025. The thesis didn’t come from a whiteboard. It came from roughly eight years of running operations inside a family water-treatment and manufacturing business, watching the same failure repeat: the company ran on fragments, and no system held how the whole thing actually worked. That pattern held up in conversations with operators across manufacturing, construction, oil and gas, and retail. Those talks turned a lived frustration into the conviction that the gap is architectural and worth building the layer for.
Aditya is non-technical by trade and focused on what he can judge directly: testing what the system produces, evaluating how it behaves, and developing sharp intuition for where it’s right and where it isn’t. He is recruiting a technical cofounder now.
Vyom builds the full model on your operations and runs the whole platform from it, free: the surfacing, the simulation, the execution, the memory, and the agent workforce. We’re asking for data access and three to four months to validate that it reflects how you actually run. You get a working system you keep, and a voice in how Vyom develops for your industry. Not a demo, not a hypothetical.
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