How your company runs inside Vyom

One model of how your company actually works.

Every driver, every effect, the time each effect takes to land, held as one living causal model. Everything below is that model, turned to face a different way. Don't just read it. Run it.

What's real today
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synthetic companies
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generated model
0 lines
industry-specific runtime
do( · )
Python ↔ TS, locked in CI
01 The model

The structure does the thinking.The language model only talks.

At the center is a real graph of your company. Every node is concrete. Every edge is a measured cause and effect with a direction, a strength, a confidence, and a lag.

The reasoning happens there, in math. The hard part isn’t any single method. It’s the engine we built around them, which runs several at once, reconciles them where they disagree, and weighs what they find against what actually happened the last time Vyom acted. That is what holds up on messy, real-world data, where one method on its own falls apart. Pull the language model out and Vyom still runs. It only goes quiet.

Live causal instrument · pull a lever on any node
T + 0h
Fire an event 100%
Fire an event, or click any node to run do() on it. The effect travels the real wiring, edge by edge, each at its own lag.
Click any node to run do() on it: hold it fixed and cut its inputs, the way a real intervention does. Hover any node or edge to read its weight, confidence, and lag.

Six things run on this one model at once.

Not six products. Six faces of one structure. Click a face.

How the living model is built, and kept true
IngestConnect to every system you already run, and to the outside world: the weather over your sites, commodity prices, regulatory filings, macro rates. Pull each one in the moment it changes.
CleanMeet each source on its own terms and turn it into a dated, sourced observation, attached to the exact part of the business it touches. Never a raw dump into a prompt.
Watch & discoverWatch continuously, and learn the causal structure from what actually moves together, and the lag between a cause and its effect.
GroundDon't just guess. Pull a lever on the model's own twin, watch the effect propagate, and turn a guessed edge into an earned one. That is the step that makes the structure real, not a correlation. Every new company is grounded from day one.
Run, without stoppingOn that live, grounded model it surfaces what matters, simulates a decision before it's taken, acts, remembers, and sharpens. Everything below is that one model at work.
02 Run a decision

See the consequence before you take it.

Vyom fixes the change you’re weighing as a real input and propagates it forward through your structure, in lag order, until it plays out. Toggle the branches. Watch the numbers move.

do( · ) · Riverside pour · storm in 72h
Leave the two slab pours in the storm window. Rain on wet concrete forces a redo.
Schedule slip~4 days
Draw payment~9d late
Crew idle1.5d
Recommendation · Schedule Sentinelchain conf 0.71–0.88
What
Move Thursday's Riverside pour to Saturday's dry window.
Why
rainfall_mm → pour_window_viability → schedule_adherence → cash_flow_position
If not
Leave it alone: schedule slips, the draw is late, the crew sits idle.
How
Re-sequence, notify crew + pump vendor, confirm Saturday inspection.

When you decide, that same forward pass becomes real, and it propagates: the person hit first hears first, and everyone affected is told what it means for the outcomes they own. Not a mass email.

03 It acts

A recommendation that can't act is only advice.

Execution runs across three surfaces, in this order of trust. Open each.

It writes back to the tools you already run and confirms the write actually landed, so a change goes through rather than just being sent. Scoped credentials, health checks, clear status when a source is degraded.
ProcoreQuickBooksSlackGitHubGmail
Work that lives in Vyom executes directly: a task, a coordination, a component someone generated on the spot.
For anything financial, clinical, or safety-critical, Vyom refuses to act without three things: a named human sign-off, the complete causal justification, and tamper-evident lineage. Miss one and the action is blocked, and the block is recorded.
04 It remembers, and sharpens

The reasoning is kept.The model tightens.

The most expensive thing a company loses is why. Vyom keeps it on the same graph the decision was reasoned on. Ask it.

Grand Ave, Q2: re-sequenced the pour, held the milestone. Outcome confirmed. Not since replaced.
Riverside, last year: tented and poured. Later replaced by the re-sequence approach after a cure-strength issue.
Within your company · always on
Every prediction is scored against what happened, including the prevention case: when Vyom warns you, you don't act, and the bad thing happens, that confirms the agent was right. Weights tighten, agents earn standing, the model tracks your business.
weather → pour · 0.88 → 0.90
Across companies · privately
Conjugate posterior pooling with a Gaussian differential-privacy mechanism, verified on synthetic multi-company cohorts. Each company shares only the statistics of its learned relationships, never its data. A new company never starts from zero.
data left the site: none
05 The world model of the enterprise

Your company's model,built on a model of how every company works.

Your deployment builds a living causal model of your specific company. Underneath every deployment sits something larger: a world model of how enterprises actually operate, learned from the broad public record of how companies run, the numbers and the text that explains them. Because it has learned how operations respond to action, it can answer what happens if you act, not just what comes next, and it already tells a healthy process from a failing one before it sees your first record.

How it learnsfrom the world's public operational record, the numbers and the text that explains them, never anyone's private data.
What it doesit is action-conditioned: it answers what happens if we do this, an intervention, not just a forecast.
Why it mattersa new company starts with a working model on day one, in any industry, and its own data sharpens it from there.
Constructionrain → pour slip → schedule collision → cost overrun → cash timing
Utilityheatwave → HVAC demand → grid load → capacity strain → price spike
Pharmaraw-material impurity → lower batch yield → the reject-vs-risk call

It reasons down the real wiring, not a chart. The same structure carries from a job site to a power grid to a plant floor.

It gets sharper with every company, privately.

Each deployment makes the shared model better for all the others, with no company's data ever leaving its own walls. A signature learned at one operation protects the next, and a company that joins later starts with what earlier ones already taught it. Prevention is the floor. A company that compounds its own intelligence is the ceiling.

Where this stands, plainly

The causal engine that reasons over your company and answers do(·) runs today, and everything the product does, it does on that engine. The full from-scratch world model is the frontier we're building toward. The architecture, the training approach, and the way we score it against known ground truth all exist. What remains is the compute and the research time to train it at scale. We won't pretend it's finished.

0 lines
of industry-specific runtime. The engine is one config-driven whole, proven across every synthetic company
06 Built and proven

Real engineering, scored against ground truth.

Because the data is synthetic today, we do what no real deployment can: generate companies whose true cause-and-effect structure we already know, then score Vyom against that truth.

0
synthetic companies, one generated model
CI + placebo
every edge bootstrapped and permutation-tested before it becomes a weight
(ε,δ)-DP
federation, released above a min cohort
FDR
several methods reconciled, scored vs planted edges
do-operatorPython ↔ TypeScript byte-identical, conformance-locked in CI. The math can't drift into the language layer.
discoverySeveral discovery methods run together and are reconciled where they disagree, FDR-controlled, scored against planted edges. Recovers real structure, not correlations.
federation(ε,δ)-DP posterior pooling. No company's contribution can be reverse-engineered.

Genuinely outstanding: exactly two things

Large-GPU spend to pretrain a company-scale model beyond today's causal engine, and live external hookups to real partners' systems. Everything before those two lines is built and proven against ground truth. Vyom is pre-seed and pre-revenue, with zero live customer deployments, looking for design partners to be the first.

06 Running it in your company

Where it runs, who sees your data, what you sign up for.

The plain terms, before anything else. No surprises later.

Where it runs
Self-hosted, in an environment you control
Data isolation
Isolated per tenant today: model, events, and memory
Your raw data
Never leaves your systems, by construction
Audit
Every action hash-chained; tampering shows
SOC 2 / ISO / HIPAA
None yet. Pre-seed, on the roadmap
Pricing
Deferred while we prove it together

Come build the model of your company.

If you run an operations-heavy business and want to be among the first companies Vyom models, we should talk.

Request access