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.

0
synthetic companies
0
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: traversal, propagation, do-calculus, Bayesian updates. Pull the language model out and Vyom still runs. It only goes quiet.

rainfall_mm  →(6h)  pour_window_viability  →(48h)  schedule_adherence  →(72h)  cash_flow_position

Six things run on this one model at once. Not six products. Click a face.

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 Sentinelconf 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
The do-nothing branch, left: schedule slip, draw late, crew 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, each 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 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.

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synthetic companies, one generated model
0 lines
of industry-specific runtime
(ε,δ)-DP
federation, released above a min cohort
FDR ✓
discovery ensemble, scored vs planted edges
do-operatorPython ↔ TypeScript byte-identical, conformance-locked in CI. The math can't drift into the language layer.
discoveryResampled ensemble, 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.

Where it runs
Self-hosted, in an environment you control
Data isolation
Model, events & memory per tenant today
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