THE TECHNOLOGY

An engine that never stops reasoning.

Built to reason continuously: a knowledge graph of your business, re-scored as records land.

SYSTEM VIEWSIMULATED
GOAL
Grow contribution margin without raising spend
ENGINE TRACE
  • ingestshopify · 30 days of orders resolved · margin joined per product
  • ingestmeta ads · spend by campaign joined to the orders behind it
  • graphentities resolved · products, channels, cohorts linked
  • reasoncandidate moves generated · twelve scored against the goal
  • reasoncut the two worst-margin products from paid · confidence 0.78
  • reasonraise the free-shipping threshold to the median basket · confidence 0.66
  • reasonshift prospecting budget into retention flows · confidence 0.55
  • workbrief drafted for the top move · queued for senior review
  • evidenceoutcome window set · prediction recorded before the work starts
STRUCTURED OUTPUT
TOP MOVE
Cut the two worst-margin products from paid
TARGET METRIC
Contribution margin
CONFIDENCE
0.78
NEXT
Senior review
THE ARCHITECTURE
01

Ingest

Every source, read-only, gated.

Revenue, analytics, ad accounts, and CRM through standard OAuth, plus market signal. The engine reads the whole business, not a sample, and the data stays in your tools.

02

Graph

The business as a knowledge graph.

Customers, products, channels, margin, and risk resolved into one structure, with every decision stored as an object: its situation, move, confidence, evidence, test, and outcome.

03

Reason

Deterministic where a rule exists.

Candidate moves are generated and scored against the goal. Where a judgment repeats, it is encoded as a rule, so the same records produce the same conclusion and every conclusion traces back to what produced it.

04

Work

Drafted by agents. Signed by people.

Briefs, campaigns, and analysis are produced at volume and queued for senior review. Nothing ships itself. When an outcome window closes, the result is recorded against the prediction.

HOW IT REASONS
A language model on your data compared with The Voltaire engine
A language model on your dataThe Voltaire engine
Where answers come fromA model predicting plausible textA graph of your records and explicit rules
Same question twiceTwo different answersThe same answer, with the trace behind it
When it is wrongNo way to find out whyThe rule or record that misled it is identifiable
What compoundsThe vendor's modelYour graph, your decision log, your accuracy record
When it reasonsWhen you askContinuously, as records land

Language models still do the reading and the drafting at volume. They sit under the review gate. They do not decide.

See it pointed at your business.