Agentic AnalyticsStart with one decision
Act 1 · The promise

Dashboards wait for questions.
Your business doesn’t.

Understand your business beyond the dashboard. See what changed, where it happened, what contributed, what may explain it, how strong the evidence is — and what deserves a decision.

See more

Connect internal performance with sourced competitor, market, and open-web evidence.

Go deeper

Let recurring agents systematically inspect governed dimensions, cohorts, time windows, and competing explanations.

Decide faster

Semantic models start every investigation from shared definitions, valid cuts, and known limits — not fresh guesswork.

Bring us one decision See one investigation ↓
React Flow

Select any node — each carries one sentence of evidence.

  • Input · internal — Company performance. Revenue, margin, and operations from one reconciling operating history.
  • Input · internal — Customer behaviour. Cohorts, channels, and baskets from governed company data.
  • Input · external · sourced · dated — Competitors + market. Open-web and market evidence — kept visibly separate from internal facts.
  • System — Governed investigation. Recurring agents work through shared definitions and safe execution — not fresh guesswork.
  • Output — Executive understanding. What changed, where, what contributed, what may explain it, how strong the evidence is.
  • Outcome — Decision. Leadership acts on supported findings; hypotheses stay labelled. Evidence and confidence feed the next investigation.
Act 2 · Requirements

Better understanding does not come from a better prompt.

It takes six connected capabilities around one goal: trusted business understanding. One system — not six sections. Remove one capability and the answer becomes incomplete or unsafe.

React Flow

Select a capability — each reveals its meaning and one concrete failure when it is absent.

  • The goal — Trusted business understanding. Exists only when all six capabilities connect — remove one and the answer becomes incomplete or unsafe.
  • Requirement 1 — Reliable company data. One reconciling operating history. If absent: two teams compute two different revenues.
  • Requirement 2 — Shared business meaning. Definitions, purpose, valid uses, thresholds, caveats, accumulated knowledge. If absent: “revenue” means three things.
  • Requirement 3 — Continuous investigation. Examined at the cadence the decision deserves, daily through yearly. If absent: the change surfaces six weeks late.
  • Requirement 4 — Controlled execution. Permissions, isolation, reproducibility, observability, cost. If absent: nobody can reproduce the reported number.
  • Requirement 5 — Access where work happens. Conversation, monitors, workspaces, reports, alerts, audit views. If absent: answers never leave one chat window.
  • Requirement 6 — Evals and evidence. Representative questions test metric choice, grain, ambiguity, provenance, causal restraint. If absent: the wrong grain passes silently.
Illustrative refusal — governance you can seerequest: gross_profit by not_a_dimension → the semantic contract would stop this before SQL. No invented answer.

One system, changed roles

Leaders ask and decideAnalysts investigate and explainData scientists frame and evaluateData engineers automate reliability and contracts
Act 3 · Illustration

From “revenue increased” to an explanation leadership can use.

The system separates observation, mathematical contribution, plausible explanation, evidence strength, and decision — instead of collapsing them into one confident paragraph.

React Flow
  • Signal · internal — Revenue increased. A monitored movement, from governed company data.
  • Where? · Measured cut — Online sales + new product segment. The movement concentrated where it matters.
  • Contribution? · Measured contribution — Decomposition of the increase. Those segments explain most of the measured increase.
  • Plausible external drivers? · Hypothetical context — Market conditions + competitor activity. Candidate explanations — source and date them before using them in a decision.
  • How confident? · Hypothesis — not cause — Supported / uncertain / unknown. Co-movement is not proof; the explanation stays a hypothesis unless stronger evidence exists.
  • What next? — Investigate, test, or act. The system proposes what a human should investigate or decide next.

Thesis illustration · August 14, 2026 — a generic worked example, not client data. Internal facts and external context stay visibly distinct. External facts require a source and date before they support a decision; candidate drivers remain hypotheses — not causes.

Current executable proof: the Okurka assortment case carries the governed request, engine-compiled SQL, rows, refusal, and handover evidence. (A fictional reference engagement; the broader growth-quality story remains the target.)

Act 4 · Engagement

Start with one decision. Build the understanding behind it.

React Flow
  • Step 01 — Choose the decision. Who needs the answer, what changes because of it, and what evidence would be sufficient.
  • Step 02 — Make meaning explicit. Define the metrics, context, valid cuts, caveats, and decision rules.
  • Step 03 — Build and connect the path. Wire the data, semantic model, agent access, recurring investigation, and evidence surfaces.
  • Step 04 — Prove and hand over. Run real questions, refusals, provenance checks, and evals; deliver the contracts, code, evidence, and operating knowledge.
  • Handover — Client ownership. The client owns what is built. Agentic Analytics leaves the execution chain after delivery.

The client owns what is built. Agentic Analytics leaves the execution chain after delivery.

Which decision is still waiting for an answer?

Bring us one decision