The Autonomous Data Office
Your data team answers in weeks. Ours answers in minutes.
A fully governed AI system that takes business questions in and returns verified answers — fast enough for real-time agent queries, rigorous enough for a board pack.
What it is
A data science department, running at machine speed.
A question — from a human or another agent
A senior executive, a frontline system, or an AI agent querying via API. Intent understood, not just syntax.
Analysis — governed at every step
Queries your data, runs the analysis, subjects every output to adversarial review before it leaves the system.
A verified answer — ready to act on
Structured data for downstream agents. Narrative for human decision-makers. Confidence bounds and audit trail attached.
A system that compounds over time
After every execution, metadata and lineage feed back into the Semantic Layer. The more you use it, the better it gets.
Agent-to-Agent
Every enterprise AI agent has a data dependency.
Sales, finance, operations, customer service — every agent hits the same wall: it needs verified data in milliseconds. The ADO is designed to be that data layer.
“Return CLV decile and 90-day churn probability for this customer.”
Validated response within minutes. Agent triggers retention offer. No human analyst involved.
“Win-rate by price band for enterprise deals, last 18 months.”
Structured recommendation with confidence bounds, pulled from CRM and finance. Embedded directly into the proposal.
“Identify primary cost drivers behind the £2.3M opex variance for October.”
ADO queries the finance warehouse, decomposes the variance, delivers the narrative into the CFO report.
The Unified Semantic Layer
Most hallucinations aren't a model problem.
Models fail because the data underneath is fragmented, inconsistent, semantically ambiguous. We resolve the ambiguity before the model sees a thing.
Schema fragmentation
The same concept defined differently across five systems. Resolved into one governed definition.
Missing business logic
Warehouses store numbers. They don't store the rules about what those numbers mean. We encode the rules.
Semantic drift
Definitions that change over time without being tracked. Versioned, auditable semantic lineage.
Hallucination risk
Models fill gaps in ambiguous data with plausible-sounding fiction. Remove the ambiguity before the model sees the data.
Governance
We don't just produce answers. We prove they're right.
Every output is subjected to adversarial review before it leaves the system. Not a confidence score — a structured challenge to the analysis. One hallucinated output in a board pack ends the AI programme. We treat that as an architectural constraint.
ADO output
Analysis complete. SQL verified, model run, narrative drafted.
Mathematical Critic
Adversarial audit. Every assumption challenged. Every number cross-checked. Edge cases stress-tested.
Validated output
Delivered with a full audit trail. Every conclusion traceable to the data and logic that produced it.
PII Shield
Every data payload is scanned and stripped of PII before it reaches an LLM. Zero-trust from intake to output.
Tenant isolation
Each client operates in a siloed environment. Your data, your schema, your execution logic — never visible to another tenant. UK data sovereignty supported.
Human-on-the-loop
When the system hits maximum retries without confidence, it escalates — full session, code, error logs — to a human operator. Graceful failure by design.
FCA & GDPR audit trails
Every agent decision is cryptographically linked to the Mathematical Critic's audit. An append-only trail from question to answer.