Epistemic Audit¶
Services → diagnosis
An Epistemic Audit tells you what will go wrong before it does.
The core question is simple: Are you structurally ready to deploy agentic AI without unacceptable hallucination risk? We answer it with evidence, not optimism.
What we assess
Data reality
PDFs, SQL, spreadsheets, KBs, tickets, wikis — and the mismatch between them.
Failure modes
Fabrications, drift, inconsistent answers, policy edge cases, silent uncertainty.
Ontology gaps
Missing concepts and relations that cause retrieval to return “relevant” but unusable evidence.
Governance requirements
Audit obligations, traceability, constraint needs, approval workflows, and abstention rules.
Diagram: what an audit maps
flowchart TB;
Q["Target decisions"] --> D["Data sources"];
D --> R["Retrieval behavior"];
R --> F["Failure modes"];
F --> G["Governance constraints"];
G --> P["Prioritized roadmap"];Deliverables (decision-grade, not slide-grade)
Readiness report
A candid assessment of reliability, risk, and what must change before production.
Prioritized risks
Top failure modes with severity, likelihood, and concrete mitigations.
Quick wins
Low-effort fixes that reduce hallucinations fast (schema, provenance, constraints, evaluation).
Roadmap
Staged plan with measurable milestones and explicit “go/no-go” gates.
Diagram: from audit to blueprint
flowchart LR;
A["Audit findings"] --> O["Ontology + constraints scope"];
O --> B["Architecture blueprint"];
B --> I["Implementation"];Best fit
- Hallucination is unacceptable (legal, medical, financial, safety-critical).
- Audits or compliance matter.
- Your data is messy and multi-source.
- You need a plan that survives model churn.