The evidence is already yours. Reading it at scale isn't.
Organisations are sitting on the evidence they need — interview transcripts, process documents, incident post-mortems, audit findings, case files, clinical records — but reading and synthesising it at scale is impractical for humans, and traditional consultant-led analysis is slow, expensive, subjective, and hard to refresh. Enkidu builds AI-augmented data science systems that perform that synthesis faithfully, transparently, and repeatably — and that can answer, for any number they produce, the question every executive, auditor, and regulator eventually asks: "Where did this come from, and why should I believe it?"
Our systems are decision-support, not decision-makers. A human reviews every released result. What we remove is the drudgery and the subjectivity — not the accountability.
- Multi-agent analytical pipelines
- Code-enforced scoring — models never do arithmetic
- Evidence-grounded, citation-enforced outputs
- Classical data science, held to the same standard
- Sovereign, on-premise deployment by design
Not maximal trust.
Calibrated trust.
Every engagement is structured by Tangram — each analytical stage a bounded zone with explicit entry and exit conditions, contractual handoffs, and error paths designed in advance — and governed by Origi, our trust assurance harness, which verifies every step at inference time: input sanitisation and injection screening on the way in, least-privilege tool access per agent in flight, and schema validation, citation enforcement, groundedness checks, and PII screening before anything reaches a report.
The result is reliance matched to demonstrated trustworthiness, backed by a verifiable dossier.
