Solutions

Three outcomes, one platform

GeometriX exists to change three things inside a regulated enterprise: how fast AI gets validated, whether agents make it past risk review, and what you can show an auditor afterward. Each outcome below names the problem, what changes, and what a technical reviewer can verify today.

Outcome 1 · Model risk & validation

Shorten validation cycles without lowering the bar

The problem

Validation teams inherit models from every framework, rebuild challengers by hand, and assemble evidence manually. Cycle time stretches while findings stay shallow — and agentic systems are now arriving in inventory with no testing method at all.

What changes

Wrap any model and run the full battery through one API: eight test families, challenger comparison, sliced analysis. Every result carries the configuration that produced it, so reruns and effective challenge become procedure instead of heroics. The same discipline extends to agents through DOE campaigns.

What a reviewer can verify

The modeva TestSuite and model wrappers, installable today; DOE campaign machinery in knowlytix.harness.testing; regulated-domain benchmark suites in knowlytix.benchmark.

Model Assurance →  ·  Agent Assurance →
Outcome 2 · Agentic AI programs

Move agents from demo to defensible production

The problem

Agent pilots stall at risk review because nobody can prove how the agent behaves. LLM-as-Judge evaluations don’t convince a second-line reviewer, and governance added after development is a wrapper, not a control.

What changes

Contracts and gated tools are properties of the agent from the first prototype. Release is gated on evidence-grounded test campaigns with typed verdicts, and the same policies tested in the lab are enforced deterministically at runtime, with drift monitoring after go-live.

What a reviewer can verify

The Harness loop and its 33 registered tools (knowlytix.harness); graph-verified ground truth generation and scoring; the runtime policy pipeline in knowlytix.harness.governance.

Agent Studio →  ·  Governance →
Outcome 3 · Risk, compliance & audit

Walk into audit with evidence, not reconstruction

The problem

When the auditor asks why an AI system decided what it decided, the answer gets reconstructed weeks later from logs that were never designed to be evidence.

What changes

Evidence is generated as the system operates: tamper-evident audit records as queryable triples, integrity verification on retrieved facts, per-claim verification decisions, and policy bundles that are signed and attributable — including multi-signature approval.

What a reviewer can verify

Audit, signing and attestation modules ship in knowlytix.harness.governance (policy audit, bundle signing, multi-sign, co-attestation) — inspectable in the installed package.

Governance →

Where this lands first

Banking

Interpretable credit and risk models, validation aligned with MRM practice, and agents whose responses respect lending and disclosure rules — verified per claim.

Insurance

Transparent pricing and claims models; coverage and claims assistants governed by policy language and state timelines.

Healthcare

Reliability-tested clinical decision support and assistants checked deterministically against protocols and formularies.

GeometriX describes fit for regulated environments; it does not certify regulatory compliance.

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