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AI Assurance

Safety and trust as a product surface.

Codesurance treats assurance as core engineering, not a checklist. Every system we ship comes with the receipts — defensible to regulators, customers, and your own teams.

Pillars

What goes into an assured system.

Safety policies

Layered guardrails, content controls, red-team baselines, and per-tenant overrides.

Auditability

Full trace of prompts, tools, decisions, and human overrides — queryable and exportable.

Continuous evals

Production telemetry tied to scoring rubrics with regression gates wired into CI.

Data sovereignty

Tenant isolation, regional residency, zero-retention modes, and customer-managed keys.

Drift & degradation

Bias, drift, and quality monitors with alerting and rollback playbooks.

Sector frameworks

Pre-mapped to HIPAA, SOC 2, EU AI Act, FCA, NHS DSPT, and similar regimes.

Engagement

How assurance enters a project.

01

Risk mapping

Per use case, against sector-specific frameworks and your internal posture.

02

Engineering primitives

Eval harness, audit log, safety layer, and rollout controls baked into the build.

03

Operating cadence

Regular review, drift checks, and a documented response playbook for incidents.

Ready to move from AI experimentation to execution?

Start with a structured discovery sprint tailored to your industry, operating model, and growth priorities.

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