Why Claude for regulated use cases?

Claude excels in constitutional AI alignment and low hallucination risk, supporting compliance narratives in regulated workflows.

How do you evaluate prompt reliability?

We build scored scenario suites (edge cases, adversarial phrasing, data redaction) and track drift via automated regression runs.

What governance artifacts are produced?

Model card, risk register, escalation playbook, red team findings summary, and retention/data handling matrix.

Claude AI Implementation & Governance

We operationalize Claude across internal knowledge retrieval, secure assistant workflows, and structured decision support layers with measurable reliability and alignment KPIs. Our approach emphasizes evaluation-first deployment: every user-facing interaction pattern gets a regression harness before broad exposure.

Reference Architecture

Retrieval Layer: hybrid semantic + keyword guard rails
Safety Layer: PII scrubbing, prompt injection filters, output toxicity scan
Evaluation Harness: scenario matrix + golden answer variance thresholds
Escalation Path: unresolved confidence triggers handoff with structured context packet

Prompt Operations

We treat prompts as versioned, testable assets. Techniques include chained constitutional reframing, reasoning trace compression, and tool-call orchestration. Drift detection surfaces semantic regression when organizational lexicon evolves.

Risk Controls

Controls mapped to NIST AI RMF & internal policy: data minimization, trace logging with immutable retention windows, role-scoped capability boundaries, and hallucination exception triage.

Adoption Metrics

Scenario Pass Rate & Drift Index
Escalation Avoidance % (deflection)
Latency vs Confidence Curve
Prompt Pattern Reuse Density
Knowledge Freshness Lag