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CLAUDE AI  ·  ENTERPRISE IMPLEMENTATION

Claude in production, governed from day one.

We operationalize Claude across knowledge retrieval, secure assistant workflows, and structured decision support. Evaluation-first deployment means every user-facing pattern has a regression harness before broad exposure.

CLAUDE.ENGAGEMENT.SPECACTIVE
Model
Claude 3.x (Opus, Sonnet, Haiku)
Governance
NIST AI RMF · audit logging · role-scoped access
Compliance
FedRAMP aligned · CMMC L2 aligned · SOC 2 aligned
Evaluation
Scenario matrix · golden answer variance · drift detection
Time to prod
First system in <30 days from contract execution
Artifacts
Model card · risk register · escalation playbook
CAPABILITIES

Six architecture layers. One governance standard.

Each layer is designed, tested, and handed off as an independently verifiable component. No black-box integrations. No ungoverned prompt paths.

01

Retrieval Layer

Hybrid semantic and keyword retrieval with guardrails. Every response traces back to a source document before user exposure.

02

Safety Layer

PII scrubbing, prompt injection filters, and output toxicity scanning active in every pipeline before responses reach users.

03

Evaluation Harness

Scenario matrix with golden answer variance thresholds. Drift detection surfaces semantic regression when organizational lexicon evolves.

04

Prompt Operations

Prompts treated as versioned, testable assets. Chained constitutional reframing, reasoning trace compression, and tool-call orchestration.

05

Escalation Path

Unresolved confidence triggers structured handoff with a context packet to the responsible human. No silent failures.

06

Risk Controls

Mapped to NIST AI RMF: data minimization, immutable trace logging, role-scoped capability boundaries, and hallucination exception triage.

HOW WE ENGAGE

Four phases. Written exit criteria at each gate.

01

Assessment

Audit current workflows for Claude fit. Score use cases by risk, value, and compliance surface before selecting the pilot.

02

Architecture

Design retrieval, safety, and escalation layers. Build the evaluation harness before any user-facing pattern goes live.

03

Deployment

Phased rollout with governance controls active. Written acceptance criteria verified before each promotion to production.

04

Operate

Observability dashboards, quarterly drift reviews, and a documented handoff so your team owns the system long-term.

ADOPTION METRICS

What we measure, by default.

DELIVERY RATE
100%
Zero failed AI engagements across 25 years
TO PRODUCTION
<30d
First Claude system live from contract
AI RMF MAPPED
NIST
Risk controls aligned to the framework
UNGOVERNED PATHS
0
Every prompt pattern has a harness
NEXT STEP

Ready to deploy Claude with governance built in?