Banking — Audit-Ready Agentic Research on Approved Data

Client profile: Global bank (markets, research, compliance)

Situation

The bank wanted to scale RAG + agent workflows, but security/compliance concerns and unpredictable spend kept projects stuck in “lab mode".

What MHN implemented (public-safe)

• Data governance foundations: ownership, entitlements alignment, approved-source boundaries

• Governed RAG: only approved knowledge sources, provenance-first outputs

• Trusted agent runtime: controlled tool access, audit trails, safety controls

• CacheGuard: spend guardrails + cost telemetry to stabilize AI unit economics

• One-Click SoT (GitHub → AWS): secure, reproducible deployment with enterprise hardening + observability

• Evidence pack: evaluation results + audit-ready artifacts for internal sign-off

  
  

Outcomes (typical)

• Faster path from pilot to approved usage

• Lower risk of leakage / unsafe actions through controlled access and traceability

• AI spend becomes predictable and defensible (stable cost-per-workflow)

• Increased trust from risk/compliance due to decision-grade evidence

  
  

CTA: Request a sample “Evidence Pack” (sanitized) or run a governed workflow pilot.

Note: These examples are anonymized composites and may combine elements from multiple engagements to protect confidentiality.