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Enterprise AI and DATA SYSTEMS //
Latest from Cases Studies
SaaS — Enterprise-Grade AI Features Buyers Can Trust
Client profile: B2B SaaS selling to regulated / enterprise customers Situation The product team needed AI features that scale without cross-tenant risk, regressions, or margin collapse from inference costs. What MHN implemented (public-safe) Identity + entitlement model: tenant-safe access boundaries Data mapping + quality checks: consistent schema/definitions feeding AI workflows Governed RAG: provenance, allowed knowledge domains per tenant/customer Trusted agents: con
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
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