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Home/All Posts/Case Studies/From Zero to Production AI in 6 Weeks
#enterprise#financial-services#case-study

From Zero to Production AI in 6 Weeks

A financial services firm needed a production-grade AI deployment that met strict data sovereignty requirements. We delivered in 6 weeks, on-premise, with full audit compliance.

AI Mate

June 19, 2026

At a glance

Industry: Financial Services | Challenge: Data sovereignty + speed | Result: Production in 6 weeks, fully on-premise

The Challenge

A mid-size financial services firm wanted to use AI to automate their client communications and regulatory reporting. The constraint: all data had to remain on-premise. No third-party cloud services could process client data. And they needed it live before the end of the quarter.

Why Standard Solutions Didn't Fit

Every major AI SaaS platform the team evaluated required data to leave their infrastructure. Even with enterprise data processing agreements, the compliance team couldn't approve it. They needed a fundamentally different approach.

Our Approach

  1. Week 1: Infrastructure — Self-hosted LLM (Ollama + Llama 3) deployed on VM, MCP server configured, Bearer token auth wired up
  2. Week 2: Content pipeline — AI-native CMS deployed, first client communication templates created and tested
  3. Week 3: Regulatory reporting — MCP tools built for pulling structured data and generating compliant report drafts
  4. Week 4: Review workflow — Human-in-the-loop approval layer added; AI drafts, compliance officer reviews, system publishes
  5. Weeks 5–6: Testing and go-live — Parallel running with existing process, issues resolved, full cutover in week 6

The Results

Six weeks after kick-off, the system was live in production. Client communications that previously took 2-3 business days to produce were now drafted and queued within minutes. Regulatory reports that required a full day of manual data gathering were generated automatically overnight, ready for compliance officer review each morning.

The compliance win

The team initially feared that AI-generated content would face more scrutiny from compliance. The opposite happened: because every output was structured, traceable, and logged, it was easier to audit than the previous manual process.
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