An operations-heavy B2B company

We engineered an agentic AI layer — a custom MCP server and a locked-down RAG pipeline — that automates knowledge-heavy internal workflows without hallucinating or leaking data.

AI InfrastructureRAGMCPAutomationAI Infrastructure
agent.firstsoft.dev
→ tool: reports.query · scope: read authorized
Q  What was Q3 churn for enterprise?

Enterprise churn was 2.1% in Q3, down from 3.4%.

cited: q3_metrics.csv grounded
model-agnostic · tracedhallucination evals: passing

The problem

The team wanted to automate knowledge-heavy workflows but couldn't trust off-the-shelf AI tools that hallucinated, had no access controls over sensitive data, and gave no audit trail for the actions they took.

Our approach

  1. 01

    We built a custom Model Context Protocol server so internal tools and data sources became safe, governed actions for the agent.

  2. 02

    We grounded every response in an enterprise RAG pipeline scoped to approved, permissioned documents only, returning citations so answers stayed traceable.

  3. 03

    We instrumented each agent run with evaluation and audit logging, expanding autonomy only where the evals proved it was safe.

The outcome

An AI layer the team actually trusts — automating routine work while keeping every answer grounded, permissioned, and fully auditable.

Impact (figures pending confirmation)

Manual work removed
40 hrs/week [confirm]
Answer grounding accuracy
98% [confirm]
Sensitive-data leaks
0 [confirm]

Build something like this

One senior team, end to end. Tell us what you're building and we'll architect the path to ship it.