Project Case Studies

Explore real systems built for operations, knowledge retrieval, and software development pipelines. We focus on correctness, auditability, and safety.

Case Study

Enterprise RAG Knowledge Copilot (Backend Architecture)

Production-grade retrieval-augmented generation pipeline with hybrid search, strict source grounding, and enterprise guardrails.

For engineering and operations teams struggling with documentation sprawl across SharePoint, Confluence, and internal repositories, we engineer production-ready RAG systems. This solution implements a robust FastAPI and PostgreSQL/pgvector backend featuring Reciprocal Rank Fusion (RRF), semantic query routing, JWT/RBAC security, prompt-injection guardrails, and an automated LLM-judge evaluation workflow via LangSmith to enforce citation-backed source grounding.

Technical Profile

  • Client Environment: Engineering and operations teams with documentation across SharePoint, Confluence, and internal repositories.
  • Business Problem: Staff lost an average of 45 minutes finding accurate procedures during outages due to documentation sprawl.
  • Constraints & Governance: JWT/RBAC access control, prompt-injection guardrails, and citation-first answers with 100% citation coverage.
  • Solution Architecture: FastAPI retrieval service with hybrid vector + keyword search, Reciprocal Rank Fusion (RRF), and semantic query routing.
  • Quantified Outcome: 75% reduction in internal lookup times with verified inline citations.
  • Technical Stack: FastAPI, PostgreSQL/pgvector, LangChain, LangSmith (LLM-judge eval), Python, JWT/RBAC.

Technical Profile

  • Client Environment: Software engineering department with active LLM developer tooling and repository flows.
  • Business Problem: AI assistants lacked secure, standardized access to repository logs, commits, and deployment metadata.
  • Constraints & Governance: Strict credential containment and fine-grained tool boundaries.
  • Solution Architecture: Custom Node.js Model Context Protocol server exposing secure stats, workflow tools, and inspections.
  • Quantified Outcome: Standardized 20+ repository tools, reducing engineering setup and custom bot integration times.
  • Technical Stack: Node.js, TypeScript, GitHub API, Model Context Protocol (MCP).
Case Study

GitHub Engineering MCP Server

Standardizing tool interfaces for AI agents in repository automation.

To enable LLM agents to act autonomously on developer tasks, they need reliable tool boundaries. We designed and built a custom MCP server that allows AI agents to securely query workflow logs, inspect commits, and analyze open pull requests.

Case Study

Custom Engineering Workflow Automation

Custom PR classification and review assistance for developer workflows—not packaged product software.

Complex development workflows suffered from context loss, high latency, and repetitive manual review across distributed codebases. We engineered a custom review-assist pipeline with graph-based task routing and human-in-the-loop approval gates. Senior engineers stay in control of the execution loop, with clean source code and native Azure/GitHub integration.

Technical Profile

  • Client Environment: Mid-market software development organization with distributed codebases and high review volume.
  • Business Problem: Context loss, high latency, and repetitive manual review across complex development workflows.
  • Constraints & Governance: Human-in-the-loop validation; senior engineers remain in the execution loop.
  • Solution Architecture: Custom review-assist pipeline with PR classification and human-in-the-loop approval gates.
  • Outcome: Faster first-pass review cycles with senior engineers remaining in the approval loop.
  • Technical Stack: LangGraph, Python, FastAPI, Docker.

Technical Profile

  • Client Environment: Enterprise development team with intensive continuous integration workflows.
  • Business Problem: Code review latency causing deployment bottlenecks and delaying merges by up to 48 hours for basic style checks.
  • Constraints & Governance: Catch architectural violations and hardcoded credentials securely before code merges.
  • Solution Architecture: Serverless integration hooks triggered on PR creation, routing code diffs to LLM review pipelines.
  • Quantified Outcome: Reduced average PR turnaround time by 40% while identifying security flaws pre-deployment.
  • Technical Stack: Claude API, Azure DevOps Services, Node.js, serverless functions.
Case Study

Azure DevOps + Claude PR Reviewer

Automated feedback cycles directly in the pull request interface.

Senior engineers spend too much time reviewing basic linting errors, file paths, and simple security anti-patterns. We integrated Claude reviews directly into Azure DevOps PRs. The bot reviews the diff within 2 minutes of creation, highlighting concerns inline.

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