Explore real systems built for operations, knowledge retrieval, and software development pipelines. We focus on correctness, auditability, and safety.
Eliminating documentation sprawl with strict source-grounded retrieval.
In mid-market organizations, critical operational information is often scattered across SharePoint, Confluence, and network drives. We built an internal knowledge copilot that indexes unstructured documentation and enforces strict source citations, preventing model hallucinations.
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.
Routing developer workflows across specialized AI agents.
Single-prompt LLMs fail on complex, multi-stage engineering tasks. We built an orchestrator that coordinates specialized, stateful agent routines (reviewing code, triaging bug reports, writing release notes) using LangGraph and state machine routing.
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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