We build and ship production AI systems, end to end.

Contracts live scattered across PDFs, Word files, spreadsheets, scanned images, and email threads. Legal and procurement teams lose most of their time reading, comparing, and drafting by hand — and every manual review carries compliance and renewal risk.

An agent-based CLM that pulls legacy and new contracts into one AI repository, extracts metadata and maps clauses across any format, and reviews each contract for compliance and risk against your playbook. Business users ask plain-language questions through an AI agent; parameterized drafting templates, obligation tracking, automated renewal alerts, and one-scan QR retrieval are built in — and any contract's authenticity can be verified in minutes.

Submission-ready document files run 100–200 pages, and a reviewer had to check every one by hand — index accuracy, page continuity, duplicates, blanks, and 100–200 signatures and stamps — before signing off. Slow work, and a single miss carried real compliance consequences.

A document-AI pipeline that ingests entire files and verifies them the way a manual reviewer would: OCR and handwriting recognition read printed, handwritten, and alphanumeric page numbers; computer vision detects signatures and stamps on every page; and content is cross-checked against the file's index. Every discrepancy is compiled into an automated report — with a human reviewer giving the final sign-off.

A large consumer audience needed instant, trustworthy answers to open-ended questions — with live information, cited sources, and multilingual support — at a scale and latency that static content and simple chatbots couldn't reach.

A production conversational-AI backend: an async FastAPI service streaming responses over SSE, LangChain and LangGraph ReAct agents with persisted conversation memory, and provider-agnostic multi-LLM support. Behind it, a distributed web-intelligence engine exposes search, crawling, and RAG tools over the Model Context Protocol — with semantic retrieval, reranking, and automatic source citation. The same multi-tenant foundation was extended to further clients and delivered into WhatsApp.

Investors, partners, and analysts needed answers from a large, structured company database — but every question meant waiting on someone to write SQL and build a report, so the data went underused.

A conversational agent that turns plain-language questions into governed SQL against the live database, adapts its answers to each user's role, and remembers context across a multi-turn conversation. Non-technical users sort, count, group, and explore the data themselves, with guided prompts to get started and saved, searchable history.