We design and build GenAI systems for enterprises — hybrid RAG, autonomous agents, Text2SQL, and intelligent analytics.
Engineered for production. Built to scale.
We don't build AI demos — we build AI systems that run in production and deliver measurable results.
Every system is built for real-world operation — monitoring, fault tolerance, and scale from day one.
We understand the data, constraints, and systems before writing a line of code.
Extensive experience delivering mission-critical systems for complex organizations.
You work directly with the engineers who architect and build. No layers, no handoffs.
From intelligent systems design to production infrastructure
Production LLM applications — hybrid retrieval, context engineering, NLP, Text2SQL, knowledge graphs, and quality evaluation pipelines.
Multi-agent systems for complex business workflows — document processing, cross-system operations, and decision support with production-grade observability.
Helping R&D teams adopt agentic coding workflows — spec-driven development, context engineering, multi-agent coordination, and organizational AI standards.
Custom ML models — classification, anomaly detection, recommendations, predictive analytics, and computer vision. Design through production deployment.
Distributed systems, microservices, real-time sync, and API infrastructure — adding AI capabilities into existing enterprise applications without replacing what works.
AI workload infrastructure — cloud architecture, containerization, data pipelines, CI/CD, and cost optimization across OLTP, OLAP, NoSQL, columnar, vector, and graph databases.
Purpose-built AI platforms for enterprise data intelligence
Self-Service BI & Text2SQL Platform
Text2SQL that actually works on enterprise data. Scales to 1,000+ tables where every other solution breaks at 20+. Quality-first architecture with agentic orchestration and multi-path SQL generation, built on DeepKnowledge's hybrid RAG engine core.
Learn More →Enterprise RAG Platform
Intelligent retrieval over documents, policies, contracts, and organizational data. Triple-storage architecture — vector, knowledge graph, and full-text search — with agentic orchestration for precision that single-method RAG can't match.
Learn More →Free, MIT-licensed tools we build in the open — for engineers and their coding agents. Starting with DeepInit.
Grounded, verified code-truth for your agent
Reads your codebase and writes down grounded, verified truth — the real rules, the live database schema, the why — every claim tied to a file:line. A Claude Code plugin that also flags the real problems it finds, then keeps it all current as your code changes.
More open-source developer tools are on the way — including one that indexes your codebase and lets your agent query it. + more coming
We design and build production GenAI systems — RAG, agents, Text2SQL, intelligent analytics. End-to-end from architecture through deployment.
Helping R&D teams adopt agentic coding workflows — spec-driven development, context engineering, organizational AI standards, and reusable agent skills.
DeepQuery and DeepKnowledge deployment, customization, and integration into your enterprise environment.
Have a complex AI challenge that needs deep engineering expertise?
Tell us about your project and we'll get back to you.