AI Engineering
We build the AI-native software your roadmap actually needs — LLM applications, autonomous agents, RAG systems, and the Model Context Protocol (MCP) servers that connect them to your tools.
This is not “we’ll integrate ChatGPT for you.” It’s production AI engineering: evaluated, observable, cost-controlled, and built to survive the next model swap.
What we build
LLM applications
- Internal copilots (sales, support, ops, engineering)
- Customer-facing assistants with retrieval and tool use
- Document understanding & extraction pipelines
- Voice and multimodal interfaces
Agentic systems
- Single-agent and multi-agent workflows
- Long-running task agents with state and recovery
- Tool-calling agents on top of your existing APIs
- Human-in-the-loop approval flows
RAG & knowledge systems
- Embedding pipelines and ingestion ETL
- Vector database design (pgvector, Pinecone, Weaviate, Qdrant)
- Hybrid retrieval (BM25 + dense + reranking)
- Evaluation harnesses for retrieval quality
MCP servers (Model Context Protocol)
- Custom MCP servers exposing your internal systems to Claude, Cursor, and other AI clients
- MCP-based internal tooling for engineering, ops, and data teams
- Authentication, scoping, and audit for MCP at scale
Production glue
- Eval pipelines (deterministic + LLM-as-judge)
- Prompt and model versioning
- Cost & token observability
- Guardrails, PII redaction, and prompt-injection defenses
How we work
- Discovery — understand the actual problem, not the AI hype around it
- Design — pick the right pattern (RAG, agent, fine-tune, or boring software)
- Build — TDD where it matters, evals before we ship
- Operate — observability, cost dashboards, alerting, on-call playbooks
- Iterate — model upgrades, prompt regression testing, continuous evaluation
When this is the wrong service
If you need a chatbot that summarizes a PDF, you don’t need us — buy an off-the-shelf tool. We’re useful when AI is on the critical path of a product or workflow and “it kind of works” isn’t acceptable.
Contact us to talk through what you’re actually trying to ship.