AI SystemsThat ExecuteReal Work
Production-grade RAG pipelines, autonomous agents, evaluation systems, and FastAPI backends. Built to run continuously, not to demo once.
Expertise
What we build
Production RAG Systems
Your retrieval works in demos but fails on real user queries. Precision drops, irrelevant results surface, and answer quality degrades under messy, unstructured input.
High-precision retrieval pipelines using pgvector, hybrid search (semantic + keyword), and reranking. Built for the gap between prototype retrieval and production-grade answer quality.
1docs = await vector_store.similarity_search(2 query, k=20, filter=metadata_filter3)4ranked = reranker.compress_documents(docs, query)5return ranked[:top_k]6
LLM Agent Infrastructure
Your agent works on the demo path but breaks when users deviate. State gets lost between steps, tool calls fail silently, and there is no way to audit what happened.
Multi-step agent workflows on LangGraph with persistent state, tool orchestration, approval gates, retry logic, and deterministic evaluation. Designed for reliability under real interaction patterns.
1agent = create_react_agent(2 model=ChatAnthropic("claude-sonnet-4-20250514"),3 tools=[search, execute, evaluate],4 memory=PostgresMemory(conn)5)6result = await agent.ainvoke(task)7
FastAPI Backends for AI Products
Your AI feature needs a real backend, not a notebook wrapped in an endpoint. You need async APIs, structured data access, migration discipline, and deployment automation from day one.
Production-grade API layers purpose-built for LLM-powered applications. Structured around pgvector, Alembic migrations, health checks, and operational readiness.
1@app.post("/v1/inference")2async def inference(req: InferenceRequest):3 async with get_session() as db:4 result = await model.predict(5 req.input, timeout=req.sla_ms6 )7 await db.log(result, latency=timer())8 return result9
AI Reliability Engineering
You are scaling prompts before you have evaluation discipline. Quality is checked by eyeballing outputs. Cost and latency are unmeasured. When something breaks in production, there is no way to tell what changed.
Deterministic evaluation frameworks, structured output validation, cost and latency observability, regression detection. The layer most teams skip between prototype and production.
1harness = EvalHarness(2 model=production_model,3 fixtures=load_fixtures("qa_golden_set"),4 metrics=[accuracy, latency_p95, cost_per_query]5)6report = harness.run_regression()7assert report.pass_rate > 0.958
Clients
Who we build for
Startups moving past prototype
You have a working AI prototype and engineering capacity, but not a specialized AI infrastructure engineer. The system needs to handle real users and real failure modes before your next raise.
Agencies delivering AI products
You sell AI-powered solutions but need specialized backend delivery capacity. Reliable, documented, and handoff-ready.
Teams evaluating capability
The work is public. Every architectural decision and tradeoff is documented in open-source repositories. Inspect the code before the conversation.
Architecture
How the systems work
Most modern AI systems reduce to two core execution patterns: retrieval pipelines and autonomous agent loops. We design, combine, and harden these patterns to operate reliably under real-world conditions.
Production RAG Pipeline
Agent Execution Loop
Process
How we build
Every engagement follows a structured execution pipeline: from system design to production monitoring. Reliability, evaluation, and observability built in from day one.
Portfolio
Systems in Production
A selection of systems designed, built, and deployed for real-world execution. Not prototypes, not demos.
Each system reflects a specific capability: retrieval, orchestration, evaluation, or data infrastructure, engineered for reliability under production conditions.
Select a node to explore each system. Every project is open-source with full documentation.
Why production AI is different
Demo-grade AI is easy to build. Production-grade AI is difficult to maintain.
The difference is not the model. It's the system around it.
Deterministic Evaluation
Replacing subjective "looks good" testing with measurable scoring and regression checks.
Cost Control
Preventing uncontrolled token usage through architecture, not afterthought optimization.
Latency Engineering
Designing systems that respond in real time, not seconds too late.
Operational Reliability
Building systems that continue working under load, failure, and scale.
If it cannot be measured, monitored, and trusted, it is not production-ready.
Contact
Let's build something production-grade.
Tell us about your system, your data, and what you need.
We'll respond within 24 hours with a specific technical plan.