We build RAG pipelines, AI agents, and LLM integrations for companies that need more than a demo. Reliable, maintainable, and actually deployed — not just prototyped.
Focused on the AI work that actually delivers value in production
Production-grade retrieval-augmented generation systems. Chunking strategy, embedding pipelines, reranking, evaluation — built to stay accurate as your data grows.
Autonomous agents that use tools, make decisions, and complete multi-step tasks — with the reliability patterns and evals to trust them in real workflows.
Add AI capabilities to your existing product without the chaos. We scope, architect, and ship LLM-powered features that integrate cleanly with your stack.
We're a small, senior AI engineering team. No juniors running your project. No bloated process. Just experienced engineers who have shipped production AI systems and know what breaks after the demo.
Most of our work falls into a pattern: a company has a real problem, they've seen the AI demos, and they need someone who can bridge the gap between "this looks impressive" and "this is running in prod and working six months from now." That's what we do.
We build for the system that runs in six months, not the demo that impresses on day one.
We'll tell you when you don't need AI, when a simpler approach works better, and what the real risks are.
You work directly with the engineers building your system — not an account manager relaying messages.
We measure whether AI systems actually work. Evaluation frameworks are part of every engagement, not an afterthought.
A process built around one constraint: AI quality you can't measure is just vibes.
"We need AI to do X" usually turns into something different once we sit with it for a week. We start with discovery — not code. What does success actually look like, and is AI the right tool to get there?
For AI work, that means building a small golden dataset early. You can't iterate on quality you can't measure. We agree on what "good" looks like before a line of code is written.
A working spike against your actual data beats a deck of architecture diagrams. We prototype in days, not weeks — and we prototype against the messy real data, not a clean sample.
Same discipline as tests in normal software — skip it and you pay for it later. Regression harnesses, CI integration, and quality metrics are built in parallel with the system, not bolted on at the end.
One real use case end-to-end before broadening scope. This keeps feedback loops tight and prevents the classic failure mode: a system that technically works but solves the wrong thing.
Observability, real user behavior, continuous evals. This is where most AI features quietly die. We build for production from day one — and we hand off with docs and a runnable eval suite so your team can keep iterating without us.
Real systems, shipped to production. Client details anonymized.
A legal services firm manages thousands of PDFs per matter — deposition transcripts, exhibits, filings. Their teams needed to find specific testimony and evidence across all of it. Traditional keyword search failed in both directions: missing relevant testimony when witnesses phrased things differently, drowning in irrelevant hits when terms were common. The work was being done manually by paralegals and junior associates.
A production RAG system on OpenSearch with hybrid lexical + vector retrieval, LLM-driven query rewriting on the way in, and re-ranking on the way out. Every answer cites back to the exact source page — non-negotiable in legal, where attorneys must verify every assertion. Built on the Vercel AI SDK with streaming responses and structured citation blocks parsed in real time.
The eval infrastructure was built alongside the system from day one: golden datasets, regression testing on every prompt change, Promptfoo running in CI. Retrieval quality became something we could measure and iterate on with confidence — not something we eyeballed.
Tell us what you're building and where you're stuck. We'll give you an honest assessment of what's feasible, what's not, and what it would take to ship it.
Fort Worth, Texas, USA