Multi-step agents that plan, call tools, and verify their own output. We design the tool surface, write the prompts, and wire up the eval loop so the agent gets better over time.
Hybrid retrieval, chunking strategies tuned to your content, reranking, citation, and caching. We build RAG pipelines that hold up at scale, not demo-ware.
Background workers that classify, route, draft, and QA — wired into Slack, Linear, Salesforce, or whatever you actually use. With proper retries and human-in-the-loop where it matters.
A custom eval harness and dashboard for every project. Regression tests, golden datasets, and per-model leaderboards so you can swap models without flying blind.
When prompting and RAG hit the ceiling, we fine-tune. SFT, DPO, or full continued pretraining — on whichever base model the eval says is best for your task.
Two-week discovery engagements: we interview your team, audit your stack, and deliver a ranked roadmap with cost, effort, and expected impact for each bet.
Short cycles. Honest scoping. Working software at every checkpoint.
1–2 weeks. We talk to users, audit data, and pressure-test the problem. If AI is the wrong answer, we’ll tell you.
2–3 weeks. A working prototype against a real eval set — not a happy-path demo.
4–8 weeks. Harden, monitor, integrate. Ship behind a feature flag to a real user cohort.
Knowledge transfer to your team, plus an ongoing support retainer if you want one.
Most engagements start with a free 30-minute call to figure out if we’re a fit.
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