Adjoint is a small AI lab. We build language-model systems — agents, retrieval, and the evaluation infrastructure that keeps them honest — and put them into production alongside a few research labs and ambitious startups.
Most teams hit a wall when a prototype meets reality. Latency budgets, regressions on real users, evaluation that doesn't generalize. Our practice is built around the joints where those problems live.
Tool-using systems that survive contact with messy production data. We design the planner, the memory, the failure modes — and the off-ramp when the model is wrong.
Hybrid retrieval pipelines that actually answer the question. We treat indexing, ranking, and reranking as a single system — measured end-to-end, not piece by piece.
The unglamorous core of every shippable system. We build evaluation harnesses, golden sets, and continuous regression tests — and we'll teach your team to maintain them.
A sample of engagements, lightly anonymized. We work in long arcs — typical engagements are 3–9 months with embedded engineering.
We stay small on purpose. Engagements are selective and long-arc; we're more useful as a fractional team than as a vendor.
Short, technical, written for engineers and researchers who have to ship something on Monday.
Why we stopped trying to freeze evaluation benchmarks, and what we do instead.
A small post on the single biggest agent-reliability win of the last year.
What the math actually means, and why it's the right name for what we do.
We take on a small number of engagements each quarter. A short note about your problem is the right way to start — we'll reply within a week, even if it's a no.