Applied AI Research
Embedded research squads framing the right problem, the right benchmark, and the model architectures that move them.
Most AI projects fail because the team optimized the wrong metric. Our research practice is the slow, expensive answer to that: framing the problem so the work that follows can actually be evaluated, and the model decisions defended.
Why we treat research as a separate practice
An engineering team can ship a model. A research team can tell you whether the model is improving. Without the second discipline, you spend two quarters polishing a baseline that was never going to clear the bar — and you only find out after launch. We embed researchers next to product teams so the problem and the metric stay honest while the build runs.
What we do
Problem framing
Translate a business outcome into a falsifiable hypothesis the team can run experiments against.
Benchmark design
Build an internal eval set that reflects production traffic, not a generic leaderboard.
Architecture exploration
Test foundation-model choices, fine-tuning regimes, retrieval shapes, and agent loops side by side.
Baselines & ablations
Establish the floor before chasing the ceiling. Every claimed gain comes with the ablation that proves it.
Literature triage
Filter the firehose of weekly papers into the three or four that change your engineering plan.
Handoff & docs
Leave the team with reproducible experiments, decision logs, and a benchmark they can rerun without us.
How an engagement runs
- 01
Week 1 — Frame
Sit with the operators. Pin down the metric. Write the eval contract.
- 02
Weeks 2–4 — Baseline
Ship a working baseline against the internal benchmark. No fine-tuning, no tricks.
- 03
Weeks 5–10 — Iterate
Run the candidate architectures. Ablate. Report weekly. Kill what doesn't move the metric.
- 04
Weeks 11–12 — Handoff
Document the winning approach, the failed branches, and the eval harness the team will inherit.