Domain-tuned systems built for your data when the general-purpose model isn't enough.
When off-the-shelf models aren't enough, we design and train domain-tuned systems — and the platform that supports them.
- Typical duration
- 12–16 weeks
- Squad
- 4–6 senior engineers
- Starting at
- $32,000 / month
- Task accuracy lift over baseline
- +23 ptsTask accuracy lift over baseline
- Inference cost reduction
- −68%Inference cost reduction
- Eval coverage at handover
- 1,200 casesEval coverage at handover
General models are a floor, not a ceiling.
Frontier models are extraordinary generalists and mediocre specialists. On your vocabulary, your edge cases, and your accuracy bar, the gap between 'impressive demo' and 'safe to deploy' is where custom work lives. That work is mostly evaluation infrastructure — not architecture.
Sound familiar?
- Accuracy plateaus below the bar your business requires
- Domain vocabulary the base model consistently misreads
- Latency or unit-cost that breaks the business case at scale
- No way to prove a model change made things better
How we deliver custom ai development.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Benchmark
Before anything is trained, we build the eval set. A few hundred labeled examples that encode what 'correct' means for your business, reviewed by your experts.
Baseline
Frontier models, prompted well, measured honestly. Often this clears the bar and the custom work stops here — which we will tell you.
Specialize
Fine-tuning, retrieval augmentation, distillation, or a purpose-built architecture — chosen by what the benchmark says will move the number.
Harden
Serving infrastructure, drift monitoring, regression gates in CI, and rollback paths. The model is a fraction of what ships.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
Domain fine-tuning
Supervised and preference-based tuning on your data, with strict train/eval separation and documented dataset provenance.
Custom architectures
Multimodal, time-series, and graph models when the problem genuinely isn't a language problem — designed to the constraint, not the trend.
Evaluation harnesses
CI-runnable eval suites that block a regression before it reaches your users. The single highest-leverage artifact we build.
Distillation & cost tuning
Smaller, faster models trained against a frontier teacher — often 10x cheaper per call at equivalent task accuracy.
Production hardening
Autoscaling inference, request shaping, graceful degradation, and the observability to debug a bad answer six weeks later.
Drift & retraining
Monitors on input distribution and output quality, plus a documented retraining trigger so decay is caught by a dashboard, not a customer.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- PyTorch
- Python
- Claude
- OpenAI
- Hugging Face
- AWS
- Docker
What this looks like in production.
Predicting equipment failure 72 hours out
Vertex Manufacturing
- Problem
- Unplanned downtime cost the company an estimated $42M annually across 14 production lines.
- Solution
- A multimodal model combining SCADA telemetry, vibration sensors, and floor-camera vision to forecast failures.
- Outcome
- False-positive rate dropped to 9%, downtime reduced 34%, and OEE improved by 6 percentage points.
- False-positive rate
- 9%False-positive rate
- Downtime reduction
- 34%Downtime reduction
- OEE lift
- +6 ptsOEE lift
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.