Ten ways we put AI into production.
Most engagements start with one and grow into three. We staff senior, we ship in weeks, and we stay on to run what we build.
AI product strategy
We map your workflows, score the opportunities on value and feasibility, and come back with a build plan, a cost model and the risks written down. Two to four weeks, ending in something you can fund.
LLM & agent development
Retrieval, tool use, multi-step agents, voice. We build the eval harness before the demo, so quality is a number you can watch rather than a feeling.
Custom software development
Web and backend engineering by senior teams. Typescript, Python, Go, Postgres, cloud-native. You get working software every two weeks and the repo from day one.
Data engineering & platforms
Ingestion, transformation, quality checks, lineage and access control. Batch and streaming. If the data is wrong, the model is wrong, so we start here more often than not.
Legacy modernization
We wrap, strangle and replace in stages — the old system keeps running while the new one takes over path by path. No 18-month big bang.
Mobile apps
React Native or fully native when it matters. Offline behaviour, push, store releases and crash budgets handled as part of the work, not after it.
MLOps & AI infrastructure
Deployment pipelines, inference infrastructure, prompt and model versioning, drift and spend dashboards. The unglamorous half that decides whether AI survives contact with users.
Dedicated teams
Engineers, a lead and a designer working in your process and your tools. Long-lived teams, not rotating contractors, with a two-week ramp.
Workflow automation
Document handling, intake, triage, back-office ops. We automate the path end to end and leave a human in the loop exactly where the risk is.
AI data operations & labeling
Labeling, annotation and QA for text, documents, images and audio, run by a dedicated team against your guidelines. Gold sets, inter-annotator agreement, edge-case review and a feedback loop back into the model — plus RLHF and eval-set curation when the task calls for human judgement.
Engagement models
Discovery sprint
A fixed-scope look at one opportunity, ending in a build plan and a cost model.
Project build
Scoped delivery of a product or system, fixed team, milestone-based.
Dedicated team
A standing squad inside your org chart, scaling up and down by quarter.