Data, ML and AI engineering
We design and build data science, ML and AI systems, from models to production ops.
Services
What we build
Eight practice areas, one studio covering the whole path to production.
Data science
Analysis, experiments and forecasting delivered as reproducible code.
Machine learning
Models from baseline to packaged serving, with honest evaluation.
NLP and LLM applications
Retrieval, extraction, classification and assistants built around language models.
Computer vision
Detection, inspection and document pipelines from labeled data to deployment.
MLOps
Training pipelines, registries, CI/CD and monitoring for models in production.
LLMOps
Evaluation sets, tracing, prompt versioning and safe rollout for LLM apps.
DevOps
Infrastructure as code, containers and observability for data and AI workloads.
Python and Node.js development
APIs, data services and integrations with tests and deployable code.
Privacy
Privacy-aware by design
Systems that touch personal data need deletion, access and records built in, not bolted on. Our guides cover the operational side, and we apply the same thinking to what we build.
- Data subject request automation
Run access and deletion requests as a repeatable workflow.
- Data mapping and discovery
Know where personal data lives before someone asks.
- AI and privacy
Prompts, logs, training data and vendors.
- Security
Security controls privacy laws expect, and breach deadlines.
- Data processing agreement
What a DPA must contain, clause by clause.
Process
How we work
1. Discover
We read your data, constraints and goals, then agree on scope and what done looks like.
2. Build
Small, reviewable increments with tests, so you see working software early.
3. Ship
Deployment, handover documentation and a runbook your team can follow.
4. Operate
Monitoring, retraining and fixes, if you want us involved after launch.
Have something to build?
Tell us what you are working on and we will reply with questions and a quote.
Request a quote