AI & ML engineering
Models are the easy part. We build the agents, retrieval, serving and data infrastructure that let a small team ship AI safely, keep data private and keep GPU bills predictable.
When to call us
- You want AI agents or an assistant working on your own documents and systems
- Notebooks are in production and nobody can reproduce them
- Inference costs are unpredictable, or data cannot leave your cloud
What you get
- 01AI use-case assessment with cost, risk and a first pilot
- 02Agents, assistants and RAG search with evaluation and guardrails
- 03Private model serving (vLLM, Bedrock, Azure OpenAI) with autoscaling and cost limits
- 04MLOps: versioned data, training, model registry, monitoring and rollback
Typical tooling
Claude · OpenAI · Amazon Bedrock · Azure OpenAI · vLLM · MLflow · Ray
We work in your existing stack first. New tools are introduced only when the assessment shows a measurable gap.
Also in this practice
How it runs
Same four phases, scoped to this practice.
- 01 2–3 wks
Assess
Architecture review, cost baseline, risk register. A written report you own.
- 02 3–6 wks
Design
Target architecture, migration waves, SLOs and a delivery plan your team reviews.
- 03 Scoped
Deliver
Embedded engineers ship alongside yours. Everything as code, everything reviewed.
- 04 Ongoing
Operate
Hand over to your team, or keep us on 24/7. Runbooks and on-call either way.
Related work
AI code review with Amazon Bedrock, without code leaving the company
Read the case study →
Common questions
Do you work with our existing team?
Always. Our engineers work in your repositories and review processes. Knowledge stays with you when we leave.
Which cloud do you recommend?
We are partners of AWS and Google Cloud and work on Microsoft Azure every day, so the recommendation can follow your workloads, skills and contracts rather than ours.
How soon can you start?
Assessments typically begin within two weeks of a signed scope. Delivery teams are planned one quarter ahead.