AI and ML engineering that reaches production
LLM features, AI agents and data pipelines, built to a measured baseline. Many AI pilots stall before production. Ours are scoped so you can see the result in four to six weeks.
What we deliver
- AI readiness audit: data, use cases, costs and risks, with a ranked list of opportunities
- LLM applications: retrieval-augmented generation, copilots and document processing
- AI agents that call your APIs, with evaluation suites and human review steps
- Data platforms and pipelines on Databricks, Snowflake or your cloud
- MLOps: model monitoring, evaluation and cost tracking in production
A good fit when
- You ran AI experiments and none reached production
- You need to prove ROI before a larger investment
- Your data is spread across systems and not ready for AI yet
Skills and technology
How we start
Every engagement starts with a two-week, fixed-price discovery. You get an audit, a plan and a budget, and you can stop there and keep all of it.
Then a four-to-six-week pilot puts a working slice in production. Only after that do we agree on a long-term team.
Related result
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Read the case study: [Client name][What changed for the client]
Questions buyers ask
How do you measure an AI pilot?
Before the pilot we agree on a baseline, such as handling time, error rate or cost per task. The pilot report compares the result against it.
Is our data used to train models?
No. We use enterprise model endpoints with training disabled, and we document every data flow in the pilot design.
How do you handle EU AI Act requirements?
Discovery includes a risk classification of each use case and the documentation the classification requires.
Talk to an engineer, not a salesperson
Book a 30-minute call. You get a first view on scope, team shape and budget, and we sign an NDA before you share details.