Training and promotion path
From experiment to a versioned artifact with a named approver.
Service offering
MLOps and AI infrastructure
Models and assistants need the same discipline as any other production service: data lineage, a release, monitoring, and a way to roll back.
Business problems
- A notebook that became a production dependency.
- No record of which data trained or grounded an answer.
- GPU and API spend with no owner.
Services and solutions
Serving
An endpoint with limits, logs, and a fallback when the model is wrong or down.
Cost and access
Quotas, keys, and environments separated for experiment and production.
Technical capabilities
- Feature and dataset documentation.
- Evaluation sets you choose, not a public leaderboard.
- Monitoring for latency, failures, and obvious drift signals.
Business benefits
- An AI feature you can turn off.
- Spend you can attribute.
- A handover your team can run.
Technologies
Your cloud's machine-learning services, container serving, and the warehouse or lake you already use. We name LLMOps separately under generative AI.
Engagement models
- A discovery sprint to confirm the outcome, constraints, and whether the work should proceed.
- A fixed-scope build when the architecture and acceptance criteria are clear.
- A monthly engineering partnership for a platform, reliability, or AI system that needs ongoing care.
- Staff augmentation when you need specific engineers inside your team. See IT staffing.
Questions
Do you train foundation models?
No. We implement and operate systems that use models from a provider or from your existing training setup.
What is not included?
We do not claim a measured accuracy number for your business until an evaluation set exists.
Talk with an engineer
Tell us the business outcome. We will say whether we can help, what the first step is, and what we will not pretend to have already done.
Request a consultation →