A path to production for your models and LLM apps.

• ForData and ML teams on Databricks or Azure whose models and LLM applications live in notebooks, pilots or manual releases.
• The problem it solvesA model works for one data scientist and never becomes a service. Releases take weeks, drift goes unseen and nobody can say which version is running.

• 01Assessment of your current path to production
• 02Registry, pipelines and serving as code in your repositories
• 03First model or LLM application taken through the evaluation gate to production
• 04Monitoring, alerts and a runbook
• 05Handover and training for your team

1. 01

   Assess the path

   We trace how a model reaches users today and rank the gaps.

2. 02

   Build the platform as code

   Terraform and Asset Bundles define workspaces, registry, serving and monitoring, deployed only by pipelines.

3. 03

   Run the first use case through

   One real model or LLM application goes from data to production through the evaluation gate and approval.

4. 04

   Monitoring and runbook

   Dashboards and alerts for drift, quality, latency and spend, with a runbook for each alert.

5. 05

   Hand over and train

   Your team learns to release, monitor and retrain on the same path.

Scope fixed before we start; quote after a first call.

ML and LLM operations

Other products

• Monthly, ongoing

  Managed AI operations

  We keep your agents and platform healthy every month: monitoring, evaluation runs, cost budgets, model updates, incident response and a monthly report.

  See the product

• 4 weeks

  AI agent pilot

  One AI worker with a fixed role next to the systems you already use, a human check where it counts and a report on tasks handled.

  See the product