Service · MLOps & AI Infrastructure
The part that keeps working after launch week
A model or agent that nobody can redeploy, version, or observe becomes a liability within months. This is the deployment path, the version history, and the monitoring that stop that happening.
- One command to deploy, one command to roll back
- Every version recorded with what changed and who shipped it
- Cost and latency visible per workflow, not as one monthly bill
- Sized for the team you actually have, not a platform team
What you get
Deliverables, not slideware
- 01
Deployment pipeline
Repeatable deploys with a rollback that has been tested, not assumed.
- 02
Version registry
Which model, prompt, and configuration is live, with the history behind it.
- 03
Observability
Latency, error rate, spend, and output quality checks on every workflow.
- 04
Runbook
What to do when it breaks at 7am, written for whoever is on call.
- 01deploy: one command · rollback tested weekly
- 02live versions: intake-agent v14 · scoring v6
- 03alerts: error rate >2%, spend >$40/day, p95 >4s
- 04on failure: pause workflow, route to exception queue
- 05owner: named, with escalation contact
How it runs
What actually happens, step by step
No discovery theatre. Each step ends with something you can read or use.
- 01
Inventory what runs
Every model, prompt, and job currently in production.
- 02
Make deploys boring
Same path every time, with a rollback that is exercised.
- 03
Instrument
Spend, latency, failures, and output checks per workflow.
- 04
Hand over the runbook
Your team can operate it without calling us.
Tangible
What lands in your hands
Named objects with a format and a week attached, so the handover is easy to picture.
Working automation
One process running end to end, on your data, with a human approval step.
Deployed servicePhase oneOperating runbook
What to do when it fails, who to call, how to roll back.
PDF · 8 pagesFinal weekEvidence dashboard
The numbers that prove it worked, refreshed daily.
Live URLWeek 5Source repository
Yours from day one, including the prompts and the evaluation set.
Git repo + READMEWeek 1
Operating Runbook
Failure modes, rollback steps, and who owns each alert.
This is the real template, with client figures replaced by representative ones. Read it before you talk to us — if the format is not useful to you, the engagement will not be either.
Before and after
What the change looks like in the week
Red is the cost you carry today. Green is what the system gives back.
Today
- Nobody remembers how the model was deployed
- Prompt changes are made live with no history
- The AI bill arrives with no breakdown
- Failures are found by a customer
After
- Deploys and rollbacks that anyone on the team can run
- A version history for every change
- Spend attributed per workflow
- Alerts that reach a person before the customer does
Objections
Answered before you ask
Do we need this if we only run one agent?
Usually a light version of it. One agent still needs a rollback and a spend alarm. We scale the setup to what is running.
Which cloud do you use?
The one you already pay for, wherever possible. We do not move hosting without a cost or risk reason written down.
Is this a monthly retainer?
It does not have to be. The runbook exists so your team can own it. Support is optional and priced separately.
Keep going
Where people look next
Related work, the category this sits in, and the proof behind it.
One next step, and it is a paid one on purpose
The AI Opportunity Diagnostic is a fixed-scope engagement. You leave with a ranked plan you can act on with us or without us.
Still sizing the problem? Write to us in your own words first. A person reads it.