How do you hand over an AI deployment so it keeps working after you leave?

Make yourself unnecessary on purpose. Leave the customer's team with an owner, the means to measure (the evaluation set and dashboards), the means to change it safely (versioned prompts and configuration, a tested release process), written knowledge (architecture, decisions, runbooks), and practice: they should have handled at least one change and one incident with you watching before you go.

1. A named owner and team

Someone at the customer is accountable for the system: its results, its costs and its changes. Without an owner, AI systems quietly decay as documents, products and models change around them.

2. Measurement they can run

• The evaluation set, with instructions to run it and a clear pass bar.
• Dashboards for quality samples, cost, latency, errors and hand-off rates, with alerts going to their team, not to you.
• A monthly report on the business metric against the baseline.

3. Safe change

• Prompts, tool definitions, retrieval settings and model versions in version control.
• A release checklist: run the evaluation set, compare with the current version, roll out to a share of traffic, watch the metrics.
• A way to roll back in minutes.

4. Documentation that answers real questions

• Architecture and data flow: what connects to what, with which credentials.
• Decisions and why they were made ("we chose hybrid search because product codes failed with vectors alone").
• Known limitations and out-of-scope requests.
• Runbooks: quality drops, cost spikes, provider outage, a document source changes format, a model is announced for retirement.

5. The changes that will come

• Content: new products and policies, so how documents get into the index and who checks them.
• Models: providers deprecate and retire models on a schedule, so a model upgrade is a routine task with a tested procedure.
• Scope: new requests from users, so how they are evaluated before being added.

6. Practice before leaving

Let their team make a real change (a prompt improvement, a new document source) and handle a simulated incident while you watch and help. Fix the documentation where they got stuck.

7. Stay reachable for a while

A planned check-in after 30 and 90 days catches drift early and often leads to the next project.

What this shows an interviewer

That you measure success by the system still working and improving a year later, not by the launch date. That is what turns a pilot into a lasting customer.