The situation
An AI product that works well in your demo tenant often stalls at the customer. Their documents live in four systems with inconsistent permissions. Single sign-on and group mappings are different from what the product assumes. The model behaves differently on their data, and their security team wants answers about data flows before go-live. Your solutions engineers are good, but there are not enough of them, and enterprise deployments are queuing.
The risk in adding outside help is not only quality. It is that gaps in the product get hidden. A capable implementer, under pressure to get a customer live, writes a connector or a prompt workaround that makes the problem disappear in that one account. Your product team never hears about it. Six months later you have customer-specific code nobody owns and a product that still has the gap.
How the partnership works
Who owns the customer. You do. Your customer contract governs, your account team leads the relationship and your product team owns the product.
Who contracts. Normally you contract with me as a disclosed subcontractor for named deployments. Where a customer prefers to contract directly for implementation, that is possible with your agreement, using the same deployment brief and responsibility split.
Who delivers what. I deliver the customer deployment work: data connectors, identity and permission mapping, configuration, evaluation on the customer’s data, rollout with their team, and handover. Your product team handles product defects and decides on gaps. Your support team takes over after go-live.
How specialists are approved. If a deployment needs a specialist outside my scope, they are named and start only with your written approval and, where they will touch customer systems, the customer’s.
How payment works. I invoice the party I contract with, on the contract day rate or as scoped delivery per deployment, on terms agreed in writing first.
Customer deployment interfaces
Most AI product deployments fail or slip at the same interfaces, and the deployment brief addresses each:
- Data sources and connectors: which systems, which content, how often refreshed.
- Identity and permissions: how users and groups map, and whether the product enforces source permissions at retrieval time.
- Model and provider configuration: which models, regions and retention settings the customer’s policies require.
- Evaluation on customer data: an agreed set of real tasks the customer cares about, run before go-live.
- Observability and support: what is logged, who sees it and how issues reach your support team.
The signature deliverable, illustrated
Illustrative extract from a deployment brief, not taken from a vendor or customer:
| Item | Product capability | Configuration | Custom work | Owner |
|---|---|---|---|---|
| Document management connector | Yes | Site selection, refresh schedule | — | Vendor product |
| Legacy wiki connector | No | — | Export-and-index script, end date set | Dipankar, then customer IT |
| Group-based permissions | Partial: groups, not nested groups | Group mapping | — | Gap logged to product team |
| Answer citation format | Yes | Customer branding | — | Vendor product |
How acceptance is judged
The customer accepts the deployment against criteria in the brief: connectors working on agreed sources, permission tests passing, evaluation results on their tasks, and handover completed. You accept the partnership output separately: deployments delivered, every workaround logged with an owner, and product gaps escalated with evidence.
Ownership and handover
The customer owns its configuration and data. Your product team owns the gap log. Custom work has a named owner and an end date. Your solutions team receives the deployment playbook so the next customer starts further along.
When to choose something else
If you want me inside your solutions team as an individual contractor, see hire a forward-deployed AI engineer. For a wider view of embedded delivery, see forward-deployed AI engineering. If you build a SaaS product and the issue is the AI feature itself, not deployment, see AI product delivery for B2B SaaS. All partner routes are on the partners overview.