How to scope an AI integration project

1. Define the job before choosing a model

“Add AI to our product” is not a scope. “Help a support agent find the relevant approved policy and draft a response with source references” is a useful starting point. It identifies a user, an input, an output, and a point where a person can check the result.

Write down what happens today, what the integration may change, and what must stay under human control. A suggestion that someone reviews has a different risk profile from an agent that issues refunds or changes customer records. Keep the first release narrow enough to evaluate against the existing workflow.

• Name the user and the task.
• Provide representative inputs and examples of acceptable outputs.
• List actions that need approval.
• Define the fallback when the AI cannot complete the task.

2. Map the data and its access rules

Identify the documents, database records, and external systems the feature needs. Record who owns each source, how often it changes, and which users may see it. Retrieval must apply those permissions before information reaches the model; hiding a source link after generation does not undo a disclosure.

For a multi-tenant SaaS product, include a test where two customers ask the same question but have access to different records. Also test a revoked permission and a deleted document. These cases make the difference between a convincing prototype and an integration that can be trusted inside a real product.

Private source data does not automatically mean private model processing. Agree which information can leave your infrastructure, the selected provider’s handling terms, retention requirements, and any hosting restrictions before implementation.

3. Decide whether you need retrieval, tools, or fine-tuning

Use retrieval-augmented generation (RAG) when the answer needs relevant information from your documents or records. The retrieval system supplies context at request time. Document quality, source freshness, search relevance, and permission filtering are therefore part of the product scope.

Use tool integration when the feature needs to query a live system or perform an action. Define the permitted operations, validate their inputs, and require approval for consequential changes. A model proposing an action is different from your application authorizing it.

Consider fine-tuning when a repeatable behavior or output requirement remains difficult after testing prompts and examples. It is not a substitute for querying current records. Ask a delivery partner to explain the simpler alternative and show why the additional complexity is justified.

4. Agree how quality and cost will be measured

Create a small evaluation set from representative work, including missing information, ambiguous requests, and restricted records. Decide what a correct answer looks like, when the feature should decline to answer, and when it should ask for clarification. Keep separate examples for testing changes so the evaluation does not simply reward memorizing the development examples.

Measure response time and operating cost alongside answer quality. Estimate cost per completed workflow, including retrieval, model calls, retries, storage, and background processing. A low price per model call is not enough if a workflow requires many calls or frequent human correction.

• Answer correctness and source support.
• Permission boundaries and refusal behavior.
• Time to a usable result, including review.
• Cost per workflow under representative load.
• Recovery from provider failures and invalid tool responses.

5. Separate a feature integration from a complete SaaS build

Adding an AI feature to an existing application can reuse its authentication, billing, navigation, and deployment. Building a new SaaS product must include those foundations as well. A prototype, a narrowly scoped MVP, and a full product release should not share an unexplained delivery estimate.

Ask for a proposal that separates discovery, implementation, evaluation, launch, and ongoing support. State the assumptions behind the estimate: access to data, availability of external APIs, expected traffic, and who provides feedback. Agree what happens when those assumptions change.

At handover, request source code, configuration instructions, the evaluation set, cost monitoring, a failure runbook, and an explanation of how to change models or prompts safely. Ownership includes the ability to operate and improve the feature after the initial team leaves.

Discuss the workflow using relevant project evidence

Code Huddle’s House Hint case study describes AI-assisted matching in a real estate marketplace. Ensemble – Flux Foundry describes an AWS-native industrial data platform that resolves inconsistent spare-parts records into canonical identities. They illustrate different product and data problems; neither should be treated as a benchmark for an unrelated project.

For your own estimate, share a redacted workflow example, a description of the existing application, and the acceptance criteria. Those details support a more useful discussion than choosing a framework or asking for a generic chatbot price.