Adding AI to an existing web app or SaaS: choose the first feature

The decision extends beyond model selection. It determines who can use which data, where results appear, how users review them and what happens when AI is unavailable. Product owners should evaluate those questions alongside user demand before investing.

Talvyn Labs designs and develops AI Products and Agents within existing systems, covering task selection, user experience, data and permission integration, and evaluation. Representative user tasks provide the starting point for the scope.

Begin with usage evidence or questions received by support. Identify one user group, one task and the result they need for the next step. A project manager may need to understand recent progress before answering a customer. A service team may need a manual for the specific model being maintained.

Write an inspectable task statement: when the user opens this record, what information do they need, and what will they do with it? If those answers are unclear, the feature may not yet have a defined place in the product.

Consider task frequency, data readiness, reviewability and the consequences of an incorrect result together. An impressive task performed rarely may offer less value than a modest task users face repeatedly.

Contextual retrieval helps users who know what they need but cannot find it. Results should lead back to authorised records or documents and expose the evidence used. This can provide a useful first scope that reads information without changing records.

Summaries and drafts inside the workspace help when users must assemble scattered information before acting. An illustrative example is a project-status draft grounded in records the user can access. They need to inspect sources and edit the draft before sending it onward.

Preparation for multistep actions applies when AI selects information and uses tools to assemble work, such as preparing follow-up tasks from a customer request. That requires explicit permissions, approvals and recovery when only some steps complete. Calling a feature an agent is not a reason to include all of that in the first release.

Feature names should describe the result, such as “Summarise work history” or “Search the manual.” Users do not need to understand the model name to begin the task.

If work starts on a customer-detail page, the feature should use the context and permissions of that page rather than requiring a separate copy-and-paste exercise. It must still make the selected context visible. Having access to data is not a reason to send all of it to a model automatically.

Distinguish an AI suggestion from a saved record. Users need ways to inspect the original, revise the request, edit the draft and cancel. Applying a result should make the intended change clear, especially before sending a message or altering a shared record.

Consider an illustrative customer portal. A user requests a project-progress summary. The system retrieves only records they are permitted to see, offers a draft with source links and requests confirmation before sharing it. If the latest update is unavailable, it should identify the gap rather than invent progress.

Products serving multiple customers must isolate information by account and actual permissions. Check access during both retrieval and tool execution. A prompt saying “do not disclose another customer's information” is insufficient. Test accounts with different access rights as part of the feature evaluation.

Documents and customer messages can contain instructions the application should not obey. OWASP's prompt-injection guidance describes how external content can influence model behaviour. Tool boundaries and approvals therefore need application-level enforcement. A source document cannot grant itself permission to initiate a transaction.

For Talvyn Labs integrations, the starting scope is read-only access with activity logging. Changes to records and commitments to customers require a person to approve them within the agreed workflow.

Specify how the user continues when a response takes too long, records are incomplete or a model service is unavailable. The interface should communicate status, support cancellation and preserve entered work when the user returns to the ordinary workflow.

Repeated clicks or requests should not unintentionally duplicate actions. Regenerating a summary and changing a business record have different consequences. Their retry behaviour needs to reflect that difference.

Measure cost per accepted, completed task, including retrieval, model calls, retries and ongoing maintenance. Test representative workloads before making assumptions about subscriptions or pricing. The price of a single model call is not the full cost of operating an AI feature.

A useful first release has a clear task, an interface connected to existing work, a defined data boundary, an evaluation set and an owner after launch. Its results should establish whether users complete the task more successfully and identify failures that still need human handling.

To scope a feature with Talvyn Labs, prepare the current screens, sample requests, authorised data and user-permission requirements. These help identify the first feature and its integration points. If the delivery route is uncertain, compare ready-made AI and custom development and use the AI pilot guide to establish acceptance criteria before expanding.