Adding AI to an app: voice, analysis and failure states
Plan an AI feature around the user journey, permissions, output checks, failure recovery and operating costs, with lessons from Nurena.
Read the guideExpertise / 04 / AI integration
AI integrationfor useful product features.
Turning speech into text, analysing content or simplifying a workflow. We define the job first, then decide what role AI should play.
Define the scope together
For teams adding a bounded, measurable AI function to an existing product. We connect tasks such as transcription and content analysis to the user’s actual workflow.
What determines timing and budget?
Input type, output format, provider choice, evaluation examples and existing APIs shape implementation. Variable operating costs such as model usage are calculated separately.
What we work on
01
Product features
We place features working with text, voice or existing content into the natural flow of your product.
02
Context and output
We design the context sent to the model, the expected response format and how results appear in the interface.
03
Failure and cost
Waiting, timeouts, retries and usage limits are considered from the start.
What you take away
We define the scope around your needs in our first conversation.
- 01Integrated AI feature
- 02Input and output contract
- 03Loading and error flows
- 04Usage and operating notes
How we move forward
01
Define the task
We agree on success criteria and a bounded job for the model.
02
Try real examples
We evaluate output, latency and cost using representative inputs.
03
Connect it to the product
We build a flow that shows progress and lets users recover from failures.
A closer look at the work
In Nurena’s voice and analysis flow, AI is part of keeping a journal. A local draft helps recover sessions interrupted during analysis.
Explore the approach in NurenaBefore you start
Which AI feature should we start with?
Transcription, content analysis or answers within a defined workflow may be appropriate. The first step is one task with clear success criteria.
Will AI always produce the right answer?
No. Representative evaluations, output validation and appropriate handling of errors or uncertainty are planned. Human review for consequential decisions needs separate definition.
Are usage costs fixed?
They depend on the service and volume. We model request count, input length and retries, then define suitable limits.
How is user data handled?
We first identify which data each function needs and where it is processed. Permission, retention and deletion behaviour should follow the product’s actual data flow.
Guides
Guides to help you decide
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Let’s talk about your project