Engineering guides / AI and realtime voice integration
By Arte Soft engineering · 24 October 2026
AI app integration: scope a useful workflow before choosing a model
An AI feature earns its place when it helps someone finish a real task. Before buying an integration, describe the work it replaces, the mistakes that matter and the point where a person must stay in control.
Choose one task that can be evaluated
“Add AI” is not a useful delivery boundary. “Turn a dictated note into an editable project update” is. Capture examples of correct output, incomplete input and a request that should be declined. Identify whether the user needs a draft, a factual answer, a classification or an action in another system. These require different checks, even when they share a model provider.
Map the boundary around the existing application
An integration should inherit your authentication and permissions rather than invent a parallel identity system. Show which records the model can read, which fields it can modify and which operations remain server controlled. A customer support assistant must not retrieve another customer’s documents simply because a prompt asks for them. Access checks belong before retrieval and again before committing a change.
Specify the smallest useful delivery
A practical first engagement includes the application entry point, a server adapter, a versioned instruction set, a representative evaluation collection and a failure interface. Agree who owns prompts, secrets and provider accounts. Include a manual fallback so an unavailable model does not turn an optional feature into an application outage. Streaming polish comes after the underlying task can succeed reliably.
Make acceptance about the work, not fluent prose
Review outputs against the same examples before and after a model or prompt change. Count unsupported claims, incorrect edits, omitted fields and recovery failures separately. Record response time and usage alongside quality so a faster configuration does not silently damage usefulness. Have the product owner review borderline cases: a technically valid JSON result can still propose the wrong business action.
Protect the operating budget and release path
Estimate cost from realistic input sizes and repeat requests, including retries and retrieval context. Set limits per task and per account, with clear handling when a limit is reached. Keep provider-specific code behind an adapter, but do not promise that every model is interchangeable. Differences in tools, context handling and output behavior need deliberate migration tests.
Before requesting an AI integration estimate
- Choose one example where the current manual process costs time, and provide the expected finished result rather than only a feature name.
- Identify the person who can approve a draft and the server permission required to commit it to the existing application.
- Define the fallback screen for an unavailable provider, including what happens to the user’s unfinished input.
What to bring to an integration review
Bring several sanitized tasks, existing API documentation, the release platforms and an explanation of sensitive data. Arte Soft’s published GPT Realtime Assistant provides a concrete product to discuss alongside a proposed integration; it is not a claim that your workflow already exists in it. The review should end with a bounded implementation plan, an evaluation checklist and an explicit list of capabilities postponed until the first task works.
Relevant Arte Soft work
GPT Realtime Assistant — our own published product is our own product or engineering project. It provides context for the work we discuss here; it is not a claim of identical results for every client deployment.
Sources and further reading
Need help with AI integration discovery?
Send the current app, system or brief, the behaviour you need and your constraints. We can agree a discovery, implementation or repair scope with concrete acceptance criteria.
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