Practice 04 · Artificial intelligence & automationAI API integration: add intelligent features to your software, done properly
A prototype knocked together in a few hours with a model provider’s API makes you want to go further. Between that prototype and a feature shipped to your users lies all the engineering: structuring the exchanges with the model, containing costs, keeping response times acceptable, protecting data and checking that quality holds up.
A feasibility analysis, written feedback within 48 hours
Who it is for, and whenFeatures an AI API integration makes possible
What these projects share: an existing piece of software, yours or one we build for you, to which a capability for understanding, generation or recognition is added without redoing its architecture.
- Automatically summarising records or exchanges in a business tool, to give the essentials before the detail.
- Classifying incoming messages into your categories, and pre-filling fields from free text.
- Suggesting a draft in the interface (a reply, a product description, a report) from the data already present.
- Analysing images uploaded by users (site photos, product photos, receipts) to extract structured information.
The scopeWhat Kopus handles on the technical side
We design the integration layer that sits between your application and the model provider, for example OpenAI, Anthropic, Mistral or an open model hosted by you. This layer carries the prompts, their versions and their variables: changing an instruction does not require redeploying the application. It also enforces a structured output format, validated before use, so your code never depends on a free-text reply.
Cost control is built into the design: a model matched to each feature (a small, fast model is often enough to classify, a more powerful one is justified for drafting), caching of repeated responses, limits on the context sent, ceilings and alerts. Latency is handled with streamed responses and asynchronous processing when the user does not need to wait.
Security covers key management, filtering of personal data before sending, call logging and protection against prompt injection. We also plan fallback scenarios for when the provider is unavailable, too slow, or returns an unusable response.
The methodThe stages of an AI API integration at Kopus
Feature specification
Available inputs, expected output, structured format, error tolerance and behaviour on failure.
Model selection and cost estimate
Comparison of a few models on a sample, measuring quality, latency and unit cost.
Integration layer development
Versioned prompts, output validation, cache, limits, logging, key management.
Evaluation set and testing
Annotated real cases, automatic runs on every change, failure review with your teams.
Production with monitoring
Dashboard of calls, costs and errors, then adjustments based on usage.
Our positionWhy we always build expecting to switch models
Model providers change their prices, retire versions and publish new ones at a steady pace. An integration written directly against one provider’s API, with prompts scattered through the code, leaves you dependent on decisions you do not control. We therefore isolate the provider behind a single interface, version the prompts and keep an evaluation set that lets a replacement model be tested in a few hours.
Under the hoodOne model call, with guardrails from end to end
Your application never calls the provider directly: it talks to a layer that picks the model, masks sensitive data and rejects any response that breaks the format. Every call leaves a usable trace, prompt version, model, duration and cost, so drifts in quality or budget show up before they turn into incidents.
// server side onlyconst ai = gateway(env.AI_KEY);API key kept server-side const res = await ai.run({ prompt: "record-summary@v7",Versioned prompt model: config.model("summary"),Swappable model input: redact(record),Sensitive data masked schema: RecordSummary,Schema-validated output limits: costCap("summary"),Cost ceiling fallback: "queue-and-notify",Fallback if unavailable}); 200 · 1.2 s · cost €0.004Call logged
{"summary": "Customer since 2019, two late deliveries this quarter.","sentiment": "neutral","priority": "medium","next_actions": ["Offer a follow-up call","Check the carrier lead time"],"confidence": 0.86}- Schema valid
- Cost within ceiling
- Provider isolated behind one interface
- Evaluation set: up to date
Your questionsFrequently asked questions about this service
Why integrate an AI API rather than use a separate AI tool?
Because the feature runs where your users already work, on the application’s data, under your rules. The copy-and-paste into an external tool disappears, and the leak risk with it.
How much do model calls cost in production?
Cost depends on the model, the amount of text sent and received, and usage volume. We estimate it on your real data and set ceilings against any surprise bill.
How do we know quality is still good after a model update?
Through the evaluation set built during the project: it replays real cases and compares the outputs with expected results. Any degradation shows up before it reaches your users.
Which model providers do you use?
Those that fit each case: major market players, open models hosted in Europe or on your servers. We compare on your data before choosing.
Can you work on our existing code?
Yes. We work in your codebase, with your conventions and your tools, in coordination with your technical team.
What is an AI API?
An online service giving access to an artificial intelligence model, for text, images or speech, from your own software. Your application sends a request and receives an answer, without hosting the model.
Can we use a model hosted in Europe or on our own servers?
Yes. Good-quality open models can run on European servers or in your own infrastructure, which meets the strictest confidentiality constraints.
Go furtherPages related to this service
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Let’s bring your projects to lifeYour next project starts here.
Let’s connect AI to your tools with an integration that is measured and ready to evolve.
A feasibility analysis, written feedback within 48 hours