Language models wired into your product with controls around them.
We connect OpenAI, Anthropic Claude, Google Gemini and other model APIs to your website, app or internal system. Each call has a clear contract, a cost limit, a test set and a fallback, so the feature keeps working when the model does not. Built from Lahore for clients worldwide.
What is AI API integration?
AI API integration means connecting a hosted language model, such as OpenAI, Anthropic Claude or Google Gemini, to your software through the provider's API. The work is mostly everything around the call: deciding what goes in, forcing a predictable shape on what comes out, limiting cost, protecting private data, testing quality, and defining what the product does when the model is slow, wrong or offline.
Provider terms on data retention and training differ by plan and contract, so we read the current documentation for the tier you will use before design is final.
Four layers that turn a demo into a feature.
A prompt that works in a chat window is not yet a feature. These are the layers we build around the model call before anyone outside the team sees it.
What each layer decides
- Contract. What the feature accepts, the exact shape it returns, the quality bar and the cases where it should refuse instead of guess.
- Context. Which data the model may see, what is removed first, how long anything is kept and which provider processing terms apply.
- Evaluation. A fixed set of real tasks and hostile inputs that is re-run whenever the model, prompt or retrieval step changes.
- Operations. Latency and cost tracking, rate-limit handling, retries, alerts and a person who can take over when the model cannot.
Need the model to answer from your own documents? That is RAG development.
Common features we connect to a model.
Most requests fall into a few patterns. Each one needs a slightly different contract and a different failure plan.
Drafting and rewriting
Product descriptions, replies, summaries and tone changes inside your editor or admin, always shown to a person for approval before publishing.
Classification and routing
Sorting tickets, leads, reviews or forms into labels you define, with a confidence note and a manual queue for unclear items.
Data extraction
Pulling names, dates, amounts and line items from messy text into a fixed schema that your database can accept after validation.
Search and answers
Natural-language questions over help pages or catalogues, with sources shown so staff and customers can check the claim.
Tool-calling features
The model asks your code to look up an order or book a slot. Your code decides whether the action is allowed.
Moderation and checks
Screening user text or uploaded content before it appears, with human review for anything the model flags or cannot judge.
Which model, which provider, which tier.
There is no single best model. The right choice depends on the task, the language, the data and the monthly volume you expect.
How we compare options
- Quality on your task. We test two or three models on your real examples, including Urdu, Arabic or mixed-language text where that matters.
- Cost per useful result. Price per token matters less than how many retries, long prompts and corrections each finished task needs.
- Latency. A reply that takes eight seconds is fine for a nightly report and painful in a checkout. We match the model to the screen.
- Data terms. Retention, training use and regional processing are checked against what your customers have been promised.
If privacy rules rule out a hosted API, see self-hosted AI models.
How an integration moves from idea to live traffic.
The path is short, but each step produces something you can inspect.
- 01
Define the job
One page that states the input, the output, who sees it, what a bad result costs and what happens instead when the model fails.
- 02
Collect examples
Thirty to a hundred real cases from your own business, including awkward ones, become the first test set.
- 03
Prototype safely
A thin server-side call is tested against the examples. Keys stay on the server and never reach the browser.
- 04
Add the controls
Schema validation, input limits, rate limits, retries, caching, logging and a daily cost ceiling are added around the call.
- 05
Pilot with people
A small group uses the feature while we read real inputs, tune prompts and add the cases that broke.
- 06
Release and watch
Rollout is gradual. Dashboards track completion, correction, escalation, latency and spend from the first day.
What goes wrong when a model is trusted too much.
Most incidents with AI features are ordinary engineering gaps, not mysterious model behaviour. We design against the following.
Failure cases we plan for
- Invented answers. Output is checked against a schema and, where possible, against your own data before it is displayed or saved.
- Prompt injection. Text from users, emails or web pages is treated as data. It can never change the rules or trigger an action on its own.
- Leaked private data. Personal details are removed or masked before the call where the task allows, and logs avoid storing raw sensitive text.
- Runaway cost. Per-user and per-day limits, caching of repeated requests and alerts stop a loop or an abuser from producing a surprise bill.
Guardrails lower the risk of model error. They never remove it, which is why a person stays in the loop for anything consequential.
What you receive and how success is judged.
An integration is finished when your team can run it without us. Success is measured by outcomes, not by how many calls the model handles.
What the handover contains
- Working code and documentation. Clean source in your repository, setup notes, environment variables listed and a short diagram of how data moves.
- Prompt and version history. Prompts are stored like code, with change notes, so a bad edit can be traced and reversed within minutes.
- The test set and results. Your examples, the scoring method and the latest scores, so future model changes can be compared fairly.
- A dashboard. Completion, correction and escalation rates, latency, errors and spend, shown in plain language for non-engineers.
Judge the feature by finished tasks, corrections made by staff and cases handed to a person. Raw usage volume says little by itself.
Direct API call, gateway or ready-made plugin?
Three ways to add a model exist. The right one depends on how central AI is to the product.
| Criteria | Custom integration | Ready-made plugin | Automation tool only |
|---|---|---|---|
| Control over prompts and output | Full, versioned in your code | Limited to plugin settings | Set inside a workflow node |
| Fit with your data and permissions | Built around your rules | Generic | Depends on connectors |
| Testing and monitoring | Test set and dashboards | Rarely included | Run history only |
| Best for | Product features and core flows | Quick content helpers | Back-office tasks, see automation |
| Upfront effort | Highest | Lowest | Low to medium |
AI API integration, answered.
Answers below come from the questions founders, shop owners and product teams ask us before they commit to a model provider.
Which AI providers do you work with?
We integrate OpenAI, Anthropic Claude and Google Gemini most often, and can connect others that offer a stable API. We recommend a provider after testing on your own examples instead of choosing by brand.
Will my data be used to train the model?
That depends on the provider, the plan and the contract. Business and API tiers usually have different terms from consumer apps. We read the current terms for your tier, tell you what they say and design so that sensitive text is not sent when it does not need to be.
Can you keep my API key safe?
Yes. Keys live only on the server, in protected environment settings, and calls are made from your backend. Browsers, mobile apps and public scripts never see them, and keys can be rotated without a release.
How do you stop costs from growing unexpectedly?
We set limits per user and per day, cache repeated requests, shorten prompts, route simple tasks to cheaper models and add alerts when spend moves outside the normal range. You receive a plain monthly view of usage.
What happens if the AI service goes down?
The feature degrades on purpose. Depending on the case it queues the job, shows a manual form, uses a simpler rule, or tells the user clearly that help is delayed. Nothing important depends on the model being available.
How do you test quality before launch?
We build a set of real tasks and difficult inputs, score the outputs against criteria agreed with you, and re-run the set after every prompt or model change. Launch criteria are written down before testing starts.
Can this be added to WordPress, Shopify or a custom app?
Yes. For WordPress we write a small plugin or function, for Shopify an app or middleware, and for custom products an API layer in Node.js, Laravel or Python. The pattern is the same on each platform.
How long does an integration take?
A single, well-defined feature is often a matter of weeks. Timing moves with the number of data sources, how strict the output must be, review and approval needs, and how much testing the risk calls for.
Add AI to your product without the guesswork.
Tell us the feature you have in mind and the data it would touch. You will get a short written plan covering provider options, controls, risks and the effort involved.