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AI Model Integration

We put leading AI models to work inside the software you already run, securely and at a cost that makes sense.

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AI model integration: an engineer connecting a large language model to a business application

We pick the right model for each job, from frontier models like GPT, Claude and Gemini to open-source models you can host yourself, and connect them to your app, website or back office through secure APIs.

Your own documents and data become usable through retrieval (RAG), so answers stay grounded in your business. We handle prompts, evaluation, caching and monitoring so quality stays high and running costs stay predictable.

From model to working feature

Put a model in your product

We connect the right model to your app, website or internal system, so features like search, writing help, image tools or virtual try-on run where your users already are.

  • LLM integrationAdd AI features such as summaries, search, writing and classification to your web and mobile apps.
AI Fitting Studio website home page

Our product: AI Fitting Studio

Models we work with
  • Claude
  • GPT
  • Gemini
  • Llama

Answers from your own data

With retrieval (RAG), the model looks up your documents, products and policies before it answers, and shows where each answer came from.

  • RAG on your dataAnswers grounded in your documents, knowledge base and databases, with sources shown.
How a question gets answered
  1. Your documents and dataManuals, policies, product sheets
  2. Searchable indexUpdated when your files change
  3. The model reads the best matches
  4. Answer with its sources

Measure before you ship

We build a test set from your real cases and score every version against it, then fine-tune when a model needs to learn your style or terms.

  • Fine-tuning & evaluationTune models for your tone and tasks, and test them against real examples before launch.
Correct answers on your test set Example figures

Keep data in house

When data can't leave your building, we run open-source models on your own servers or private cloud, with the same features and no outside access.

  • Private & self-hostedOpen-source models on your own cloud when data has to stay in-house.
ask.ts
const answer = await ai.ask({
  model: "private-llama",   // runs on your servers
  question,
  sources: await search(question, { top: 5 }),
  rules: ["Cite every source", "Say when you don't know"],
});
The Webtronix team at work in the Islamabad office
The team behind your project: analysts, designers, developers and testers under one roof.

How we work

  1. Week 1Choose the featureWe agree what the AI should do for your users, what data it can use and how we'll judge it.
  2. Weeks 1–2Compare modelsWe test a few models on your real examples and compare accuracy, speed and running cost.
  3. Weeks 2–6Build it inWe connect the chosen model to your product and data, with caching and limits to keep costs predictable.
  4. LaunchTest and releaseScored against the test set, then released to users with monitoring in place.
  5. OngoingWatch and upgradeWe track quality and cost, and move to better or cheaper models as they appear.

Our products and client work for AI Model Integration

All products

Questions

Which model should we use?

It depends on the job. We test several on your own examples and recommend the one with the best mix of accuracy, speed and cost, and keep the code ready to switch later.

How much does it cost to run?

Most features cost cents per use. We estimate running costs during testing and add caching and limits so the bill stays predictable as usage grows.

Will the model be trained on our data?

Not unless you choose to fine-tune. We use providers whose business terms exclude your data from training, or host models privately.

Can you add AI to software another company built?

Usually, yes, as long as we can reach its data or API. We review the system first and tell you what's possible.

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