AI Integration & LLM Apps in Pakistan

What this covers

AI search, assistants and automation built for a specific job in your product, and tested on real examples.

Tools

LLM, RAG, OpenAI, Claude. The exact stack depends on what you already use.

Where it helps

One clear use case, with examples of good and bad answers.
Screens that handle failures, with a person checking results where needed.
Test results to compare against, and an idea of the running cost.

We agree what “done” means before work starts.

What you get

Use-case and data-access review
A small prototype using approved data
Model or retrieval integration with loading and error states
Evaluation examples, cost considerations, and handover notes

The final list depends on the agreed scope. The estimate and schedule follow from it.

Data and security

We agree on login and access rules, how long data is kept, where keys are stored, and who handles releases. Early reviews use sample data, not real records.

How I work on it

Discovery, then scope and what “done” means, then design, build, testing, release prep and any agreed support.

What helps me get started

The task to improve and examples of current inputs and outputs
Approved data sources and access constraints
Accuracy expectations, review needs, and usage estimates

A short outline is enough for a first call. Please send sample data, not real records or passwords.

Related services

Read the planning guide

Questions

Can we test the idea before a full build?

Yes. A small prototype can check the task, the data, how we’ll judge answers and the likely cost before a bigger build.

Will the assistant always be right?

No. We decide how to test it, what happens when it isn’t sure, and who reviews its answers. I won’t promise perfect accuracy.

AI features that do one job well.