AI and ML Integration

Yes. That is the common case: we add LLM-powered features into a product you already have, connect them to your data through retrieval, and keep the integration flexible so you can change model or provider later without a rewrite.

Usually retrieval-augmented generation, because it grounds a strong existing model in your own data without the cost of training. Fine-tuning makes sense for a fixed style or a narrow task with good examples. Often the right answer is RAG first, fine-tuning only if it is still needed.

We ground answers in your data through retrieval, constrain the model with guardrails, and measure accuracy with evaluations. That reduces confident errors sharply, and we are honest that it does not remove them entirely.

We build an evaluation harness for the behaviour that matters, so you can see how often the system is right, where it fails, and whether a prompt or model change actually helped. Running it continuously catches quality drift before your users do.

We choose the model that fits your quality, cost, privacy, and latency needs, and keep the integration flexible so switching providers later is a contained change.

Yes. Source, infrastructure configuration and documentation are pushed to your repositories from day one, and the cloud accounts stay in your name. If we part ways, you keep everything and nothing stops working.

On scope. A one-to-two-week discovery produces an itemised estimate for a defined first release, so you see the full cost before committing to the build.

A focused v1 usually takes three to six months, shipped in two-week increments you can test as they land. A clickable prototype typically exists within fourteen days of the brief.

Yes. Our team works on GMT+6, with at least six hours of daily overlap with the Gulf, Europe, the UK, the US and Australia, and demos are recorded for anyone who missed the call.

We keep watching the metrics, respond to incidents and ship small improvements under a maintenance agreement, for as long as you want us. Runbooks and monitoring are in place from day one, so nothing depends on memory.