AI chatbots, agents and automation that do real work.

The models, APIs and cloud platforms are the same wherever the team sits, so what you are paying for is engineering time. Most of that time goes into evaluation sets, edge cases and integration, and a lower hourly rate means you can afford to do those properly. We also tell you when a simple rule or ordinary software would do the job better than AI.

It depends mostly on how many conversations it handles and how long they are. You pay the model provider per use, plus hosting for the app and any search index, and a retainer only if you want us to maintain it. We estimate the monthly usage cost from your expected volume in the proposal, and set spending limits on the provider account so a busy month can’t surprise you.

It depends on the use case, the integrations and how much human review is needed. A prototype on your data costs far less than a production system connected to several tools. We send a fixed written quote after a short call, along with an estimate of the monthly model usage you will pay the provider directly.

A prototype tested on your real examples typically takes two to four weeks. A production system with integrations, review steps and monitoring typically takes six to twelve weeks, depending on scope.

We use API settings that don’t train on your data, keep documents and vector stores in your own cloud account, and can host them in a region near your users, such as the EU, the US, the Middle East or India. We design data handling with GDPR and similar laws in mind, but we are not lawyers, so your legal adviser should confirm what your case requires.

Yes, with one caveat. Current models handle Arabic, Hindi and major European languages well enough for many business conversations, and we build right-to-left chat interfaces. Our team doesn’t write Arabic, so a native speaker on your side reviews the Arabic examples in the evaluation set and the answers before launch.

It will sometimes, because language models can produce wrong answers that sound confident. Answers are grounded in your own data with sources shown, low-confidence cases go to a person, and anything that changes data or reaches a customer can wait for approval. When a mistake does get through, we add it to the evaluation set, fix the cause and re-test before the fix goes live.

Usually not. Most business tasks work well with a hosted model such as GPT, Claude or Gemini, given your documents through retrieval and clear instructions. Fine-tuning or an open model on your own servers makes sense for very high volumes, strict privacy rules or a narrow task, and we compare the cost and accuracy on your examples before recommending it.

You do, along with the code and data. Model provider accounts are opened in your name, and we hand over documentation so another team could maintain the system.

Yes, on a monthly retainer if you want one. That covers monitoring quality, re-running evaluations when providers update their models, fixing new edge cases and adding use cases.