AI Engineering
AI features inside real products—assistants, search and document understanding over your own data, and automation of the work your team currently does by hand.
What we build
AI features that live inside a product rather than beside it: assistants that can act on your data, retrieval and search over your own documents, extraction from invoices and forms, classification and routing, and automation of the steps a person currently does by hand.
We start from the task, not the model. If a rules engine, a better search index, or a clearer form solves the problem, we will say so — that answer is cheaper for you to run and easier for us to support.
How we make it reliable
AI features fail differently from ordinary code: they degrade quietly instead of throwing errors. So every feature ships with an evaluation set built from your real cases, so a prompt or model change can be measured rather than guessed at.
We design for the failure modes explicitly — grounding answers in retrieved sources, showing citations where trust matters, keeping a human approval step for consequential actions, and logging inputs and outputs so a bad answer can be traced.
Cost and latency are treated as product constraints from the start: model choice, caching, batching and fallbacks are decided against the budget you actually have, not the best-case demo.
Data, privacy and ownership
Your data stays yours. We work in your cloud accounts and provider accounts, keep keys server-side, and document exactly what is sent to which provider and what is retained.
For Arabic-language products we test in Arabic. Retrieval quality, tokenisation and output tone all behave differently in Arabic than in English, and that is a testing problem, not an afterthought.
Capabilities
- Assistants and chat interfaces grounded in your own content
- Retrieval-augmented search over documents, tickets and knowledge bases
- Document understanding: extraction, classification and routing
- Workflow automation with human approval where the stakes require it
- Model and provider integration, with fallbacks and cost controls
- Evaluation sets, regression checks and output logging
- Arabic and English behaviour tested separately
- Agent-accessible APIs (including MCP servers) for internal tooling
Related guides
If you're researching options, these guides cover common decisions and trade-offs.
FAQs
Do we need our own model?
Almost certainly not. Most business problems are solved with a hosted model plus your own data and a well-designed retrieval layer. Training or fine-tuning a model is worth discussing only once a hosted model has been shown to fall short on a measured evaluation.
How do you stop it from inventing answers?
By grounding answers in retrieved source material, showing citations where trust matters, constraining outputs to a schema when the result feeds another system, and keeping a human approval step for anything consequential. We also keep an evaluation set from your real cases so accuracy is measured, not assumed.
Where does our data go?
Only where you agree it goes. We document which provider processes what, keep credentials server-side, and can restrict processing to a specific region or provider if your compliance requirements demand it.
Does it work in Arabic?
Yes, and we test it in Arabic rather than assuming English results carry over. Retrieval quality and output tone in Arabic need their own evaluation, which is part of the work.
What does an AI feature cost to run?
It depends on the model, the volume, and how much context each call needs — which is why we estimate per-request cost during scoping and design caching and model fallbacks against your budget before building.
Tell us about your timeline, stack, and risks—we'll respond from matrixmindsit@gmail.com.
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