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Applied AI

AI that moves a number, not a demo.

LLM features, retrieval, agentic workflows, and computer vision built into products that already have users — with evals, guardrails, and bilingual Arabic and English behaviour.

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The approach

How we treat this work.

The failure mode with AI work is not that the model is bad. It is that a demo gets built, everyone is impressed, and nothing changes — because nobody agreed in advance which number was supposed to move, and there is no way to tell whether the thing got worse last Tuesday.

So we start from the metric: retention, conversion, operations cost, support load. Then we build the smallest feature that could move it, inside your existing product rather than beside it, and ship an eval harness with it so a regression is visible rather than a rumour.

Arabic matters here more than most people expect. Retrieval quality, tokenisation, and output register all behave differently in Arabic, and a pipeline tuned only on English will quietly underperform for half your users. We test both.

What you get

What actually lands.

  • LLM features inside your product

    Chat, copilots, summarisation, and structured extraction woven into the surface your users already use — connected to your data, not a separate toy.

  • Retrieval over your own documents

    Grounded answers with citations, semantic and hybrid search, and chunking tuned for Arabic as well as English.

  • Agentic workflows with an audit trail

    Agents that read from your systems and take actions, with every step logged and a human approval gate wherever the action is not reversible.

  • Computer vision

    Recognition, OCR, inspection, and live-video pipelines for retail shelf monitoring, heritage sites, and field operations.

  • Evals and guardrails

    Golden datasets, regression suites, prompt versioning, and content-safety layered on every model call — so quality is measured, not asserted.

  • Cost and latency you can live with

    Model selection, caching, and routing chosen against your actual traffic — the cheapest model that clears the bar, not the largest one available.

OpenAIAnthropicLangGraphPythonpgvectorPostgreSQLHugging FaceAzureAWS
Where we deliver

The markets we ship into.

  • Saudi Arabia and the Gulf — bilingual assistants and document retrieval where Arabic accuracy is the acceptance criterion, not a nice-to-have.
  • Egypt — operations and support automation for teams running at volume on tight margins.
  • UAE — enterprise knowledge and workflow automation.
  • United States — AI features inside consumer and B2B products.
Common questions

What gets asked before signing.

Where should we start with AI?
With a number you already track and would like to move — support tickets deflected, time to complete a task, conversion on a step. Not with a technology. If nobody can name the metric, the honest first engagement is a short discovery to find one, and sometimes the finding is that AI is not the cheapest fix.
Do you use our data to train models?
No. Your data is used to answer your queries and to build your evaluation sets, under your account and your retention rules. Where the requirement is that nothing leaves your infrastructure, we build against models you host — that constrains which models are available, and we set that expectation before design rather than after.
How do you handle hallucination?
By grounding answers in retrieved sources with citations the user can open, by constraining outputs to schemas where the downstream system needs structure, and by measuring. An eval harness with a golden dataset turns "it seems worse" into a number, which is the difference between fixing it and arguing about it.
Does it work as well in Arabic?
Not automatically, which is exactly why we test it separately. Retrieval chunking, tokenisation, dialect handling, and output register all behave differently in Arabic, and a pipeline tuned on English will underperform quietly. We build Arabic evaluation sets alongside English ones and report both.
What does it cost to run?
Model spend is usually smaller than people fear and less predictable than they would like, because it scales with usage rather than seats. We model it against your expected traffic during design, then reduce it with caching, routing to smaller models, and prompt trimming — and we show you the running cost rather than burying it.
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