AI integrations
Agents and retrieval wired into real workflows — with an evaluation harness from day one, because a feature you cannot measure is a feature you cannot defend.
Evals first, or do not start
The failure mode of AI features is specific and predictable: the demo is wonderful, the feature ships, and nobody can say whether it is working. Quality becomes a matter of opinion, regressions are invisible, and every prompt change is a gamble nobody can price.
So we build the evaluation harness before the feature. A reference set drawn from your real cases, a rubric for what a good answer is in your domain — which is a domain question, not a machine learning one — and a scored run you can repeat. After that, changing the prompt or the model is an experiment with a result instead of a vibe shift.
This is also what makes the work defensible to whoever signs off on it. "It seems better" does not survive a board meeting. A scored comparison does.
Retrieval is mostly not a model problem
Most retrieval-augmented features fail at the retrieval end, not the generation end. The model is asked a reasonable question, handed the wrong five passages, and produces a fluent answer built on them. From the outside that looks like hallucination; it is a search problem wearing a costume.
So the work concentrates there: how documents are split, what gets indexed, how queries are expanded, and how results are reranked before anything reaches a model. Arabic content adds its own wrinkles — orthographic variation, diacritics, and a tokenisation story that is simply worse-served than English by most off-the-shelf defaults. That is worth testing on your corpus rather than assuming.
Knowing when not to answer
A system that always produces an answer will confidently produce wrong ones. The valuable behaviour is the opposite: recognising a question it should not attempt and handing it to a human, a deterministic path, or a plain "I do not know".
That boundary is a product decision, not a technical one, and it is worth making explicitly. The cost of a wrong answer is not the same in a support assistant as it is in anything touching money or a legal obligation — and the fallback should reflect that.
Everything goes to production with logging that lets you see what it did and why. An AI feature you cannot inspect after the fact is one you cannot improve.
What gets delivered
- An evaluation harness with a reference set from your real cases and a domain rubric.
- The retrieval layer — chunking, indexing, query handling and reranking — tested against your corpus, Arabic included.
- The agent or feature itself, with explicit boundaries on what it will not attempt.
- Graceful fallback paths for the cases it declines.
- Production logging and a scored baseline, so the next change can be compared against this one.
How we work
The studio builds its own products and runs them, which is where the insistence on measurement comes from. The same principle applies elsewhere on this site: techiato.org and the Omaraty site both publish machine-readable catalogs and plain-text summaries for language models, because being citable by an assistant is a channel worth engineering for rather than hoping about.
Common questions
Can you work with our existing model provider?
Yes. The evaluation harness is deliberately provider-agnostic, which is most of its value — it lets you compare providers on your own cases instead of on someone else's benchmark.
How do you handle Arabic?
As a case to test rather than a box to tick. Retrieval quality in Arabic is genuinely harder than in English and the defaults are weaker, so it gets its own slice of the reference set and its own scores.
What if the evals say the feature is not good enough?
Then you find out in week three rather than after launch, and you get to decide whether to narrow the scope, change the approach, or stop. That is the harness doing exactly what it is for.
Start with a brief
Tell us what you're building. We send back a one-page response with scope, timeline, and a number you can plan around.
Send us a brief