Retrieval systems (RAG)
Assistants grounded in your own documents, policies and records — with source citations, permission-aware retrieval so people only see what they are entitled to, and honest answers when the corpus does not contain one.
Services / AI
AI integration that ships inside real products.
Most organisations do not need an AI strategy. They need one workflow to stop taking three days. We embed models, agents, retrieval and automation directly into the software people already use — where the value is measurable, the behaviour is bounded, and a human stays in the loop wherever the stakes require it.
A demo is easy; a dependable feature is not. The difference is engineering: grounding answers in your own documents, evaluating outputs against real cases before release, handling the moments the model is wrong, controlling cost per request, and keeping sensitive data where policy says it must stay. That work is the actual project, and it is the part we specialise in.
Every engagement is scoped to the project — contact us for a tailored quote.
- engineers
- 20+
- countries
- 7
- disciplines
- 8
- platform uptime
- 99.9%
01
What we build
AI features with a job to do, not a chatbot bolted onto a homepage.
Agents and workflow automation
Bounded agents that carry out defined multi-step tasks — triage, extraction, drafting, routing, reconciliation — with tool access scoped tightly and an audit trail for every action taken.
Document and form intelligence
Extraction and classification across contracts, invoices, submissions and scanned records, in Arabic and English, with confidence thresholds that escalate to a person instead of guessing.
Search that understands intent
Semantic and hybrid search across large internal archives, so staff find the right clause or record by describing it rather than by remembering its filename.
AI inside existing products
Summaries, suggestions, drafting and anomaly detection added to software you already run, designed to fit the interface people know instead of forcing a new one.
Evaluation and guardrails
Test sets built from your real cases, output evaluation before every release, prompt and cost controls, rate limiting, logging and fallbacks — so behaviour is measured, not assumed.
02
How we work
We start from the workflow, never from the model.
- Discovery
Find the workflow worth automating
We look for tasks that are frequent, rule-heavy, language-based and currently slow. You get a shortlist with an expected effect, a data-readiness assessment, and an honest note on which ideas are not worth doing yet.
- Design
Decide where the human stays
We design the interaction, the confidence thresholds and the escalation path first — what the system does alone, what it proposes for approval, and what it must never touch.
- Build
Ground it, then evaluate it
Retrieval over your data, tightly scoped tool access, and an evaluation set drawn from real cases. We measure quality before shipping, not after a complaint.
- Deploy
Release behind controls
Staged rollout to a small group first, logging and cost monitoring live from day one, and clear controls for switching a capability off without redeploying the product.
And after
Monitoring, maintenance, steady iteration — we stay. Models change quickly, and a feature that was excellent last quarter can quietly regress. We keep evaluations running, retune retrieval as your content grows, and move you to better or cheaper models when they prove out.
03
The stack
Model-agnostic by design — we are not selling you a vendor.
- LLMs
- Agents
- RAG
- Vector search
- Embeddings
- Python
- TypeScript
- Evals
- Streaming APIs
- Cloud / private hosting
We work across major model providers and open-weight models, and choose per use case on quality, latency, cost and where the data is allowed to go. Security is a practice: encryption in transit and at rest, least-privilege access, a secure development lifecycle, in-house penetration testing and monitoring, and UAE data-residency options — including private or in-region deployment where prompts must never leave the country.
04
Built for
Where AI integration earns its place.
Government entities
Assistants over public information, Arabic-language document processing, and service triage — designed with residency, auditability and human oversight as first-order requirements.
Enterprises
Knowledge trapped in years of documents, and repetitive language work spread across departments — the two places where retrieval and automation pay for themselves quickly.
Product teams
Teams adding intelligence to an existing product who need it to be reliable, affordable per user, and explainable to their own customers.
Founders and private clients
AI-native products where the model is the core of the offering, and the engineering around it decides whether it survives contact with real users.
05
Questions we get asked
Direct answers on a noisy subject.
What does AI integration actually involve?
Connecting models to your data and your workflow: retrieval over your own documents, tool access, evaluation against real cases, guardrails, logging and cost control — then embedding the result in software people already use.
Will our data be used to train someone else’s model?
Not in the setups we build. We use configurations and providers that exclude your content from training, and where policy requires it we deploy in-region or privately so data does not leave your environment.
What is RAG, and do we need it?
Retrieval-augmented generation grounds a model’s answers in your own documents rather than its general training. You need it whenever answers must be accurate, current and traceable to a source your organisation controls.
Does AI work well in Arabic?
Modern models handle Modern Standard Arabic well and dialects less consistently. We evaluate on your actual Arabic content before committing to an approach, and design fallbacks for the cases where confidence is low.
How do you keep AI costs predictable?
By sizing the model to the task, caching aggressively, limiting context, monitoring cost per request from day one, and setting rate limits — so spend scales with usage in a way you can forecast.
Services / AI
Start a project
Tell us the task that eats your team’s week. We will tell you plainly whether a model helps, and what it would take to do it properly.
Every engagement is scoped to the project — contact us for a tailored quote.
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