AI Integration for Your Operations
AI is not a product you buy; it is a capability embedded into work that already exists. We embed it where it saves real time or supports a decision — and where it does not, we say so.
Where does AI genuinely help?
- Repeated questions consuming your team. Answers that already exist in your documents and policies, re-explained by hand dozens of times a day.
- Documents read only to extract data from them. Invoices, applications, contracts and forms keyed into a system by hand.
- A decision delayed because its information is scattered. The data exists, but across different systems, needing someone to gather and summarise it.
- A classification or routing step that depends on an employee’s judgement: assigning a request, setting a priority, triaging a case.
What we deliver
- An assistant that answers from your own knowledge (RAG): it builds its answer from your documents and systems, cites the source, and respects each user’s permissions — it shows nobody what they are not entitled to see.
- Data extraction from documents straight into the system, with human review where it matters.
- Automation of decision steps inside your process: classification, routing, summarisation and draft generation.
- Extending an existing system with AI capability without rebuilding it.
How we work
- We start from the case, not the model: what is the decision or task, and what does it cost today in time or errors?
- We examine the data and the permissions: where is the knowledge, and who is entitled to reach it? Governance is designed before launch, not after.
- A first usable release within 20 days on a narrow scope, with an agreed measure of success.
- Expand or stop based on measurement — not on impression.
Our work
- ACCBS (Arabian Grants) — we automated the full grants process from application to issuance, and extended the system with AI capability.
- Flowtech — we built the entire system and integrated its data sources, then extended it with AI.
- WonderBot — our own product: intelligent chat answering from company knowledge, on web and messaging channels.
- The Aljdwa platform — automated preparation of feasibility studies within a platform we built and support.
Frequently asked
Does our data leave the organisation?
That is settled in design before implementation: we agree where data is processed, what is sent and who can reach it, and build on that basis. We design permissions so that a user cannot see through the assistant what they cannot see in the system itself.
What if the model gets it wrong?
We design for the real case, not the demo: answers grounded in a source the user can open, human review at sensitive steps, and clear boundaries for what it answers and what it hands to a person.
Do we need to rebuild our systems first?
Not necessarily. Much of what we have delivered extended existing systems by integrating with them, rather than replacing them.
How do we know it worked?
We agree a measure before starting — time saved, cases completed automatically, an acceptable accuracy rate — and measure it on a narrow scope before expanding.
Tell us about your case
Send a short description of what you want done, and we reply with a first reading of the scope and the path — including saying plainly if the project is not worth building right now.