TECHNOLOGY SERVICE · 01
AI & Automation
Assistants, document intelligence and process automation built against the systems you already run. This is for operations, finance and service teams drowning in documents, tickets and repeated lookups, and for the technology leaders who have to keep that work auditable.
Start where the data already is
Most useful AI work in an established organisation is retrieval, not generation. The first engagement step is a survey of where your content lives: the SharePoint sites, the ERP tables, the shared drive of PDFs nobody has indexed since 2019. That inventory decides what is buildable this quarter and what needs a data project first.
Candidate processes are then scored on three things: how often the task runs, what a wrong answer costs, and whether the source material is clean enough to retrieve. Anything that fails the third test goes back into the pipeline rather than into a prototype.
- Content and system inventory, with owners named
- Use-case scoring on volume, error cost and data readiness
- A shortlist of two or three processes worth automating first
Retrieval before fine-tuning
Fine-tuning is expensive, slow to correct and rarely the right first move. Grounding a general model in your own documents gets to a defensible answer faster, and lets you fix a bad response by fixing a document rather than retraining. Chunking strategy, embedding choice and a re-ranking pass do more for accuracy than model selection in nearly every case we have handled.
Where a task genuinely needs specialised behaviour, classification models and structured extraction usually beat a fine-tuned chat model on both cost and reliability.
Evaluation is the product
An assistant without an evaluation set is a demo. Before anything reaches users, our engineers build a graded question set from real queries supplied by your team, then score every change against it. That set lives in your repository and grows as edge cases appear.
Guardrails follow the same discipline. The system is told what it may not answer, when to say it does not know, and when to hand the conversation to a person, and every one of those events is logged.
- Graded evaluation set, versioned alongside the code
- Refusal and escalation policy agreed with your risk owner
- Full prompt, retrieval and response audit trail
Where the model runs, and where the data stays
Data residency is a live question for UAE banks, healthcare groups and government-facing bodies. Deployment can target your own cloud tenancy, including the AWS and Azure regions inside the UAE, with model access brokered through that tenancy so prompts and documents do not leave it. Open-weight models can be self-hosted where a regulator or an internal policy rules out third-party inference entirely.
What you get
- A written use-case assessment with each candidate process scored and ranked
- A working prototype running against your own documents, typically within four weeks
- Retrieval pipeline with chunking, embedding and re-ranking tuned to your corpus
- An evaluation set of graded question and answer pairs, held in your repository
- Guardrail configuration, refusal rules and human escalation paths
- Deployment into your cloud tenancy with SSO and role-based access
- Audit logging of every prompt, retrieved source and response
- Runbook plus a handover session for the team that will operate it
Typical outcomes
4 weeks
Kick-off to a prototype on your own data
40-60%
Handling time removed on document-heavy tasks
100%
Of responses logged with their sources
Stack we use
Questions
Yes. Both AWS and Azure operate regions inside the UAE, and we deploy the application, the vector store and the logs into your own tenancy there. Where policy forbids third-party inference altogether, open-weight models can run on your own infrastructure instead.
Responses are grounded in retrieved passages and cite the source document, so an operator can check them. The system is configured to refuse rather than guess when retrieval returns nothing relevant, and refusals are tracked as a metric.
Not for document-based assistants, which read the files where they already sit. Analytics-driven use cases such as forecasting do need a reliable pipeline first, and we will say so at assessment rather than after a failed pilot.
Kuyil AI is our own voice-first product for kiosks and public spaces, sold as a product with its own licensing. This service line is bespoke engineering against your systems; the two share techniques but are bought separately.
Related
Next step
Start with a 20-minute call.
Tell us the roles you need filled, the system you need built, or both. You will speak to someone who has done the work, and leave the call with a route forward.