The problem
The answer usually exists somewhere: in a policy, a contract, a shared drive or an old email thread. Finding it takes too long, a few experienced people become the bottleneck, and staff re-key the same information from documents into systems. Generic AI tools can’t help safely because they don’t know your sources or respect who is allowed to see what.
What is retrieval-augmented generation (RAG)?
RAG lets an AI system answer from your own documents. It searches approved sources for relevant passages, then writes an answer grounded in them, ideally with a citation, instead of relying only on what the model learned in training.
It is the foundation of most useful enterprise assistants and of the agents we run through Generative Agents as a Service: staff ask in plain Arabic or English and get an answer with a link to the source, filtered by what they are permitted to see.
What’s included
Enterprise knowledge search
One place to search policies, procedures, contracts and records, with results that respect access rights.
RAG assistants and staff copilots
Assistants that answer from approved sources with citations, and say so when they don’t know. Customer-facing versions become AI agents on your website and WhatsApp.
Document intelligence
Extracting and checking data from invoices, applications, forms and scanned documents, with human review where it matters.
Workflow automation
Connecting AI steps to your ERP, CRM and other systems through APIs and integrations, so work moves without re-keying.
Security and governance
Permission filtering, data residency decisions, audit logs and clear ownership for every source.
Use cases
Professional services
Search across past proposals, contracts and expertise so knowledge doesn’t sit with a few people.
Healthcare administration
Internal policy and procedure search for staff. Clinical decisions always stay with clinicians.
Finance and operations
Invoice, claim and application processing with extraction, validation and exception routing.
Hospitality and real estate
Staff assistants for standard operating procedures, leases and property information.
How we build
1. Map the information
Identify source owners, formats, freshness, user roles and access restrictions.
2. Prototype retrieval
Test whether representative questions retrieve the correct approved passages before writing any answers.
3. Connect the workflow
Integrate the assistant or document process with one bounded business task.
4. Prepare operations
Define monitoring, incident handling, source updates, cost controls and human review.
How we measure
We evaluate each part of the system separately, so problems are easy to find and fix.
- Retrieval relevance
- Answer support
- Citation accuracy
- Access-control failures
- Extraction errors
- Review effort
For document workflows we measure on representative samples of your real documents, not demo files.
Start with the Enterprise AI Prototype
A fixed-scope first step that tests one knowledge or document workflow on your own data.
- One workflow tested on agreed sample data
- Retrieval and answer-quality results
- A security and permissions review
- A production-readiness gap assessment
What we won’t promise
RAG does not eliminate incorrect answers. Permission filtering, source maintenance, the ability to say “I don’t know” and human review are design requirements, not extras. A prototype is not evidence of production reliability, so we test the gap before you roll out.
Questions
Is our data safe with an enterprise AI system?
Security is designed in from the start. We work from approved sources only, apply each user’s existing access permissions to what the assistant can retrieve, and agree where data is processed and stored, including UAE or regional hosting where it is required.
Does RAG stop AI from making things up?
It reduces the risk but does not remove it. Grounding answers in approved documents, showing citations, letting the assistant say it doesn’t know, and human review for important decisions are all part of the design.
Can it work with Arabic documents?
Yes. We test retrieval and answers on representative Arabic and English documents, including scanned files, and measure quality separately in each language.
Which processes are best to automate first?
Ones that are frequent, rule-heavy and document-based, with a clear owner and a measurable cost today, such as invoice or application processing, policy questions and report preparation. Our AI Readiness Sprint ranks your candidates if you are unsure.