PandoratechAI assistants that answer from your documents, not from memory
A RAG assistant retrieves passages from your policies, ERP records and document stores before writing a word — so answers cite sources instead of inventing them. We build bilingual assistants for UAE businesses, hosted in UAE regions or on private endpoints and evaluated before launch.
A retrieval-augmented generation (RAG) assistant answers questions by first retrieving relevant passages from a company’s own documents, databases and ERP records, then generating a reply grounded in those sources with citations. Pandoratech builds RAG assistants in English and Arabic for UAE businesses, with permission-aware retrieval and continuous accuracy evaluation.
Content reviewed:
- 5
- Pipeline stages from ingestion to evaluation
- EN/AR
- Assistants fluent in English and Arabic
- UAE
- Hosting in UAE regions or private endpoints
- 6
- Connector families for your data sources
Under the hood
The five-stage RAG pipeline we build and operate
Reliable answers are a pipeline property, not a model property — each stage is engineered and measurable.
- 01
Ingest & permission-trim
Documents are pulled with access rules attached — users only get answers from content they may see.
- 02
Chunk & index
Content is split into meaningful passages — a clause, a table, a policy section — indexed for semantic and keyword search, English and Arabic.
- 03
Retrieve
Each question is matched against the index to find the passages that answer it — ranked, filtered and permission-checked.
- 04
Generate with citations
The model writes the answer from retrieved passages only, linking every claim to its source for one-click verification.
- 05
Feedback & evaluation
User ratings, corrections and a standing test set feed a continuous evaluation loop — accuracy is measured, not assumed.
See it work
One question, three sources, zero guesswork
Illustrative: the assistant answers a policy question and shows the exact sources behind every claim.
Your team asks
What is our return policy for B2B orders above AED 10,000?
The assistant answers
B2B orders above AED 10,000 can be returned within 14 days if unused and in original packaging (Return policy v3.2, §4). Later returns need sales-manager approval and a 10% restocking fee. Order SO-2841, delivered 28 September, is inside the window.
Data sources
Six connector families, one index
The assistant is only as good as what it can reach — connected to the systems your knowledge lives in, permissions intact.
Odoo & ERP records
Products, prices, stock, orders, invoices and customer history — read with the asker’s permissions.
PDFs & policy documents
Contracts, handbooks, SOPs and policy PDFs — versioned, so the assistant cites the current policy.
SharePoint & Google Drive
Shared drives indexed with folder permissions intact — including OCRed Arabic scans.
Websites & intranets
Your public site, partner portals and internal wiki, kept fresh on a schedule.
Email archives
Selected mailboxes searchable for commitments, quotes and decisions — mailbox-level access control.
Databases & data warehouses
SQL sources and warehouses queried through governed views — the assistant sees numbers, not raw tables.
The evaluation harness that ships with every deployment
Every deployment ships with an evaluation harness built from your real questions — the metrics it tracks in production.
| Metric | What it measures | How we verify |
|---|---|---|
| Answer accuracy | Does the answer match the source documents? | Human-reviewed test set of real questions, rerun after every change |
| Citation coverage | Are all factual claims linked to a passage? | Automated checks that every claim resolves to a retrieved source |
| Hallucination rate | How often does it state something unsupported? | Adversarial question sets plus sampling of production answers |
| Response latency | How long an answer takes end to end | Percentile timings monitored on production dashboards |
| Arabic answer quality | Fluency and correctness of Arabic replies | Native-speaker review of Arabic test sets and Gulf business terminology |
Targets are set per deployment during the evaluation phase — the right bar depends on the use case and the cost of a wrong answer.
Frequently asked questions
How good is the Arabic? Our documents are bilingual.
Arabic is a first-class target, not a translation toggle. Retrieval works across Arabic and English sources, answers come in the user’s language, and native speakers review Arabic output against Gulf business terminology.
Does our data get used to train public models?
No. Assistants run on contractual no-training terms and private or UAE-region endpoints. Your documents are indexed for retrieval inside your own environment; nothing you connect trains any foundation model.
How do you stop the assistant from making things up?
Three layers: answers are generated only from retrieved passages; every claim must cite its source or the answer is held back; and a standing evaluation set measures hallucination on your real questions.
Which foundation models do you use?
Assessed per case. The choice depends on Arabic capability, latency, cost and hosting that meets your residency requirements — including fully private options. We benchmark candidates on your questions first.
Can this run on-premise or fully private?
Yes. Retrieval always runs in your environment; for generation we offer UAE-region managed endpoints, private deployments, or fully on-premise open-weight models where policy demands it. Trade-offs are mapped during discovery.
Who can see what through the assistant?
Exactly what they can already see. Retrieval is permission-trimmed: documents carry their access rules into the index, and the assistant answers only from content the user may read. Finance documents stay invisible to non-finance users.
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