Capability
Knowledge & RAG Systems
Enterprise knowledge systems that answer questions from your own policies, procedures and documents, with citations to the source. Retrieval respects existing permissions and works across Arabic and English content.
The quality of a knowledge assistant is decided long before the model answers: in how documents are prepared, split, indexed, secured and evaluated.
01Architecture
Reference architecture
A typical arrangement of the components. Every implementation is adapted to your systems, data-residency and security requirements.
Sources
- File shares and intranet
- Document management systems
Preparation
- Cleaning and chunkingPython
- Metadata and permissions
Retrieval
- Hybrid searchpgvector · Qdrant
- Re-ranking
Answering
- Language modelOpenAI · Anthropic
- Answer checks and citations
02Components
What this capability covers
01
Document ingestion
Connectors for file shares, intranets and document systems, with cleaning, chunking and metadata that preserve structure.
02
Bilingual retrieval
Hybrid keyword and semantic search tuned for Arabic and English, including mixed-language documents.
03
Permission-aware answers
Users only receive answers drawn from documents they are allowed to see.
04
Citations and evaluation
Every answer links to its sources, and answer quality is measured against a curated question set.
03Implementation
How we implement it
Before building the index, we assemble a question set with your subject-matter experts and use it to measure retrieval and answer quality throughout. Chunking and metadata follow the structure of your documents, which matters more than the choice of model.
- Hybrid retrieval combines keyword and semantic search for Arabic and English.
- Access control is enforced at retrieval time, not only in the interface.
- When the sources are insufficient, the assistant says so instead of guessing.
04Deliverables
What you receive
Curated evaluation question set
Ingestion and indexing pipeline
Permission-aware retrieval service
Assistant interface or API
Technology
Technologies we work with
- LlamaIndex
- pgvector
- Qdrant
- Postgres
- OpenAI
- Anthropic
- FastAPI
- Redis
Chosen per project and aligned with your existing standards. Listed as technologies we use, not as partnerships or endorsements.
05Use cases
Typical applications
Patterns this capability is designed for, not a list of delivered projects.
01
Policy and procedure assistant
Employees ask in their own words and receive answers with links to the governing document.
02
Contract and tender search
Legal and procurement teams find clauses and obligations across large document sets.
03
Technical documentation
Engineers and operators query manuals and maintenance records while on the job.
06Related
Where to go next
More in Intelligent Systems & Automation
- AI Systems Engineering
Computer vision, natural language, deep learning, multi-modal and predictive systems engineered for real-world deployment, not demonstrations.
- Generative AI
Custom AI assistants, document intelligence, content generation and enterprise copilots, grounded in your own data.
- Enterprise AI Agents
AI agents that plan and complete multi-step tasks across your systems: gathering information, preparing work and taking actions through approved tools.
- Document Intelligence
Arabic and English documents — forms, invoices, contracts, certificates and correspondence — turned into validated, structured data.
- Intelligent Automation
RPA, workflow automation, decision engines and intelligent document processing for operational efficiency.
- AI Customer Experience
Smart chat and voice assistants, conversation analytics and agent-assist tools for customer service.
Next step
Scope Knowledge & RAG Systems for your organisation
Share your process, data and constraints. We will propose an approach, the evidence to collect first and a realistic plan.