We consolidate fragmented knowledge from documents and systems, then clean, chunk, and index it into high-quality vector stores. Chunking and embedding strategies are tuned to your Arabic and English content to maximise retrieval accuracy. Your knowledge becomes a reliable foundation for any downstream assistant or semantic search engine.
What's included
- Consolidate fragmented knowledge from documents and systems across formats such as PDF, Office files, pages, and databases.
- Clean content and extract text and structure from complex files, tables, and scanned images where needed via OCR.
- Select a chunking strategy suited to the content that preserves context and semantic boundaries.
- Generate embeddings with models appropriate for Arabic and English and build the vector index in a vector database.
- Enrich each chunk with metadata such as source, date, department, and permission to support filtering and precise retrieval.
- Establish a refresh and re-indexing mechanism that keeps the index synchronised with changing sources.
Methodology & standards
Source audit: catalogue documents, formats, volumes, and quality, and prioritise ingestion.
Ingestion and processing: extract, clean, normalise, and de-duplicate text, and fix Arabic encoding.
Chunking and embedding: trial chunking strategies and chunk sizes, and select the best embedding model by measurement.
Indexing and enrichment: build the vector index and attach metadata and permission controls.
Evaluation and handover: measure retrieval quality on reference queries and document the refresh and operations mechanism.
Deliverables
- A ready, semantically searchable vector knowledge base connected to a vector database.
- A documented, re-runnable ingestion and processing pipeline for new sources.
- The metadata schema and per-chunk permission controls.
- A report on the chosen chunking strategy and embedding model with measurement-based justification.
- A retrieval-quality report against reference queries.
- An operations guide and a documented re-indexing and periodic-refresh mechanism.
Regulatory controls it satisfies
Typical timeline
Delivery depends on the volume and variety of sources and typically ranges from two to six weeks, from audit to a ready index.
Common questions
Why does chunking matter so much for Arabic content?
Poor chunking cuts a sentence or idea in half, weakening retrieval and confusing grounding. Arabic adds challenges around diacritics, direction, and encoding variation, so we test different strategies and sizes and measure their effect on retrieval accuracy before committing.
How do we keep the index current as our documents change?
We deliver a re-runnable pipeline with a refresh mechanism that detects new and modified sources and re-indexes them without a full rebuild, on an update cycle matched to how often your content changes.
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