Enterprise RAG & Knowledge Systems
RAG is how an LLM answers from your knowledge instead of its training data — but production RAG is a data engineering problem before it is a prompting one. Masarrati builds enterprise knowledge systems end to end: ingestion pipelines that handle messy real documents, chunking and embedding strategies tested against your queries, hybrid retrieval that combines semantic and keyword search, permission-aware filtering so users only retrieve what they may see, and evaluation that measures groundedness rather than fluency.
What is Enterprise RAG & Knowledge Systems?
Enterprise RAG development builds retrieval-augmented generation systems over an organisation's own documents — grounded, citation-backed answers with permission-aware retrieval. Masarrati engineers the full pipeline: ingestion and normalisation, hybrid retrieval tuned on real queries, tenant-safe access control, groundedness evaluation, and freshness sync, delivered as a platform your team owns.
Engineering Targets
Figures below are the benchmarks we design and test against on this type of build. They are targets, not a warranty — what your platform actually achieves depends on your data, scale and integration surface, and we agree the numbers that matter with you before work starts.
Why This Matters
Most RAG disappointments are retrieval failures wearing a generation costume: the right passage was never found, so the model improvised. Treating retrieval as a measured engineering discipline — with permissioning and freshness as first-class requirements — is what separates a knowledge system your teams trust from a demo they stop using after a week.
What You Get
Capabilities
Ingestion Pipelines
Parsers for PDFs, office documents, wikis and tickets, with table and layout handling, deduplication and metadata extraction that survives messy real-world files.
Retrieval Engineering
Chunking, embedding and hybrid search tuned against your actual query set, with rerankers where they earn their latency.
Permission-Aware Access
Retrieval filtered by the caller's entitlements at query time, so the system never surfaces a document the user could not open directly.
Grounded Generation
Answers constrained to retrieved evidence with inline citations, and honest refusal when the corpus does not contain the answer.
Evaluation & Monitoring
A scored question set measuring retrieval hit rate and answer groundedness on every change, plus drift monitoring in production.
Freshness & Sync
Incremental re-indexing from source systems so the knowledge base tracks reality instead of a snapshot from launch week.
Our Approach
How We Deliver
Corpus Audit
Inventory sources, formats, permissions and freshness needs before any indexing
Retrieval Baseline
Build and measure retrieval quality on your real queries before touching generation
Grounded Answers
Layer citation-constrained generation over proven retrieval, with refusal behaviour
Operate & Sync
Ship freshness pipelines, monitoring and evaluation, then hand over the platform
Real-World Applications
Use Cases
one grounded assistant across wikis, drives and ticket history with per-user permissions
agent-facing answers drawn from product docs and past resolutions, with citations
clause and policy retrieval across contract repositories with source links
protocol and guideline lookup grounded in the organisation's approved documents
internal copilot answering from architecture docs, runbooks and past incident reviews
Technology Stack
Explore More
Related Services
Artificial Intelligence
Agentic AI and machine learning solutions — autonomous AI agents, multi-agent systems, and enterprise AI automation that transform your business.
- Agentic AI development
- Multi-agent AI orchestration
- Autonomous AI agent systems
Generative AI Solutions
Custom LLM applications, RAG pipelines, and AI agents that understand your business context.
- LLM fine-tuning and prompt engineering
- RAG pipeline design and optimization
- Autonomous AI agent development
Computer Vision & Image AI
Visual intelligence systems for object detection, medical imaging, quality inspection, and document processing.
- Object detection and recognition
- Medical image analysis (X-ray, MRI, CT)
- Industrial quality inspection
Common Questions
Frequently Asked Questions
Why do RAG systems give wrong or made-up answers?
Usually because retrieval failed and generation improvised: the right passage was never found, chunking split the answer, or the index was stale. We treat retrieval as the primary engineering problem — measured on your real queries before generation is layered on — and constrain answers to cited evidence with refusal when the corpus is silent.
How do you handle document permissions in RAG?
Retrieval is filtered by the caller's entitlements at query time, mapped from your identity provider and source-system ACLs. A user can never retrieve — or have summarised — a document they could not open directly, and that guarantee is tested as part of the evaluation suite.
Can RAG work over our messy internal documents?
That is the expected input. The ingestion layer handles PDFs with tables, scanned files, wikis, tickets and exports — with parsing, deduplication and metadata extraction built for real corpora rather than clean demo files. Corpus quality issues we find are reported back, since they affect every downstream answer.
How does the knowledge base stay up to date?
Through incremental sync pipelines from the source systems — new and changed documents are re-indexed on a schedule you choose, deletions propagate, and freshness is monitored so the assistant tracks reality rather than a launch-week snapshot.
Industries We Serve
Related Industries
Information Technology
Enterprise IT solutions that drive efficiency, security, and digital innovation.
Healthcare
HIPAA-compliant digital solutions for modern healthcare delivery and patient care.
Islamic Fintech
Shariah-compliant financial technology for the modern Islamic economy.
Education
EdTech solutions that make learning accessible, engaging, and measurable.
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