RAG & Knowledge Systems
Your employees should not have to search through folders, SharePoint sites, PDFs, manuals, wikis, and internal systems just to find one answer.
Relytic designs, builds, evaluates, and deploys RAG and enterprise knowledge systems that make large collections of company information searchable through natural language — with the relevant evidence behind the answer.
The problem
When company knowledge becomes a problem
As organizations grow, useful information gets scattered across systems, formats, languages, and teams.
The result is familiar:
- Employees spend too long searching for information
- The same questions are answered repeatedly
- Important knowledge is buried inside long documents
- Different teams work from different versions of the truth
- Generic AI assistants return plausible answers without enough evidence
- Search quality drops when documents contain tables, scans, diagrams, or multiple languages
A good knowledge system should do more than generate fluent text. It should retrieve the right information, preserve context, show where answers came from, and behave predictably when the information is missing.
What we build
Enterprise knowledge assistants
Give employees one place to ask questions across internal knowledge and receive concise answers with supporting sources.
Enterprise search
Improve how people find documents, passages, policies, procedures, technical information, and other internal knowledge.
Multilingual RAG
Search and answer across knowledge bases containing multiple languages without forcing everything through a single-language workflow.
Multimodal document RAG
Make information inside PDFs, scans, tables, charts, diagrams, and other visually rich documents available to the retrieval system.
Knowledge systems connected to your existing tools
Integrate with the systems where your information already lives, including SharePoint, Microsoft 365, Google Drive, Confluence, document repositories, databases, and internal APIs where appropriate.
Controlled and permission-aware retrieval
Design systems so users only retrieve information they are authorized to access and important answers remain traceable to their source material.
System anatomy
We build the complete RAG system
A reliable RAG application depends on much more than choosing an embedding model and connecting it to an LLM. Depending on the project, the system may include:
- Ingestion and synchronization from business data sources
- OCR and document parsing
- Chunking and document structuring
- Metadata and permission handling
- Embedding and indexing
- Semantic, keyword, or hybrid retrieval
- Query rewriting and expansion
- Reranking
- Multimodal document understanding
- Answer generation
- Source citations
- Abstention when evidence is insufficient
- Monitoring and regression testing
- Cost and latency optimization
The architecture is chosen around the workflow, data, scale, security requirements, and expected user behavior rather than forcing every project into the same stack.
Measurement
Reliability is part of the development cycle
Evaluation is not a report we add after the system has already been built.
We use it throughout development:
- Retrieval
- We test whether the system actually finds the information required to answer the question using metrics such as Hit@K, Recall@K, MRR, and nDCG where appropriate.
- Answer quality
- We evaluate whether generated answers are correct, complete, grounded in the retrieved evidence, and supported by accurate citations.
- Difficult cases
- We deliberately test the situations most likely to break the system: ambiguous questions, missing information, conflicting documents, complex tables, visual information, multilingual queries, and long documents.
- Production constraints
- Accuracy is only part of the problem. We also evaluate latency, cost, processing failures, scalability, and operational complexity.
The goal is not to produce a benchmark for its own sake. The measurements tell us what to improve next.
In practice
What this looks like in practice
For a multilingual enterprise RAG system, we do not stop at asking whether the chatbot appears to give good answers.
We investigate questions such as:
- 01Were the correct documents retrieved?
- 02Did reranking improve the ordering of the evidence?
- 03Does performance remain strong across languages?
- 04Are tables, charts, and diagrams represented correctly?
- 05Is each important claim supported by the source?
- 06Are citations pointing to the right evidence?
- 07What happens when the answer is not present in the knowledge base?
- 08Which configuration provides the right accuracy, latency, and cost trade-off?
This turns RAG development from trial-and-error into an engineering process.
Document intelligence inside the RAG pipeline
Work has included evaluating Azure Document Intelligence, Docling, GLM-OCR, and multimodal document-processing approaches on representative enterprise documents, including complex tables, charts, and diagrams.
Tools evaluated
- Azure Document Intelligence
- Docling
- GLM-OCR
- Multimodal document processing
Built for real enterprise scale
- Hundreds of thousands
- of documents
- 7
- languages
- Thousands
- of daily queries
Enterprise RAG experience with knowledge bases containing hundreds of thousands of documents.
English, Arabic, Chinese, Italian, Portuguese, German, and Spanish in the same enterprise environment.
A production system used internally by hundreds of employees and handling thousands of queries per day.
Common use cases
Internal knowledge assistant
Help employees find policies, procedures, technical information, internal guidance, and company knowledge without manually searching across multiple repositories.
Technical-document search
Search large collections of manuals, specifications, reports, and engineering documents while preserving the original source context.
Policy and compliance Q&A
Retrieve answers from controlled document collections where citations, versioning, and source traceability matter.
Customer-support knowledge systems
Give support teams faster access to product documentation, troubleshooting information, and internal knowledge.
Research and expert knowledge assistants
Help teams synthesize information across large collections of reports and specialized documents while keeping the evidence visible.
Honest advice
When RAG is — and is not — the right solution
RAG is useful when the answer depends on information that lives outside the model and needs to stay current, private, traceable, or organization-specific.
It is not automatically the right architecture for every AI problem. Some workflows are better solved with structured search, deterministic software, document extraction, an agentic workflow, model fine-tuning, or no AI at all.
We start with the problem and choose the architecture after understanding the data and workflow.
How we work
From problem to production
- 01
Understand
We map the users, questions, data sources, permissions, document types, scale, and business constraints.
- 02
Define success
We establish representative evaluation cases and decide what good performance means before optimizing the system.
- 03
Build and evaluate
We develop the ingestion, retrieval, reranking, generation, and document-processing components iteratively and measure how each change affects the system.
- 04
Integrate and deploy
We connect the system to the required data sources and business environment and prepare it for real users.
- 05
Monitor and improve
Where appropriate, we track production behavior, failures, quality regressions, latency, and cost as usage grows.
- 01
Understand
We map the users, questions, data sources, permissions, document types, scale, and business constraints.
- 02
Define success
We establish representative evaluation cases and decide what good performance means before optimizing the system.
- 03
Build and evaluate
We develop the ingestion, retrieval, reranking, generation, and document-processing components iteratively and measure how each change affects the system.
- 04
Integrate and deploy
We connect the system to the required data sources and business environment and prepare it for real users.
- 05
Monitor and improve
Where appropriate, we track production behavior, failures, quality regressions, latency, and cost as usage grows.
Next step
Have company knowledge that is difficult to use?
Book a 30-minute conversation with Relytic to discuss your documents, users, current search workflow, and what a reliable knowledge system would require.