Enterprise RAG Development — A Private AI Assistant Over Your Documents
Enterprise RAG (retrieval-augmented generation) is a system that lets employees ask questions in natural language and get answers grounded in your own documents — with citations, security and access control. Perimattic deploys it as a fixed-scope, enterprise-grade build in 4–6 weeks.
Overview
What is KnowledgeRAG?
Retrieval-augmented generation (RAG) connects a large language model to your private documents so it answers from your actual content instead of guessing. Enterprise RAG adds the layers a business needs: PII protection, access control, audit logging and citations. Perimattic's KnowledgeRAG delivers all of this deployed in your environment.
A public chatbot doesn't know your contracts, policies, product docs or research. RAG closes that gap: your documents are indexed, and every answer is retrieved from and grounded in them — with sources cited so staff can trust it. The hard part isn't the concept, it's making it enterprise-safe: keeping PII out, enforcing who can see what, logging every query, and keeping answers accurate as documents change. That production layer is the difference between a weekend demo and a system your whole company relies on.
KnowledgeRAG uses a fixed, opinionated stack — Qdrant for vector storage, BGE-M3 for embeddings via TEI, Microsoft Presidio for PII detection, Azure Entra ID for SSO and role-based access, and audit logging by default. We chose these because they work well together in production, not because they were fashionable this quarter. That fixed stack is why we can commit to a 4–6 week window on a real enterprise build.
What You Get
What you get
A private knowledge assistant deployed in your environment, indexed over your documents, secured with PII detection and role-based access, and delivered with an admin portal so your team controls what is ingested. Staff ask questions in plain language; the assistant answers from your content with citations pointing back at the source document. Access is enforced per user through SSO so the assistant only surfaces content the querying user is allowed to see. Every query is logged with timestamps and user identity so auditors can reconstruct usage.
Scope
Fixed scope — what is and is not included
| Not included | Included |
|---|---|
OCR or digitisation of non-digital archives | Document ingestion pipeline for the agreed source systems |
Custom connectors beyond the agreed set | Vector database (Qdrant) with hot and cold storage tiering |
Net-new content creation or document authoring | Embeddings (BGE-M3 via Text Embeddings Inference) |
Long-term document management (this is a search layer over your existing DMS, not a replacement for it) | PII detection (Microsoft Presidio) with policy-based redaction |
Layered AI guardrails on both retrieval and generation | |
SSO and role-based access control (Azure AD / Entra ID) | |
Admin portal for document upload and processing | |
Audit logging and query monitoring dashboard | |
Deployment inside your environment |
| Timeline | Best for | Tech stack |
|---|---|---|
| 4–6 weeks | Consulting firms, legal and compliance teams, R&D and knowledge-heavy organisations, internal support and IT operations | Qdrant vector databaseBGE-M3 embeddings via TEIMicrosoft Presidio (PII detection)Azure Entra ID (SSO + RBAC)Multi-cloud storage with hot/cold tiering |
Process
How it works
Ingestion
We connect and process your document sources into a searchable index. Common sources include SharePoint, Google Drive, Confluence, S3 buckets, and internal file shares. Documents pass through a normalisation and chunking pipeline before embedding.
Embedding and indexing
BGE-M3 embeddings go into Qdrant for high-quality retrieval. Hot storage holds the most-queried content; cold storage keeps everything else. Reindexing is incremental so newly-uploaded documents are searchable within minutes.
Security layer
PII detection scans every document at ingestion and every query at runtime — with policies you configure. SSO is wired into your Entra ID tenant so users log in with their existing credentials. Role-based access control enforces document-level visibility so the assistant never surfaces content a user isn't allowed to see.
Assistant and admin portal
A chat frontend for end-users; an admin portal for the team that owns the knowledge base. Admins upload documents, monitor ingestion, review query analytics, and configure PII and access policies.
Deploy and monitor
We ship into your environment with audit logging and monitoring on both the retrieval and generation paths. Handover includes runbook, admin training, and a clean line to a retainer if you want ongoing tuning.
Use Cases
Use cases
All of these share one pattern: lots of documents, expensive human search time — exactly where RAG pays back fast. Every deployment is anchored on a specific document set with measurable user volume, which is what makes the outcomes provable.
Consulting knowledge bases
Consultants search past engagements, methodologies, and templates constantly. A grounded assistant that surfaces the right precedent in seconds — instead of a 30-minute file hunt — compounds across an entire practice.
Legal and contract Q&A
In-house legal teams handle recurring questions about clauses, exceptions, and precedents. A private RAG assistant answers these against the actual contract set with citations, freeing lawyer time for the genuinely novel questions.
Policy and compliance lookup
Every regulated business fields the same policy questions from staff — expenses, leave, security, procurement. A private assistant grounded in the current policy documents (and only the current versions) removes the "which version applies?" ambiguity.
R&D and literature search
Research teams read the same corpus repeatedly. A RAG assistant grounded in your internal research plus the external literature you care about accelerates the initial exploration phase of any project.
Internal IT and HR support
The first-level questions — how do I reset my password, what is the onboarding checklist, what benefits do I have — answered instantly against the current documents. Human support handles the escalations only.
Results
Proven in production
For a consulting-firm client, this system cut document search time by 65% and document preparation time by 50%.
The consulting client had a large internal knowledge base that consultants searched dozens of times a day. Search time was the invisible tax on billable hours. After deploying KnowledgeRAG the median time to find the right precedent dropped by 65%, and preparation time for new client deliverables — which relies heavily on precedent retrieval — dropped by 50%. Neither number came from a demo; they came from timing logs before and after go-live.
Why Us
Why Perimattic for enterprise RAG
We've deployed this stack repeatedly, so it's a product, not an experiment — Qdrant, BGE-M3, Presidio, Entra SSO, audit logging, storage tiering, all proven together. Security is default. And the 65% / 50% result is ours, in production, not a benchmark from a paper. That means when we quote 4–6 weeks and enterprise-grade defaults, we're describing what we deliver, not what we hope to.
Frequently Asked Questions
Frequently asked questions
What is RAG?
RAG stands for retrieval-augmented generation. It is a design pattern that connects a large language model to a private knowledge base so answers are retrieved from that content and grounded in it, instead of generated purely from the model's training data. The result is an assistant that answers from your documents with citations, not from a general model that guesses.
How is enterprise RAG different from ChatGPT?
ChatGPT is a general-purpose assistant that answers from its training data. Enterprise RAG is a private assistant that answers from your own documents. It runs in your environment, it doesn't send your content to a third-party training pipeline, it enforces access control per user, and every answer is grounded in cited sources from your knowledge base — not synthesised from unrelated training data.
Is my data used to train models?
No. KnowledgeRAG is deployed inside your environment. Documents and queries do not leave your infrastructure for training. Model inference against embeddings can be run against a hosted LLM (OpenAI, Anthropic, Azure) with a no-training data-use agreement, or against a self-hosted open-source model where regulatory constraints require it.
How do you handle sensitive or PII data?
Every document is scanned by Microsoft Presidio at ingestion time; every query is scanned at runtime. Detected PII entities are either redacted, tokenised, or blocked from indexing depending on the policy you configure. PII policies are set per source and per user role, so what a compliance officer sees is not the same as what a general employee sees.
What document types are supported?
Common enterprise document formats — PDF, Word, PowerPoint, Excel, plain text, Markdown, and HTML — are supported out of the box. Structured data (tables, CSVs) is normalised through a schema-aware pipeline. Non-digital archives (scanned images) require OCR, which is not included in the KnowledgeRAG scope by default but can be added.
Can it enforce who sees what?
Yes. Role-based access control is enforced through Azure Entra ID (or your equivalent identity provider). Each user's query only retrieves content their role is authorised to see; content outside their access boundary is filtered before it ever reaches the language model. Audit logs record every query and its returned citations.
How accurate are the answers?
RAG answers are grounded in your documents with citations, so the accuracy is bounded by the quality and currency of the knowledge base itself. Every answer carries citations pointing at the source passages, so users can verify claims. In the consulting-firm deployment, staff consistently prefer the grounded assistant over unstructured search because they can see where the answer came from.
How long to deploy?
Fixed 4–6 weeks. Week 1 is scoping and source system access; weeks 2–4 are ingestion, security layer, and admin portal; weeks 5–6 are user acceptance testing and production cutover. The window is committed at engagement start.
What does it cost?
KnowledgeRAG is quoted as a fixed scope. The final number depends on how many source systems we integrate, the volume of documents to index, and the complexity of your PII and access policies. Contact us for a costed proposal — no hourly billing.
Related
Related in the Perimattic AI Suite
KnowledgeRAG pairs naturally with these sibling modules.
AI Agent Development
Agents often sit on top of a RAG knowledge base to make grounded decisions.
ExploreAI Automation
The retrieval layer is often wired into workflow automations that need grounded outputs.
ExploreAI Blueprint
If you have not yet chosen the knowledge base or the users, start here.
ExploreDeploy your knowledge assistant — fixed price, fixed timeline
One-page scope. One-page price. One clear delivery window.
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