RAG-Powered Knowledge Systems for Businesses
A RAG knowledge system is an AI assistant that answers from your own documents with cited sources instead of guessing. MicroPyramid builds private, retrieval-based copilots, support assistants, and document search over your SOPs and data, deployed in your environment, with a working prototype in days and answer quality you can audit.
What We Build
Six types of RAG-powered knowledge systems, each designed for a specific business problem
Internal Knowledge Copilot
Build a private retrieval assistant over your company docs, SOPs, runbooks, and policies, with citations and access control.
- Document ingestion pipeline
- Semantic retrieval with citations
- Role-based access control
AI Support Assistant
Turn your existing support docs, FAQs, and ticket history into an intelligent assistant that deflects common questions instantly.
- Knowledge ingestion & indexing
- Retrieval-backed answers
- Fallback & escalation logic
Enterprise Document Search
Replace keyword search with semantic search across large document collections: contracts, reports, research, technical specs.
- Semantic search & ranking
- Multi-format document support
- Filters & faceted navigation
Sales & Proposal Assistant
Help sales and bid teams instantly retrieve case studies, proposal templates, pricing, and product specs from your own data.
- Proposal & case study retrieval
- Product knowledge base
- CRM-ready integration
Private Document Q&A
Secure, access-controlled Q&A over sensitive documents (contracts, compliance docs, legal briefs) without data leaving your environment.
- On-premise or private cloud
- End-to-end encryption
- Audit logging
Secure RAG with Citations
Every answer is attributed to its source with page-level citations, auditable, trustworthy, and safe for regulated industries.
- Source-attributed answers
- Confidence scoring
- Hallucination mitigation
Where RAG Systems Deliver ROI
The strongest use cases share one trait: a large, growing body of knowledge that people need to query in natural language
Customer Support
Reduce ticket volume with an assistant that answers common questions from your docs, help articles, and product knowledge base.
Internal Operations
Help teams instantly search across policies, runbooks, process documentation, and internal wikis without hunting through Confluence or Drive.
Sales Enablement
Fast retrieval of case studies, competitive battlecards, proposal templates, and product specs during active deals.
Compliance & Legal
Secure Q&A over contracts, regulatory guidance, and legal policies, with full attribution and audit trail.
HR & Onboarding
Give new hires instant answers from the employee handbook, benefits docs, and onboarding materials without bothering HR.
Product Documentation
Help users self-serve from product docs, API references, and release notes instead of opening support tickets.
Best Fit For
- you have a large body of docs, SOPs, policies, tickets, or product knowledge people need to query
- answers need citations, permissions, or auditability instead of generic chatbot responses
- you want a private assistant for internal teams, customers, or regulated workflows
- you need retrieval-backed answers grounded in your own data
Not the Right Fit When
- you mainly need AI inside an existing product workflow rather than a standalone or shared knowledge system
- your source content is thin, outdated, or not ready to index yet
- you expect autonomous answers without guardrails, review, or ownership of the underlying knowledge
- the goal is novelty rather than solving a real support, search, or internal knowledge problem
If you need AI inside an existing app workflow, start with AI Feature Development.
Related public proof: DiscoveredBy shows our current AI search visibility and recommendation work, while Refactored.ai shows AI-assisted product workflows in a production learning platform.
How We Deliver
A focused, low-risk process designed to get you from problem to working system fast
Discovery & Scoping
Map use cases, identify data sources, define privacy and security requirements, and set success metrics
Data Preparation
Document ingestion, chunking strategy, embedding pipeline, and vector index setup
RAG Architecture
Retrieval system design, LLM selection, prompt engineering, and context management
Build & Deploy
UI integration, accuracy testing, staged deployment, and monitoring setup
RAG & AI Technology Stack
We select models and infrastructure based on your privacy, cost, and performance requirements, not on defaults
AI & Retrieval
Data & Storage
Infrastructure
How to Get Started
We recommend starting with a Discovery Sprint, low risk, clear output, and a foundation for everything that follows
RAG Discovery Sprint
Map your use case, assess data sources, and get an architecture and implementation roadmap
- Use-case mapping & data review
- Architecture recommendation
- Privacy & security assessment
- Implementation roadmap
Knowledge Copilot MVP
Full build of a retrieval-based assistant with UI and source citations
- Document ingestion pipeline
- Retrieval + LLM integration
- Web interface with access control
Ongoing RAG Expansion
Continued development and iteration on your AI knowledge system
- Additional data sources
- Quality & accuracy improvements
- Analytics & monitoring
Success Stories & Client Projects
Products and platforms we've built and shipped for clients worldwide
Refactored
Interactive Python learning management system with tutorials, exercises, and assessments
Read case study
PRO Music Tutor
Premium online music learning platform connecting students with world-class instructors
See more work
Bough Digital
UK-based digital marketing agency platform with campaign management and analytics
See AI proof
CREDITABLE
Employee financial wellness platform for savings, loans, and workplace financial services
See portfolioFrequently Asked Questions
Straight answers to what US founders and teams ask before building a RAG knowledge system.
What is a RAG knowledge system?
A RAG (retrieval-augmented generation) knowledge system is an AI assistant that retrieves relevant passages from your own documents and uses them to generate an answer with cited sources, instead of relying on what a language model memorized. Because every answer is grounded in your content and attributed to its source, it stays accurate, auditable, and current as your data changes.
How is this different from ChatGPT or a generic chatbot?
A generic chatbot answers from a model’s general training and will confidently make things up about your business. A RAG knowledge system answers only from your documents, SOPs, and data, shows the source behind each answer, and respects your access permissions, so it can be trusted for support, compliance, and internal operations where a wrong answer has a cost.
Should we build a custom RAG system or just buy Microsoft Copilot or Glean?
Off-the-shelf copilots like Microsoft 365 Copilot or Glean are a good fit when your knowledge already lives entirely inside their ecosystem and generic answers are acceptable. A custom RAG system is the better choice when you need answers grounded in data they don’t reach, page-level citations, your own access rules, private or on-premise deployment, or a copilot embedded in your own product, and when you want to own the system rather than rent per-seat licenses.
Does our data stay private?
Yes. We deploy in your environment, your private cloud or on-premise, so your documents and embeddings never leave your control, and we can run with access-controlled, audit-logged Q&A for sensitive content. Model and infrastructure choices are made around your privacy, residency, and compliance requirements rather than a default vendor.
How do you keep answers accurate and prevent hallucinations?
Every answer is grounded in retrieved passages and attributed with page-level citations, so a user can verify the source. We add confidence scoring, fallback and escalation logic when retrieval is weak, and an evaluation pass on real questions before launch, so the system says "I don’t know" or escalates rather than inventing an answer.
How long does it take to get a working RAG system?
A discovery sprint produces a working prototype in days, and a production knowledge copilot with ingestion, citations, and access control typically ships in weeks rather than months, because we use AI-assisted engineering and scope the smallest valuable version first. We deploy in iterative slices so you see answer quality on your own data early.
What data sources can it use, and do we own the code?
It can ingest PDFs, office documents, wikis, help articles, ticket history, contracts, and database or API content across multiple formats. You own all source code and intellectual property we build, committed to your repositories as we go, so there is no lock-in if you later bring the system in-house.
Ready to Build Your Knowledge System?
Start with a free discovery call. We'll assess your use case, data sources, and requirements, and propose a concrete first step.