Private AI Knowledge Systems for UK Businesses
A RAG knowledge system is an AI assistant that answers from your own documents with cited, auditable sources instead of guessing. MicroPyramid builds private RAG-powered copilots, support assistants, and semantic document search for UK professional services, legal, public sector, and SaaS teams: query your internal knowledge in natural language, with source citations and role-based access.
The UK Knowledge Problem RAG Systems Solve
UK businesses (from City law firms and regulated enterprises to growing SaaS companies and public sector bodies) accumulate enormous volumes of structured and unstructured knowledge. Most of it sits inaccessible in SharePoint, Confluence, email threads, or PDF archives. When a colleague or customer asks a question, the answer exists somewhere, but finding it costs hours.
RAG (Retrieval-Augmented Generation) systems solve this by indexing your documents privately, retrieving the most relevant passages for each query, and returning a grounded answer with citations back to the source. No hallucination, no data leaving your environment, no off-the-shelf chatbot trained on public internet content.
Organisations are accountable for how personal data is processed and stored. Our systems are designed with that in mind: data residency in AWS eu-west-2, access controls, audit logging, and the ability to deploy entirely on-premise if your data protection team requires it.
What We Build for UK Teams
Six types of private RAG-powered knowledge systems, each shaped around UK compliance, data-residency, and sectoral needs
Internal Knowledge Copilot
Give your UK team a private retrieval assistant over internal policies, SOPs, compliance guides, and runbooks, with citations, role-based access, and an audit trail your risk and compliance teams can review.
- Document ingestion pipeline
- Semantic retrieval with citations
- Role-based access control
AI Support Assistant
Turn your existing support docs, product FAQs, and ticket history into an intelligent first-line assistant. Ideal for UK SaaS companies managing high support volumes from European and US customers.
- Knowledge ingestion & indexing
- Retrieval-backed answers
- Fallback & escalation logic
Enterprise Document Search
Replace full-text keyword search with semantic retrieval across contracts, compliance filings, regulatory correspondence, and technical specs, built for the document-heavy realities of UK professional services.
- Semantic search & ranking
- Multi-format document support
- Filters & faceted navigation
Legal & Compliance Q&A
Give legal, risk, and compliance teams instant retrieval over regulatory guidance, industry notices, and internal policies, with source attribution so nothing gets misquoted.
- Regulation & policy retrieval
- Source-attributed answers
- Access-controlled by team
Private Document Q&A
Secure, access-controlled Q&A over sensitive documents (client contracts, board papers, data-processing records) deployed entirely within your private cloud or AWS eu-west-2 (London) region.
- On-premise or private cloud
- UK data residency (eu-west-2)
- Audit logging
Secure RAG with Citations
Every answer is attributed to its source document with page-level citations, auditable, trustworthy, and safe for UK regulated industries, from legal and professional services to public sector procurement.
- Source-attributed answers
- Confidence scoring
- Hallucination mitigation
Best Fit For
- you have a large body of policies, contracts, compliance docs, or product knowledge UK teams need to query
- answers need citations and auditability your risk and compliance teams can review
- you want a private assistant that keeps all data within the UK or your own infrastructure
- you need retrieval-backed answers grounded in your own regulated data
Not the Right Fit When
- you mainly need AI embedded inside an existing product workflow rather than a standalone knowledge system
- your source content is thin, outdated, or not ready to index
- you expect autonomous answers without guardrails or human review in regulated workflows
- the goal is a public-facing generic chatbot with no grounding in your own documents
If you need AI inside an existing product workflow, start with AI Feature Development instead.
Why UK Teams Work With Us
12+ years of delivery experience, shaped to fit UK data residency and commercial expectations
Privacy by Design
We design data handling in from day one: data minimisation, audit logging, access controls, and data-residency configurations scoped to AWS eu-west-2 (London) by default.
GBP Billing, Stripe & GoCardless
Invoiced in GBP with Stripe or GoCardless Direct Debit. No FX surprises, no US-dollar conversion overhead, straightforward commercial terms for UK businesses.
Your Engineers, Direct Access
You work with the engineers building your system, not an account manager relay. The same team that did discovery writes the code and answers questions on Slack.
How We Deliver
A focused, low-risk process designed to get UK teams from problem to working system fast
Discovery & Scoping
Map UK-specific use cases, identify data sources, define data-handling requirements, and set success metrics
Data Preparation
Document ingestion, chunking strategy, embedding pipeline, and vector index setup, hosted in your preferred UK region
RAG Architecture
Retrieval system design, LLM selection (private or API), prompt engineering, and context management
Build & Deploy
UI integration, accuracy testing, staged deployment, and monitoring, with full handover documentation
RAG & AI Technology Stack
We select models and infrastructure based on your UK data-residency, privacy, and performance requirements, not on defaults
AI & Retrieval
Data & Storage
How to Get Started
We recommend a Discovery Sprint: low risk, clear output, a data-handling review, and a foundation for everything that follows
RAG Discovery Sprint
Map your use case, assess data sources, and get an architecture and privacy-aware implementation roadmap
- Use-case mapping & data review
- Architecture recommendation
- Data-handling and access review
- Implementation roadmap
Knowledge Copilot MVP
Full build of a retrieval-based assistant with UI, source citations, and UK data residency
- Document ingestion pipeline
- Retrieval + LLM integration
- Web interface with access control
Ongoing RAG Expansion
Continued iteration on your AI knowledge system as your data and use cases grow
- Additional data sources
- Quality & accuracy improvements
- Analytics & monitoring
AI Work in Production
Two products we built and run ourselves, plus a client platform with AI feedback shipped inside the product.
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Read the case studyFrequently Asked Questions
Straight answers to what UK founders, CTOs, and compliance leads 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 the most relevant passages from your own documents and uses them to generate an answer with cited sources, instead of relying on what a language model memorised from the public internet. Because every answer is grounded in your content and attributed to its source, it stays accurate, auditable, and current as your data changes, which is what makes it safe for regulated work.
Can you build a RAG system with UK data residency?
Yes. We deploy by default in AWS eu-west-2 (London) so your documents and embeddings stay in the UK, and we can run the entire system on-premise or in your own private cloud if your data protection team requires it. Data minimisation, role-based access controls, and full audit logging are designed in from day one.
How is a custom RAG system different from Microsoft 365 Copilot or Glean?
Off-the-shelf copilots like Microsoft 365 Copilot or Glean work well 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, UK data residency or on-premise deployment, or a copilot embedded in your own product, and when you want to own the system outright rather than rent per-seat licences indefinitely.
How do you stop the AI from hallucinating or inventing answers?
Every answer is grounded in retrieved passages and attributed with page-level citations, so a user can verify the source before trusting it. We add confidence scoring, fallback and escalation logic when retrieval is weak, and an evaluation pass on your real questions before launch, so the system says “I don’t know” or escalates to a human rather than making something up. For regulated teams, that auditability is the difference between a usable tool and a compliance risk.
Can it work for large regulated enterprises or public-sector teams?
Yes. Cited, access-controlled retrieval is a strong fit for large or regulated enterprises, legal and compliance teams, and UK public sector bodies: secure Q&A over regulatory guidance, internal compliance manuals, contracts, and policy libraries, with source attribution so nothing gets misquoted. Data residency in the UK, audit logging, and per-team access controls are designed in, not bolted on, so the system can sit inside regulated and sensitive workflows.
How long does it take to get a working system, and how does billing work?
It depends on scope: how many sources and formats we ingest, how strict the permission rules are, and how the assistant is surfaced. A short discovery sprint comes first and ends with a fixed estimate for the build. We scope the smallest valuable version first and deploy in iterative slices so you see answer quality on your own data early. UK clients are invoiced in GBP via Stripe or GoCardless Direct Debit, with no US-dollar conversion overhead.
Do we own the source code and IP?
Yes. You own all source code and intellectual property we build, committed to your repositories as we go, so there is no vendor lock-in if you later bring the system fully in-house.
Ready to Build Your UK Knowledge System?
Start with a free discovery call. We'll assess your use case, your data-handling requirements, and your data sources, and propose a concrete first step with no obligation.