Error: knowledge is impossible to search
Answers are buried in PDFs, wikis, drives and tickets.
( automate · llm / rag solutions
AI Knowledge Base & RAG Solutions Built on Your Own Data
We build AI knowledge bases and retrieval augmented generation (RAG) systems that answer accurately from your documents and data — and private or on-premise LLMs for organisations that need AI without sending data outside.
// overview
What is an AI knowledge base?
An AI knowledge base lets people ask questions in plain language and get accurate answers drawn from your own documents and data — with sources. It is usually built with retrieval augmented generation (RAG): the system first finds the most relevant content, then a large language model writes the answer from it.
RAG is the most reliable way to make AI useful on company knowledge: it keeps answers grounded, shows where they came from, respects permissions and updates as content changes — without retraining a model.
For organisations with strict privacy needs, we deploy private and on-premise LLMs so data never leaves your environment. Want the knowledge base behind a chatbot or agent? See AI chatbots and AI agents.
// sound familiar?
Signs you need an AI knowledge base
If these sound familiar, RAG can turn your documents into answers.
Warning: AI answers are made up
Generic AI tools guess instead of using your content.
Error: data cannot leave your environment
Privacy or regulation rules out public AI services.
Warning: the same expert gets every question
A few people answer everything because nobody else can find it.
Error: a RAG pilot underperformed
Your prototype gave patchy answers and nobody knows why.
Warning: content changes constantly
Static FAQs and chatbots go out of date quickly.
Need AI that never leaves your servers? See private and on-premise LLMs.
// what's included
Our LLM and RAG development services
Everything needed for accurate, secure AI on your own knowledge.
AI Knowledge Base Development
Ask-anything search over documents, wikis, tickets and databases.
RAG Development Services
Ingestion, chunking, hybrid search, re-ranking and citations done properly.
Private LLM Deployment
Models running in your own cloud account or private environment.
On-Premise LLM
Models hosted in your data centre for maximum control.
LLM Fine-Tuning Services
Fine-tune models on your examples when RAG alone is not enough.
Custom GPT & Assistant Development
Purpose-built assistants for specific teams and tasks.
Evaluation & Accuracy Testing
Test sets, scoring and monitoring so quality is measured, not guessed.
LLM Integration
Connect LLMs to your apps, chatbots, agents and workflows.
// free assessment
Get a Free RAG Readiness Assessment
Tell us about your documents, systems and privacy needs, and we will send a short assessment of your content, the right architecture and where to start.
// process
How we build RAG systems that work
A process designed around accuracy, security and measurable quality.
Start Your RAG Project →01Sources & questions
Knowledge sources, users and the questions that matter agreed.
→ source map + test questions
02Ingest & structure
Content cleaned, chunked and enriched with metadata and permissions.
→ search-ready knowledge
03Retrieval & model
Hybrid search, re-ranking and the right model chosen and hosted.
→ working RAG pipeline
04Evaluate
Accuracy, citations and safety scored against test questions.
→ evaluation report
05Deploy & monitor
Integrated into chat, apps or agents with ongoing monitoring.
→ live system + quality dashboard
// how we measure
What we measure on LLM and RAG projects
We agree these measures with you at the start and report on them in plain language.
01
// answer accuracy on test questions
02
// time to find information
03
// questions answered without an expert
// a typical challenge
Technical team whose product knowledge lived in thousands of manuals and tickets.
// how we approach it
Private RAG knowledge base with hybrid search, citations and permission-aware access for support and sales.
// what we report
Progress against the measures above, every month, in plain language.
// who it's for
LLM and RAG solutions for knowledge-heavy organisations
For teams whose expertise is locked in documents.
Support & Service Teams
Accurate answers from manuals, policies and past tickets.
Regulated Industries
Private LLMs for finance, healthcare and legal teams.
Sales & Product Teams
Product and technical knowledge at their fingertips.
Software Products
RAG features inside your product. See AI SaaS.
// private & on-premise llm
Private and on-premise LLMs
Some data should never be sent to a public AI service. We deploy private LLMs in your own cloud account and on-premise LLMs in your data centre — open-weight models sized to your hardware and needs, connected to your knowledge through RAG and protected by your security controls.
You keep full control of data, logs and access, with no data used to train anyone else’s model.
- your documentsyour environment
- questionsprivate llm
- private llmanswers
- logsyour storage
- accessyour sso
- outside worldno data shared
// approaches
RAG, fine-tuning or both?
The right approach depends on what you need the model to know and how it should behave.
01 / rag
Retrieval Augmented Generation
Answers from your latest content, with sources and permissions.
- Up to date
- Citations
- Permissions
02 / fine-tune
Fine-Tuning
Teach a model your format, tone or specialised tasks.
- Style
- Format
- Specialised tasks
03 / private
Private Hosting
Run models in your cloud or on-premise for control.
- Your cloud
- On-premise
- No sharing
04 / apps
In Chatbots & Agents
Put your knowledge base to work in assistants and agents.
- Chatbots
- Agents
- Workflows
Building an internal assistant for staff? See enterprise AI chatbots →
// compare
Custom RAG vs off-the-shelf AI search tools
How a custom LLM development company approach compares with the alternatives.
| Feature | tech pumpkin | Off-the-shelf AI search | Public AI chat |
|---|---|---|---|
| Answers from your content | ✓ With citations | ✓ Within limits | ✕ |
| Permission-aware | ✓ Per user | Varies | ✕ |
| Private / on-premise options | ✓ | Vendor cloud | ✕ |
| Measured accuracy | ✓ Evaluation sets | Rarely | ✕ |
| Any source or system | ✓ Custom connectors | Supported sources | Uploads only |
| Ownership | ✓ You own it | Platform-owned | Platform-owned |
// go deeper
LLM and RAG, explained
What teams ask before building with LLMs.
What is retrieval augmented generation?
Retrieval augmented generation (RAG) is a technique where an AI system first retrieves relevant information from your content, then uses a large language model to write an answer based on it. It keeps answers grounded and up to date, shows sources and avoids retraining models every time content changes.
RAG vs fine-tuning
| Factor | RAG | Fine-tuning |
|---|---|---|
| Best for | Knowledge and facts | Style, format, specialised tasks |
| Updates | Instant when content changes | Retrain needed |
| Citations | Yes | No |
| Cost to start | Lower | Higher |
Many systems combine both.
When do you need a private or on-premise LLM?
Choose a private LLM when data cannot leave your environment, regulations require it, or usage is high enough that hosting your own model is cheaper. Modern open-weight models are capable enough for many business tasks, especially when combined with RAG.
How to improve RAG accuracy
- Clean and structure source content
- Chunk by meaning, with metadata
- Use hybrid keyword and vector search
- Re-rank results before answering
- Require citations and refuse when unsure
- Measure against a test set and monitor live answers
// faq
LLM and RAG FAQs
Straight answers to the questions we hear about LLMs and RAG.
What is an AI knowledge base?
An AI knowledge base lets people ask questions in plain language and get accurate answers drawn from your own documents and data, with sources.
What is RAG?
Retrieval augmented generation finds relevant content from your sources first, then uses a large language model to write an answer from it, keeping answers grounded and up to date.
Do we need to train our own model?
Usually not. RAG with an existing model works for most knowledge use cases. Fine-tuning helps with specialised formats or tasks.
Can you deploy a private or on-premise LLM?
Yes. We deploy models in your own cloud account or data centre so data never leaves your environment.
How do you make answers accurate?
Clean content, smart chunking, hybrid search, re-ranking, required citations and evaluation against test questions, plus monitoring after launch.
Can the knowledge base respect permissions?
Yes. Retrieval checks each user’s access so they only get answers from content they are allowed to see.
Which sources can you connect?
Documents, wikis, drives, ticketing systems, databases and websites, plus custom systems through APIs.
How much does a RAG project cost?
Cost depends on sources, volume, hosting choice, permission model and evaluation needs. After a scoping call you receive a written scope and estimate.
Is our data safe?
Yes. We use your own cloud accounts, role-based access and encryption, choose model providers and settings that meet your privacy requirements, and never use your data to train public models.
Who owns what you build?
You do. You own the code, prompts, workflows, data and accounts from day one. We document everything and offer ongoing support, but you are never locked in.
// let’s make your knowledge answer
Ready to build your AI knowledge base?
Tell us about your documents, users and privacy needs. You will get a clear plan and estimate — no obligation, and an NDA if you need one.
- 01Discovery call — sources, users and hosting
- 02Written scope and estimate
- 03Kickoff with your dedicated team