AI & Automation
AI Knowledge Base & Search
Make what your organization knows findable — ask a question in plain language and get a clear, sourced answer drawn from your documents, policies and records.
AI Knowledge Base & Search: the overview
Organizations know a lot, but that knowledge is scattered across drives, wikis, PDFs, emails and systems — so people can’t find it, ask a colleague instead, or make decisions without it. AI knowledge search addresses that: it indexes your content and lets people ask a question in plain language and get a clear answer, with links to the source.
We connect your documents, policies, records and systems, and build search grounded in that content using AI retrieval, so answers come from your material and cite where they came from — not from the open internet. Access controls follow your existing permissions, so people only see what they’re allowed to, which makes it suitable for internal and sensitive knowledge.
The result is knowledge that’s actually usable: faster onboarding, fewer interruptions to experts, and more consistent answers. Because it’s built on your content, it becomes more useful as that content is kept current — and it connects naturally to an AI assistant if you want a conversational front end.
What AI Knowledge Base & Search includes
Unified Indexing
Index documents, wikis, PDFs, records and systems into one searchable knowledge base.
Natural-Language Search
Ask questions in plain language and get direct, relevant answers.
Sourced Answers
Answers cite and link to the source document, so they’re verifiable.
Access Control
Permissions so people only find and see what they’re allowed to.
Freshness & Updates
Scheduled re-indexing so answers reflect your latest content.
Assistant-Ready
Powers an internal or customer AI assistant as a conversational front end.
Who AI Knowledge Base & Search is built for
Knowledge-heavy teams
Make policies, manuals and expertise findable in plain language.
Support & service
Agents and customers find accurate, sourced answers faster.
Onboarding & training
New staff self-serve answers instead of interrupting colleagues.
Regulated organizations
Searchable, access-controlled knowledge with sourced answers.
Is AI knowledge base and search the right choice?
A good fit when
- Knowledge is spread across drives, wikis, PDFs and systems, and staff ask colleagues instead of searching.
- Content has owners and is kept reasonably current.
- Permissions matter: different teams should see different documents.
- You want sourced answers people can check, not just a list of files.
Consider another option when
- Your content already lives in one tool whose built-in AI search covers it; try that first.
- Content is mostly out of date or contradictory; a content clean-up comes first.
- You need a conversational front end for customers; see AI assistants, which can use the same index.
Usually in a first release
- Connectors to two or three content sources (for example a shared drive, a wiki and a policy library)
- Permission-aware indexing and search
- Answers with citations and links to the source passage
- A labelled test set of real questions, scored before go-live
- Scheduled re-indexing and a usage log
Outside the first release unless agreed
- Additional sources, languages or file types beyond those agreed
- Rewriting or tidying source content
- Customer-facing deployment unless agreed
- Actions in other systems
Anything outside the approved scope is reviewed and agreed before work begins. See how we work.
Data, controls, responsibilities and ownership
Data migration and integrations
- Each source connector depends on that system’s API, including whether it exposes document permissions so search can respect them.
- We document which data goes to which model provider for each workflow, and keep personal or sensitive fields out of prompts where the task does not need them.
- Commercial model APIs offer business terms and data-retention settings that vary by provider and account type. We configure the settings you choose and record them; see security and data protection.
- The vector index and document copies are stored in your own cloud account, and deleting a source document removes it from the index on the next refresh.
- Where your policy requires it, open-weight models hosted in your own cloud account or on your own servers are an option. They usually mean more hosting effort and can be less accurate on hard cases, so we compare them on your test set before you decide.
Roles, approvals and audit
- Permission-aware retrieval
- Search only returns passages from documents the signed-in user can already open.
- Citations
- Every answer links to its source passage; if no source is found, the tool says so rather than guessing.
- Evaluation before go-live
- Answer quality is measured on a labelled set of real questions and re-run after content, prompt or model changes.
- Audit logging
- Searches, answers and sources returned are logged, with a retention period you set.
What we need from your team
- Content owners for each source, and a decision on what is in and out of scope.
- Admin access to the sources, and confirmation of how permissions are set today.
- A set of real questions staff ask, with the answers your experts would give.
- Model provider, hosting and storage accounts in your organization’s name.
Ownership, support and running costs
- Project code, prompts, configuration and evaluation sets transfer to you on full payment, and your data and documents are yours throughout. Third-party foundation models remain the provider’s and are used under the provider’s terms; see our IP and ownership policy.
- Model provider, hosting and storage accounts are set up in your organization’s name where possible, so usage, retention settings and terms sit between you and the provider.
- Running costs by category: model or API usage (grows with volume and input length), vector index and storage, application hosting, evaluation upkeep when your content or the model changes, and monitoring. Amounts depend on volume, model choice and hosting.
- Providers update and retire models. A maintenance and support agreement covers re-running your evaluation set and adjusting prompts when that happens.
Related reading for this decision
- AI assistants and chatbots
A conversational front end that uses the same grounded index.
- LLM app development: RAG, agents and what ships
A plain-language explanation of retrieval and how answer quality is evaluated.
- Systems and API integration
How content sources are connected and kept in sync.
- Security and data protection
How access, storage location and retention are agreed for indexed content.
How we deliver AI Knowledge Base & Search
Custom software built around the way your business works. Five steps, with a free pilot of 2 to 3 key modules before the full build.
Step 1: Understand
We learn how your business works.
Your requirements, workflow, challenges and goals, understood before anything is recommended.
Step 2: Plan
We design the right solution around your workflow.
Modules, workflows, roles, approvals, reports and integrations, agreed before development.
Step 3: Select Technology
Choose the right technical foundation.
Technology options matched to your users, security, budget and growth, not one fixed stack.
- Free pilot
Step 4: Pilot
Test our work before full project development.
Free. You choose 2 to 3 key modules and we build them first, so you can judge our work.
The full project starts only after you approve the pilot.
- Full project
Step 5: Build & Scale
From approved pilot to complete digital system.
Full development, testing, deployment, training and support, built to grow with you.
Guides for this decision
In-depth, practical reading for teams planning this kind of project.
- Guide · 9 min readEnterprise Knowledge Search with RAG: Permissions, Sources and AccuracyEnterprise knowledge search with RAG lets staff ask questions in plain language and get answers drawn from company documents, with citations, limited to what each person is allowed to see. This guide covers sources, permissions, accuracy and rollout.Read the guide
- Guide · 9 min readHow to Evaluate an AI Pilot Before Rollout: Test Sets, Metrics and a Go/No-Go ScorecardAn AI pilot is ready for rollout when it meets acceptance criteria you set in advance, on a test set built from your own data, including the hard cases. This guide covers metrics by task type, human review thresholds, failure-mode testing and a go/no-go scorecard.Read the guide
AI Knowledge Base & Search FAQ
Traditional search matches keywords and returns a list of documents to dig through. AI knowledge search understands the question, finds the relevant passages across your content, and returns a direct, sourced answer, which is usually faster for the person asking.
Only from your own content — the documents, policies, records and systems you connect — using retrieval so answers are grounded in your material and cite their source. When it cannot find a source, it says so rather than answering from general knowledge.
Yes, where the source system exposes its permissions. Search then only returns content each person is already allowed to open, which is essential for internal and sensitive knowledge. We confirm how each source handles permissions before the scope is agreed.
Yes. We set up scheduled re-indexing so the knowledge base reflects your latest documents and records rather than a stale snapshot. How often depends on each source and how quickly its content changes.
Yes. The same grounded knowledge can power an internal or customer AI assistant as a conversational front end — the two services work well together.
The pilot shows answer quality on a test set of your real questions over the connected sources, and confirms that permissions are respected. It does not prove quality on sources or topics outside the pilot, behaviour with your full document volume, or running cost at scale. Those are measured in a controlled rollout after you approve the full build.
Project code, prompts, configuration, the index and the evaluation set transfer to you on full payment, and your content is yours throughout, with no per-seat fees from us. Third-party foundation models remain the provider’s and are used under the provider’s terms, and open-source components stay under their own licences, as our IP and ownership policy sets out.
See working software before you commit
Before you commit to the full project, we build 2 to 3 of your key modules as working software, free of charge. Your team tests the pilot, and the full build starts only after you approve it.
See how the free pilot worksUnderstand
We learn your requirements and how your organisation works today.
Select pilot modules
Together we choose 2 to 3 key modules that prove the solution.
Build the working pilot
We build those modules as real, working software, free of charge.
You test it
Your team uses the pilot. The full project starts only after you approve it.
Start a conversation
Tell us how your business works.
Describe what is slowing your team down. We will help you work out what to build, and how a free pilot lets you judge our work before the full project.
Prefer WhatsApp? Start a chatWhat happens next
You send a short brief
The problem, the people involved and any target date. A senior engineer replies within 4 business hours.
We understand your workflow
A first call about how your business works today. An NDA can be signed before you share details.
You test a free pilot
You choose 2 to 3 key modules and we build them first, so you judge real software before the full project.