AI Knowledge Base Assistant for Irish Businesses

An AI knowledge base assistant lets staff ask questions in plain English and get answers drawn only from your own policies, manuals and SOPs, with a citation to the source document. Digital Bridge builds internal assistants for Irish organisations from €4,250, with permission-aware access control.

What problem ai knowledge base assistant solves

Institutional knowledge in an Irish SME lives in one or two people's heads and a shared drive nobody can search. When they are on leave, the whole team slows down.

Why businesses are looking for ai knowledge base assistant

Knowledge assistant enquiries rarely come from marketing. They come from an operations manager who has watched the same policy question be asked in a group chat four times in a month, while the correct answer sits in a document nobody can locate. The organisation is not short of information; it is short of retrieval. What buyers want to see proven is grounding. Every answer must point at the document and section it came on a fixed quote, so a supervisor can check it in seconds. An assistant that is confidently wrong about a safety procedure or a leave entitlement is worse than no assistant at all, and serious buyers know it. In the enquiries that reach us, almost nobody uses technical language. People describe the problem in their own words — asking for "search all our internal documents at once", "ai assistant for company policies", "staff cannot find information sharepoint", "internal knowledge base with ai search" and "answer questions from our own pdfs" — and what they want back is a plain answer with a price attached.

What to look for in ai knowledge base assistant

Pick the single question set that costs the most senior time — HR policy, technical specification or client history. Index that corpus first and measure whether the follow-up questions stop.

  • Answers with a visible citation to the source document, page and revision date
  • Respect for existing permissions, so nobody sees content they could not already open
  • Coverage of the messy reality — PDFs, scanned procedures, spreadsheets and old intranet pages
  • A clear signal when the document set genuinely does not contain the answer

Our documents are out of date

The assistant surfaces that quickly, which is uncomfortable but useful; we flag stale and contradictory sources during indexing.

Will it leak restricted content?

Permissions are enforced at retrieval time against your existing groups, not bolted on afterwards.

Do we have to reorganise everything first?

No. We index what exists today and suggest cleanup based on what people actually ask for.

What you get

Fixed scope from €2,995. One-time build from €4,250. Hosting, re-indexing and support from €249/month.

  • Ingestion of policies, manuals, SOPs and past tickets
  • Semantic search with citation back to the source page
  • Permission-aware answers by team and role
  • Slack, Teams or web interface — whichever your team lives in
  • Gap report showing the questions your docs cannot answer
  • Scheduled re-indexing as documents are updated

Technology we use

We build on proven, well-documented platforms so you are never locked into us.

  • Supabase pgvector
  • OpenAI Embeddings (EU)
  • Anthropic
  • SharePoint
  • Google Drive
  • Slack
  • Microsoft Teams

How the project runs

Knowledge inventory (Day 0–4): We locate where knowledge currently lives — shared drives, email threads, a folder on one laptop, and the two people everyone asks. The last category is the risk the project exists to reduce. Source curation and ownership (Day 5–11): Documents are triaged into current, superseded and delete. Each surviving section gets a named owner and a review date, because an assistant answering from a 2019 policy is worse than no assistant. Permission model (Day 12–17): Access rules are mapped before indexing so the assistant can never surface HR, payroll or commercially sensitive material to someone who should not see it. Permissions are enforced at retrieval, not by prompt. Assistant build with citations (Day 18–25): Every answer cites the source document and section and links to it. Staff need to verify quickly, and citations are what turn the assistant from a curiosity into something people trust. Team pilot with gap logging (Day 26–36): A pilot group uses it for a fortnight while unanswered or badly answered questions are logged. Those gaps become the content backlog, which is usually the most useful output of the pilot. Freshness governance (Ongoing): Ongoing review dates, stale-document flags and a monthly report of the questions being asked. Knowledge bases decay quickly, and the governance is what keeps this useful in year two.

What we have learned delivering ai knowledge base assistant in Ireland

The real problem is rarely search. It is that the correct answer exists in three versions across two drives and nobody knows which is current, so curation does more for accuracy than any model choice. Permissions must be enforced at the retrieval layer. Instructing a model not to reveal something is not a security control, and we will not ship a knowledge assistant that relies on a prompt to keep payroll data private. Citations drive adoption. In our experience staff trust an assistant that shows exactly which policy paragraph it used, and quietly abandon one that gives a confident answer with no way to check it.

Measured outcomes

8s To an answer — Versus an average of 20 minutes hunting through shared drives. Cited Every answer — Linked to the exact document and section it came on a fixed quote. Role-aware Permissions — Staff only see documents their role is allowed to access.

Can staff see documents they should not?

No. Permissions are enforced at retrieval time against your existing groups in Microsoft 365 or Google Workspace, so a document that a user cannot open is never used to answer their question.

What if our documents are out of date?

The assistant surfaces that quickly, which is usually the first win. The gap report shows which questions have no reliable source, giving you a short, prioritised list of documents worth fixing.

Does it work inside Slack or Teams?

Yes. Most Irish teams prefer it in the tool they already use, so we deploy as a Slack or Teams bot with the same citations and permissions as the web interface.

How do you stop it inventing answers?

Retrieval-augmented generation with a strict rule: if no supporting passage is found above a confidence threshold, it says it does not know and points to who to ask. That behaviour is tested during the pilot.

How long does setup take?

Typically three to four weeks depending on document volume and how tidy the shared drive is. Ingestion and permission mapping take longer than the AI work in almost every project.

What problem does AI Knowledge Base Assistant solve?

Institutional knowledge in an Irish SME lives in one or two people's heads and a shared drive nobody can search. When they are on leave, the whole team slows down.

Why are Irish businesses buying ai knowledge base assistant now?

MARKET SIGNAL

The search is usually internal: staff cannot find the answer they already own

Knowledge assistant enquiries rarely come from marketing. They come from an operations manager who has watched the same policy question be asked in a group chat four times in a month, while the correct answer sits in a document nobody can locate. The organisation is not short of information; it is short of retrieval. What buyers want to see proven is grounding. Every answer must point at the document and section it came on a fixed quote, so a supervisor can check it in seconds. An assistant that is confidently wrong about a safety procedure or a leave entitlement is worse than no assistant at all, and serious buyers know it. How buyers describe it In the enquiries that reach us, almost nobody uses technical language. People describe the problem in their own words — asking for “search all our internal documents at once”, “ai assistant for company policies”, “staff cannot find information sharepoint”, “internal knowledge base with ai search” and “answer questions from our own pdfs” — and what they want back is a plain answer with a price attached. The competing results are enterprise knowledge platforms with implementation partners and annual contracts. There is very little for a fifty-person Irish organisation that wants its existing document store made answerable without a year-long programme.

What should you look for in ai knowledge base assistant?

Pick the single question set that costs the most senior time — HR policy, technical specification or client history. Index that corpus first and measure whether the follow-up questions stop. Answers with a visible citation to the source document, page and revision date Respect for existing permissions, so nobody sees content they could not already open Coverage of the messy reality — PDFs, scanned procedures, spreadsheets and old intranet pages A clear signal when the document set genuinely does not contain the answer

What results should you expect?

TO AN ANSWER Versus an average of 20 minutes hunting through shared drives. EVERY ANSWER Linked to the exact document and section it came on a fixed quote. Staff only see documents their role is allowed to access.

Before and after, measured

Grey bar = manual process today. Coloured bar = after integration. Figures are medians from Digital Bridge deployments with Irish SMEs, not guarantees — your own baseline is measured during the pilot week.

What is included in the build?

  • Ingestion of policies, manuals, SOPs and past tickets
  • Semantic search with citation back to the source page
  • Permission-aware answers by team and role
  • Slack, Teams or web interface — whichever your team lives in
  • Gap report showing the questions your docs cannot answer
  • Scheduled re-indexing as documents are updated

Which tools do you integrate with?

Not listed? Almost anything with an API can be integrated, and where no API exists we build a validated import or webhook bridge instead.

Knowledge inventory

We locate where knowledge currently lives — shared drives, email threads, a folder on one laptop, and the two people everyone asks. The last category is the risk the project exists to reduce.

Source curation and ownership

Documents are triaged into current, superseded and delete. Each surviving section gets a named owner and a review date, because an assistant answering from a 2019 policy is worse than no assistant.

Permission model

Access rules are mapped before indexing so the assistant can never surface HR, payroll or commercially sensitive material to someone who should not see it. Permissions are enforced at retrieval, not by prompt.

Assistant build with citations

Every answer cites the source document and section and links to it. Staff need to verify quickly, and citations are what turn the assistant from a curiosity into something people trust.

Team pilot with gap logging

A pilot group uses it for a fortnight while unanswered or badly answered questions are logged. Those gaps become the content backlog, which is usually the most useful output of the pilot.

Freshness governance

Ongoing review dates, stale-document flags and a monthly report of the questions being asked. Knowledge bases decay quickly, and the governance is what keeps this useful in year two.

What have we learned delivering ai knowledge base assistant in Ireland?

The real problem is rarely search. It is that the correct answer exists in three versions across two drives and nobody knows which is current, so curation does more for accuracy than any model choice. Permissions must be enforced at the retrieval layer. Instructing a model not to reveal something is not a security control, and we will not ship a knowledge assistant that relies on a prompt to keep payroll data private. Citations drive adoption. In our experience staff trust an assistant that shows exactly which policy paragraph it used, and quietly abandon one that gives a confident answer with no way to check it.

What does it cost?

One written figure, agreed before work starts One-time build from €4,250. Hosting, re-indexing and support from €249/month. Payment terms are 50% deposit and 50% on launch (40/30/30 on larger builds), with a signed scope before any work starts. You own the build, the prompts and the data.

Ready to scope your ai knowledge base assistant project?

Call Joey Bray directly. Thirty minutes, no sales script — we map the workflow, tell you honestly whether AI is the right answer, and give you a fixed price if it is.