LLM & RAG · Enterprise knowledge base AI
Banao builds AI assistants that read your company's actual documents — wikis, policies, runbooks, ticket histories, PDFs — and answer employee questions with a cited passage a reviewer can trace back to the source. The model generates nothing from guesswork; it retrieves from your corpus and grounds every reply in what you wrote.
The knowledge already exists in your organisation. The problem is that employees cannot find it fast enough to act on it, so they ask a colleague, wait for an answer, or guess. We replace that search-and-ask loop with a single assistant that covers everything you have documented, answers in seconds, and shows its source.
Banao— our own engineers answer questions from internal runbooks and architecture docs through a knowledge assistant we built and run every working day.
Connecting an LLM to your company knowledge is an ingestion problem, a retrieval problem, an accuracy problem, and an access-control problem — we solve all four as one system.
We ingest your wikis, SharePoint, Confluence, PDFs, Notion pages, ticket histories, and scanned documents, parse them for clean text, and build a searchable index that reflects today's content — not a one-time export.
Employees do not always remember the exact phrase the policy uses. We combine meaning-based vector search with keyword matching so the assistant finds the right passage even when the wording in the question differs from the wording in the document.
Every answer names the document and the section it came from. Employees can verify it in one click, and the assistant says 'not found' when the corpus does not cover a question instead of inventing a plausible answer.
The assistant respects your existing access rules: an employee only receives answers from documents they are authorised to read. HR policies do not surface in answers to contractors; confidential files stay confidential.
A policy updated this morning is what the assistant retrieves this afternoon. Incremental re-indexing means the knowledge base stays current as documents change without a full rebuild each time.
Employees can ask follow-up questions in the same thread — 'what about the exception for contractors?' — and the assistant tracks the context of the conversation rather than treating each question as isolated.
We wire the assistant into Slack, Microsoft Teams, or your existing intranet portal so employees do not need to learn a new tool — they ask where they already ask questions, and the answer comes back cited.
A live eval suite scores retrieval accuracy and answer faithfulness on your real questions, so you can see whether the system holds its accuracy over time and catch any regression before it becomes a trust problem.
Most enterprise knowledge tools have the same failure: the information exists and the search bar exists, but the gap between them is too wide for a busy employee to cross reliably. A policy is in Confluence but the employee does not know whether to search 'annual leave' or 'PTO' or 'time off'. A runbook exists but it was last updated three years ago and nobody knows which version applies. An answer was given in a Slack thread and is now effectively lost.
An AI knowledge assistant changes that dynamic without requiring a documentation rewrite or a new filing system. It reads across the entire corpus — whatever shape it is in — finds the passage that best matches what was asked, and answers from it. The employee gets a reply in seconds with a source they can trust. The colleague who fielded five of those questions a day gets their time back.
Enterprise search returns a list of documents that may contain the answer; an AI knowledge assistant returns the answer itself, drawn from the most relevant passage, with the source cited. Employees do not need to know the exact term the policy uses.
A significant share of institutional knowledge lives in conversations, not documents. We ingest message exports, resolved tickets, and email threads as first-class sources so answers that were given once can be given again.
An AI assistant that shows its source is one employees will check and trust. One that asserts without citing is one they will stop using after the first wrong answer. Every answer in a Banao knowledge system carries a source link.
New employees ask the most questions and have the fewest colleagues to ask. A knowledge assistant covering onboarding documents, process guides, and HR policies lets a new hire move at full speed from week one instead of waiting for a reply.
Most organisations do not have a single, tidy knowledge base. They have Confluence and SharePoint and a shared drive and PDFs emailed as attachments and Slack threads and years of helpdesk tickets. The first question we hear is 'do we need to clean all of that up first?' The answer is no — that is the build problem we solve, not a prerequisite you need to solve first.
We ingest from wherever the knowledge already lives, apply parsing and deduplication to handle contradictions, and attach metadata that lets retrieval filter by department, region, product line, or document date. The assistant learns the shape of your corpus and retrieves appropriately from it — including the awkward legacy formats.
We connect to your existing sources via their APIs — Confluence, SharePoint, Notion, Google Drive, Jira, Zendesk — and index from them directly. You do not need to move or reformat your knowledge to make it retrievable.
We budget for scanned PDFs, inconsistent naming, duplicate versions, and contradictory information. The build includes deduplication and conflict-surfacing so the assistant does not confidently answer from a document that was superseded two years ago.
We read permissions from your existing identity system and enforce them in the retrieval layer — so connecting the AI assistant does not create a new pathway for employees to access documents they were not authorised to read before.
The ingestion pipeline, the index, and the model calls all run inside your cloud environment or ours with region restrictions. Documents never leave the boundary your data-governance team has defined.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The deployments are live.
Majra (UAE)
We built an AI knowledge platform for Majra that retrieves from its own published corpus and answers in English and Arabic with the source attached — so users receive the organisation's documented position rather than a model's paraphrase of content it encountered during training.
Enterprise professional services firm (anonymized)
An internal assistant retrieves across ten years of policy documents, process guides, and resolved helpdesk tickets, answers with a cited source, and routes anything outside its corpus to a named expert — reducing the volume of questions that reached the team who previously had to answer them.
Banao's engineers query their own runbooks, architecture decisions, and technical documentation through an internal knowledge assistant built on the same RAG stack we sell. It answers from what we have actually written — processes, decisions, code explanations — with a source reference attached. A senior engineer's answer to a common question becomes accessible to the whole team without that engineer being pulled into every conversation.
Running our own knowledge assistant on a ~300-person engineering operation is not a demo. It is how we found out what breaks at scale: the chunking strategy that works for policy PDFs does not work for code comments; Slack message exports need different deduplication logic than wiki pages; onboarding is the use case that gets used most and where a wrong answer matters most. That experience is part of every build we deliver.
Some organisations invest in an AI knowledge assistant before the underlying knowledge problem is worth solving. We will tell you which situation you are in before you commit a budget:
Every knowledge base AI project we have seen stall has stalled for the same reason: the team approved a build off a demo using tidy test documents. We test against your actual corpus and your hardest real questions before any architecture is committed.
We ingest a representative slice of your knowledge corpus, test retrieval accuracy on your real questions, and hand back a scoped design, an accuracy baseline, and the ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We build the full ingestion pipeline, retrieval and re-ranking, grounding layer, access controls, eval harness, and the integration into the channel your employees use — deployed inside your cloud boundary.
We go live with a monitored rollout, tracking retrieval and faithfulness scores on real queries, updating the index as documents change, and improving coverage based on the questions the system cannot yet answer well.
What is enterprise knowledge base AI?
It is an AI assistant that reads your company's own documents — wikis, policies, runbooks, PDFs, ticket histories — and answers employee questions by retrieving the most relevant passage and presenting it with a source citation. The assistant answers from what you have documented, not from general internet knowledge.
How is this different from a search tool like Elasticsearch or Confluence search?
Search returns a list of documents that may contain the answer; an AI knowledge assistant returns the answer itself, drawn from the most relevant passage, with the source cited. Employees do not need to know which term the policy uses or how to refine a query — they ask in plain language and get a direct answer.
Does our knowledge base need to be cleaned up before you build?
No. Messy, inconsistent, and duplicate documents are the normal starting condition, and cleaning them is part of the build — not a prerequisite. We handle PDFs, scanned files, Confluence exports, SharePoint, Notion, Jira, and email threads. We surface contradictions so they can be resolved and flag stale documents rather than hiding them.
How do you make sure employees only see answers from documents they are allowed to read?
We read permissions from your existing identity provider and enforce them at the retrieval layer — before a passage is ever handed to the model. A user's query only matches documents they have access to, which means connecting an AI assistant does not create a new access pathway that bypasses your existing controls.
Can the assistant be wrong, and how do you handle that?
Yes. Any retrieval system will occasionally surface the wrong passage or find nothing and need to abstain. We build an abstention path so the assistant says 'I don't have that' rather than inventing, cite every answer so the user can verify it, and run a live eval suite so the accuracy rate is a number you can watch rather than a feeling after a demo.
Which tools and platforms can it plug into?
We build integrations with Slack, Microsoft Teams, intranet portals, and internal web apps. For knowledge sources we connect to Confluence, SharePoint, Notion, Google Drive, Jira, Zendesk, and document repositories via their APIs. The assistant lives where employees already work; they do not need to go to a new application.
Can it handle multiple languages?
Yes. We have built bilingual English-and-Arabic knowledge systems for GCC organisations, and we can handle any combination of languages your corpus includes. Retrieval runs in the query language, and the model responds in the same language the employee used to ask.
How long does it take to build and deploy?
A common path is a 2-week Discovery Sprint to baseline accuracy on your actual corpus, then a 6–10 week build, then a monitored rollout starting with a contained user group. Our ~300-engineer delivery bench means the project starts in weeks, not the months a fresh hire would take to come up to speed.
In 45 minutes we will tell you whether your knowledge corpus is sufficient to ground an AI assistant, and what it would take to get one into the tools your employees use every day.
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