An enterprise AI knowledge assistant that searches SharePoint, Confluence, Google Drive and your ticketing history, answers in plain language, cites the exact passage, and applies each user's existing permissions before a single document is read.
An enterprise AI knowledge assistant is a chat interface that answers employee questions using retrieval-augmented generation over internal content such as SharePoint, Confluence, Google Drive and ticket history. It retrieves only documents the user is already permitted to open, then generates an answer grounded in those passages. It outputs a short answer with citations, and says it does not know when the sources do not support one.
Policies live in SharePoint, runbooks in Confluence, proposals in Drive and the real fixes in old tickets. People ask a colleague instead, wait for a reply, and sometimes act on an outdated version.
of digital workers struggle to find the information they need to do their jobs effectively (Gartner survey of 4,861 employees in the US, UK, India and China).[1]
used on average by a digital worker up from 6 in 2019, per the same Gartner survey.[1]
of the workweek spent looking for internal information or tracking down colleagues who can help (McKinsey Global Institute).[2]
Public chatbots fill the gap when staff paste internal documents into tools IT has not approved.
Connectors index content with its access control lists, and sync changes and permission updates on a schedule.
Hybrid keyword and vector retrieval finds the passages; the model answers only from them and links each claim.
Every query, retrieved document and answer is logged, and answer quality is tested against a question set you own.
We pick one team, such as IT service desk, operations or sales, connect two or three sources, and agree the metric: answer accuracy on a gold question set.
Content is indexed in your cloud tenant with access controls. Your security team tests that restricted documents stay restricted.
20 to 100 users ask real questions in Teams or Slack. Thumbs-down answers are reviewed daily and fixes applied.
We score accuracy, citation correctness and permission tests, plus usage and time saved reported by users, then agree next steps.
| Criterion | Intranet search | Off-the-shelf copilot | Stratgik build + run |
|---|---|---|---|
| Answer format | List of links | Chat answers with citations | Chat answers with passage-level citations |
| Sources | One platform | Strong inside its own suite | SharePoint, Confluence, Drive, tickets and databases together |
| Permission handling | Native | Native within the suite | Inherited from each source and tested in the pilot |
| Answer quality measurement | None | Limited visibility | Gold question set scored weekly |
| Model and hosting choice | Not applicable | Vendor's choice | Your cloud tenant, your model provider |
| Best fit | Finding a known document | Content mostly in one vendor suite | Mixed sources, regulated content, custom workflows |
Security research shows AI incidents cluster where access controls and governance are missing. A sanctioned assistant with permission checks is a control, not only a productivity tool.
Enter the people who would use it and how long they spend hunting for information. The share of search time saved is an assumption measured with pilot users.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
21-day pilot, one department, up to 3 sources and 100 users
per month, up to 1,000 users
per month, organisation-wide
Usage (model tokens, vector storage, cloud hosting) billed at cost in your own cloud account where possible; no training on your data; taxes excluded. GBP and AED prices are indicative conversions from USD.
An enterprise AI knowledge assistant respects permissions by indexing each document with its access control list and filtering retrieval by the signed-in user's identity before the model sees anything. If a user cannot open a file in SharePoint, Confluence or Drive, its content is never retrieved for their question. We test this in the pilot with your security team using restricted documents and test accounts.
RAG, or retrieval-augmented generation, is a method where the system first retrieves relevant passages from your documents, then asks a language model to answer using only those passages. For internal knowledge it matters because answers stay grounded in current company content, can cite their sources, and do not require training a model on your data.
No. We deploy using enterprise model services such as Azure OpenAI, Anthropic models via AWS Bedrock, or Google Vertex AI, whose business terms state customer data is not used to train their models. The index and logs sit in your own cloud account. Your security team reviews the architecture and data flows before any content is indexed.
Accuracy depends mostly on content quality and retrieval, not the model. We measure it on a gold set of real questions from your employees, scored for correctness and correct citations, before and after launch. When sources conflict or are missing, the assistant is set to say so rather than guess, and those gaps are reported to content owners.
Microsoft 365 Copilot and Glean are good choices when your content and workflows fit their products. A custom build makes sense when you need sources outside those suites, a specific model or hosting region such as a UAE or UK data residency requirement, domain-specific answer rules, or measured evaluation you control. We will tell you if an off-the-shelf tool fits better.
A secure RAG assistant for one department runs in production within 21 days: sources and security scoped in the first days, permission-aware indexing in week two, and live pilot users in week three. Organisation-wide rollout with more sources, languages and teams typically takes a further one to three months, driven by security reviews and content clean-up.
A 30-minute call to pick the department and sources, then a fixed-price 21-day pilot scored on answer accuracy and permission tests.