Product family
Product conceptEnterprise Knowledge Copilot
Answers your people can check, drawn only from what they are allowed to see.
A retrieval-augmented assistant over policies, manuals, tickets and wikis that cites its sources, respects existing permissions and says when it does not know.
Product concept. This is not a self-serve product yet. There is no public account, live dashboard or price list; we deliver it as a configured build and will show you the architecture, not a staged demo.
/ 01Overview
What it is and why it matters
Knowledge in most organisations is spread across SharePoint sites, wikis, ticket histories, PDFs and the heads of a few experienced people. Search returns documents; people want answers. The Enterprise Knowledge Copilot concept closes that gap with retrieval-augmented generation (RAG) done carefully.
Content is ingested with its access controls, split into passages that keep their headings and context, and indexed for both keyword and semantic search. When someone asks a question, the copilot retrieves only passages that person may see, writes an answer from them, and links every claim back to its source. If the sources do not support an answer, it says so.
This is a product concept delivered through our engineering practice. The value depends heavily on your content, so every engagement starts with a sample of real questions and a measured baseline.
/ 02Features
How it works
01
Permission-aware retrieval
Access control lists are carried from the source system into the index, and filtering happens before generation, not after.
02
Hybrid search
Keyword and vector retrieval are combined and re-ranked, which handles product codes and exact phrases as well as paraphrased questions.
03
Citations on every answer
Answers link to the passages they used, so readers can verify quickly and owners can fix the source when it is wrong.
04
Abstain and escalate
When confidence is low or sources conflict, the copilot says so and can route the question to a named subject-matter owner.
05
Content freshness
Incremental sync keeps the index aligned with source systems, and stale or superseded documents can be excluded by rule.
06
Measured quality
A test set of real questions with expected sources is scored on every change for retrieval accuracy and answer faithfulness.
/ 03Actual capabilities vs roadmap
What a first release includes, and what it does not
In scope for a first build
- Connectors for common document stores and wikis
- Hybrid retrieval with re-ranking and metadata filters
- Answers with inline citations and abstention
- Evaluation set and regression scoring
- Web, Teams or Slack front end
Roadmap, not yet delivered
- Structured data questions over approved tables
- Answer feedback loop into content-owner workflows
- Multilingual retrieval across mixed-language content
/ 04Use cases
Where it earns its keep
Policy and procedure questions
Staff ask how a policy applies to their situation and get the relevant clause, not a 40-page PDF.
Support agent assist
Agents see suggested answers drawn from resolved tickets and product documentation, with links to verify before replying.
Engineering knowledge
Runbooks, architecture decisions and incident reviews become searchable by question rather than by file name.
Related solutions: Internal Knowledge Search, Customer Support & CX, IT & Engineering
/ 05Integrations
Connects to the systems you already run
Listed systems are integration targets reached through their documented APIs. Their mention does not imply a partnership or endorsement.
Content sources
- SharePoint and OneDrive
- Confluence and Notion
- Google Drive
- Zendesk or ServiceNow knowledge bases
- File shares and PDF archives
Identity
- Microsoft Entra ID
- Okta
- Google Workspace
Front ends
- Web app
- Microsoft Teams
- Slack
- Embedded widget in internal tools
/ 06Security and governance
Designed for oversight
- Retrieval honours source permissions per user; there is no shared superuser index view.
- Data can stay in your region and your cloud account, including the vector index.
- Model providers are used under terms that exclude training on your data, or open-weight models are hosted privately.
- Query logs are retained for a period you choose and can be pseudonymised.
/ 07Demo
No live product demo yet
There is no live demo of Enterprise Knowledge Copilot because it is not a released product. Our demo lab includes a browser simulation on synthetic data that shows one of the core patterns behind it: “Cited retrieval over a policy library”.
/ 08Pricing or quote
Priced to your scope, never invented
Scoped per engagement. Cost drivers are content volume, number of sources, users and the model hosting option, all estimated before you commit.
/ 09FAQ
Questions about Enterprise Knowledge Copilot
Will it make things up?
Any language model can produce unsupported statements. We reduce that risk by restricting answers to retrieved passages, requiring citations, measuring faithfulness on a test set and making the copilot abstain when evidence is weak.
Do we have to clean up our content first?
Not entirely, but content quality sets the ceiling. Discovery usually identifies a small set of high-value sources to start with and flags duplicates and outdated material.
Can it answer from databases as well as documents?
Questions over structured data need a different pattern with controlled query generation. See the AI Analytics Copilot concept or our data engineering service.
Related services
- Service / buildEnterprise RAG & Knowledge AIRetrieval-augmented systems that answer from your content with citations, permissions and measured quality.
- Service / integrateData Engineering & AI PipelinesThe data foundations AI needs: reliable pipelines, clean models, governed access and lineage.
- Service / operateEvaluation, Guardrails & ObservabilityKnow how your AI systems behave: test suites, runtime guardrails, tracing and quality monitoring.
Explore Enterprise Knowledge Copilot with our team
A 45-minute conversation with an engineer: your workflow, the architecture, the risks and what a first version would include.