Business Case

Knowledge Management

Knowledge scattered across wikis, servers, and people's heads — lost every time someone leaves.

Sound familiar?

Check off what you recognise — CLUE is listening.

I'm listening …

Especially common in:Manufacturing & MachineryEnergy & UtilitiesPharma & Life Sciences

The Challenge

Your company is full of knowledge — spread across wikis, SharePoint, Teams chats, file servers, and the heads of experienced employees. Whenever someone has a question, the search begins: who might know this? Where is it documented? In which document? Hours are lost.

How Aclue solves it

Our knowledge management agent is built on RAG technology (retrieval-augmented generation). It searches across all your knowledge sources — documents, wikis, databases — and answers questions in natural language. With source citations. In seconds.

Your data stays with you. No external training, no data leakage. We deploy the model on your own hardware, in your own cloud, or with your cloud provider of choice — you retain full data sovereignty. We align data protection and processing requirements with your responsible stakeholders.

Flexible integration: whether Microsoft Teams, your intranet site, Slack, or an API — the agent is embedded wherever your employees work. Needs analysis, custom development, integration into your existing landscape — all from one partner.

Reliable answers need clear boundaries

Respect permissions: Before integration, we establish which sources and documents each person may access. Search and answers must respect those boundaries; connecting a source does not authorize access for every user.

Keep knowledge current: For each source, we agree update and deletion processes and ownership. Changed or removed documents must also be reflected in the search index.

Make uncertainty visible: Citations help people verify answers but do not guarantee correctness. When authorized sources are insufficient, the agent should explain the limitation and refer the user to a responsible contact instead of inventing an answer.

Evaluate quality: Agreed questions, expected sources and examples with insufficient evidence form the test set. Before releases, we check answer quality and access restrictions against it. Scope and acceptance criteria are defined in the project.

Evaluate value together

We compare search time, answer quality and repeated questions before and after the pilot.

Illustrative calculation, not a customer result: If 50 employees each save 16 hours per year, that is 800 hours. At an assumed internal cost of EUR 50 per hour, the theoretical capacity value is EUR 40,000. This is not a guaranteed cash saving. Project, licence and operating costs, as well as usable capacity gains, determine the business case.

Concrete applications

  • 01

    Service agent

    answer customer questions instantly with current product knowledge

  • 02

    HR knowledge agent

    automatically answer employee questions about benefits, processes, and policies

  • 03

    Technical support

    make error databases and solution documentation searchable

  • 04

    Onboarding

    new employees find answers on their own

Built with: Enterprise AI Integration →

Where this already works

Hamburger Energiewerke

Hamburger Energienetze, Hamburg Messe — Energy & Utilities

Knowledge Management at Energy Utilities

Result: 80% less time spent searching for technical documentation, answers in seconds instead of hours

More projects on this topic →

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