Use Case

Enterprise Knowledge Base Search with AI

Employees waste hours every week searching for information that exists somewhere in the organization but is impossible to find. We build AI retrieval systems that answer natural language questions accurately, with sources cited.

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The Challenge

Enterprise knowledge — policies, procedures, product documentation, past decisions, research reports — is scattered across SharePoint, Confluence, email, Slack, and shared drives. Employees either cannot find what they need, settle for outdated information, or ask a colleague who then has to find it for them. The knowledge exists. The organization simply cannot access it efficiently.

Our Approach

We build Retrieval-Augmented Generation systems that connect to your existing knowledge sources, index the content, and enable natural language question-answering across the full corpus. Answers come with citations to the source documents so employees can verify accuracy. The system handles follow-up questions, and every interaction informs ongoing knowledge gap identification.

How We Do It

Knowledge Source Indexing

We connect to your existing document stores — SharePoint, Confluence, Google Drive, Notion, internal wikis, PDF repositories — and build a semantic index of the full content. Documents are chunked and embedded with metadata that enables precise retrieval. Existing access permissions are respected in the index.

Natural Language Search Interface

Employees ask questions in plain language through a chat interface, Slack bot, or embedded search widget. The system retrieves the most relevant document passages, synthesizes an accurate answer, and cites the specific source documents. Multi-turn conversations allow for follow-up and clarification.

Access Control and Permission Enforcement

The system enforces your existing document permissions at query time — employees only get answers from documents they have access to read. Sensitive information stays within its intended access boundary even when the knowledge base spans multiple systems.

Gap Analysis and Content Improvement

Questions that cannot be answered confidently are logged and surfaced to knowledge owners for content creation. Frequently asked unanswered questions identify the highest-priority gaps in your documentation. The knowledge base gets more complete over time as a result of use.

What You Get

✓Employees find accurate answers to internal knowledge questions 5x faster than keyword search

✓New employee ramp time decreases 30% as onboarding information becomes immediately accessible

✓Knowledge owner inquiry volume drops 40% as self-service answers become reliable

✓Documentation gaps identified and addressed systematically based on actual usage patterns

Technology Stack

Claude 3.5 SonnetOpenAI EmbeddingsPineconeLangChainSharePoint APIConfluence API

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Industries We Serve

Financial Services HR Operations

Frequently Asked Questions

How do you handle documents that are outdated or contradict each other?+

We include document metadata — creation date, last modified date, owner — in the retrieval context so the AI can surface the most recent version and flag contradictions when they exist. For organizations with significant documentation debt, we offer a content audit as part of the scoping process to identify the most problematic areas before indexing.

Can the system access our SharePoint and respect existing permission groups?+

Yes. We integrate with SharePoint, OneDrive, and Azure Active Directory to query documents and enforce permissions at search time. A user who cannot read a document in SharePoint will not receive answers sourced from that document. This is a non-negotiable design requirement, not an optional feature.

What happens when the AI gives a wrong answer?+

Every answer includes citations to the source documents it relied on, which allows users to verify accuracy immediately. We also build a feedback mechanism into the interface so users can flag incorrect answers. Flagged answers are reviewed, the source documents are corrected if needed, and the index is updated. Over time, accuracy improves as poor-quality source content is identified and fixed.

How much content can the system handle, and is there a limit to the knowledge base size?+

There is no practical upper limit for enterprise use cases. We have built knowledge bases indexing hundreds of thousands of documents. Larger indexes require more infrastructure, which affects cost. During scoping, we assess your document volume and design the infrastructure accordingly. Query response time stays under 3-5 seconds regardless of index size.

Ready to build this for your team?

We take this from concept to production deployment. Usually in 3–6 weeks.

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