Knowledge Management
Organizational Knowledge, Instantly Accessible
AI systems that answer questions from your policies, documentation, and institutional knowledge—with sources and appropriate uncertainty.
The Knowledge Problem
Your organization has answers to most questions employees ask—scattered across wikis, SharePoint, Slack history, email threads, and the memories of senior staff.
New employees spend weeks figuring out how things work. Experienced staff answer the same questions repeatedly. Important information gets lost when people leave. Knowledge exists but isn't accessible.
Traditional search helps but doesn't solve the problem. Employees need answers, not lists of documents to read.
What We Build
When the system doesn't know, it says so clearly rather than inventing plausible-sounding responses.
Intelligent Q&A Systems
Agents that understand questions and provide answers—not just retrieve documents. The system synthesizes information from multiple sources and explains its reasoning.
Source Integration
Access knowledge wherever it lives: Confluence, Notion, SharePoint, Google Docs, Slack, Teams, HR platforms, technical wikis, PDFs, and legacy content.
Citation and Verification
Every answer includes sources. Users can verify information against original documents. Confidence indicators show when answers are solid versus tentative.
Continuous Learning
Feedback mechanisms capture when answers miss the mark. Frequently asked questions without good answers surface for content creation.
Typical Impact
The RAG Challenge
Retrieval-Augmented Generation (RAG) is the foundation of knowledge management AI—but naive RAG implementations disappoint:
- Retrieval failures: The system has the answer but can't find the relevant documents.
- Synthesis errors: The system finds relevant documents but misinterprets them.
- Citation gaps: The answer seems right but sources don't support it.
- Hallucination: The system invents plausible answers when knowledge doesn't exist.
We engineer RAG systems that minimize these failures through hybrid retrieval, reranking, careful prompt engineering, and evaluation harnesses that catch failures before deployment.
Use Cases
Employee Self-Service
Answer questions about policies, benefits, procedures, and organizational information. Reduce load on HR, IT, and administrative teams.
Customer-Facing Knowledge
Product information, troubleshooting guides, and documentation access for customers. Consistent answers across support channels.
Technical Documentation
Engineering knowledge: architecture decisions, API documentation, runbooks, and tribal knowledge captured in searchable form.
Onboarding Acceleration
New employees get answers immediately rather than waiting for colleagues. Faster ramp to productivity.
Prompt Engineering for Knowledge
Knowledge system prompts require:
Hallucination Prevention
When information isn't in the knowledge base, the agent says so. No making up plausible answers.
Source Fidelity
Answers accurately represent what sources say. No creative interpretation or extrapolation beyond the content.
Appropriate Uncertainty
Confidence varies based on source quality and coverage. The agent communicates uncertainty clearly.
Tone Calibration
Answers are helpful and approachable—not formal legalese or robotic responses.
Ready to make your organizational knowledge work harder?
We'll assess your knowledge landscape, identify high-impact opportunities, and show you what intelligent Q&A can deliver.