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Every organization runs on knowledge that lives in people’s heads and scattered across documents: how you qualify a deal, answer an RFP, review a contract, run a process. Clarifeye captures that knowledge, consolidates it into a single trusted source, and packages it so any AI client (Claude, ChatGPT, Copilot, or your own) can apply it with your logic instead of generic priors. It works as a continuous loop across four stages.

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1. Capture

Clara, Clarifeye’s agent, interviews your experts asynchronously, in their own language, adapting to whoever it’s talking to. It also reads everything you connect: uploaded documents and connected drives like Google Drive or SharePoint. You decide what knowledge matters and assign interviews to the people who hold it.

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2. Consolidate

Captured knowledge is messy. People disagree, sources conflict, and the most important things are often never written down. Clara resolves contradictions, fills gaps, builds consensus, and writes it all into a clean set of artifacts, the trusted and structured representation of your expertise.

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3. Make AI-ready

The knowledge is packaged into a form AI can consume. Your team installs the Clarifeye skill in their AI client, and from then on the client can draw on any of your knowledge stores through it, faithful to what was captured and connectable anywhere.

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4. Track signals

Knowledge goes stale. As people use it, in an AI client, in a task, or anywhere else, they surface corrections, gaps, and drift. Clarifeye centralizes these as signals, the single place where everything that should update your knowledge converges. Signals can trigger new interviews, closing the loop back to Capture.

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How this maps to Clarifeye

Everything you do in Clarifeye lives inside a knowledge store, one self-contained body of expertise for a team, product, or use case. The store’s navigation follows the same four stages:
  • Build & Improve
    • Interviews: capture knowledge from your experts (Capture)
    • Work with Clara: review interviews, tasks, and artifacts, and update the knowledge together (Consolidate)
    • Signals: everything flowing in that should update the store (Track signals)
  • Knowledge
    • Artifacts: the consolidated expertise (brief, playbooks, mental map, and more)
    • Sources: the documents and connected drives behind it
  • Use
    • Task: apply the knowledge to get real work done, like filling an RFP (Make AI-ready)
Two surfaces are easy to confuse. Work with Clara changes the knowledge (a policy shifted, a new source arrived), while Task uses the knowledge as it stands today to produce an output.

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Next steps

Quickstart

Build your first knowledge store and connect it to an AI client.

Core concepts

Knowledge stores, artifacts, signals, and how AI consumes them.