What is Clarifeye?

Every transformation project runs on knowledge that only lives in people’s heads: how a legacy system really applies a rule, how a process is actually run across sites, what an expert knows that no document says. Getting it out is slow. You wait weeks for interview slots, read thousands of pages by hand, reconcile two sites describing the same step differently, then consolidate everything into wikis and process maps, and start again on the next project. Clarifeye gives that knowledge one place to live. Clara, Clarifeye’s AI consultant, stays on the project from day one: she reads your documents and systems, prepares and runs the interviews, and keeps your process blueprints, business rules and playbooks current as the answers come in. The result is a body of knowledge your team and your AI tools can both query, and that stays with you when the project ends. It works as a loop across four steps.

​

1. Scope

Tell Clara what your project is about and who runs it. She proposes the artifacts to build, a project plan to get there, and shows what needs you today. As the project progresses she keeps proposing the next step: who to interview, which gap to close, which artifact to update.

​

2. Contextualize

Bring the context that already exists into one place. Upload documents, connect drives like SharePoint or Google Drive, and plug in tools such as Confluence, Granola or your IT systems through MCP. Clara reads it all and flags gaps and contradictions before anyone is interviewed.

​

3. Interview

Clara asks what nobody wrote down. She spots the gaps worth exploring, drafts the interviews, and runs them with the right people. Interviews are asynchronous, in each person’s own language, and every question is informed by what is already known, so contributors answer in their own words whenever it suits them.

​

4. Consolidate

Every answer lands in the right artifact. Interviews and sources fold back into the artifacts your teams work from, from the mental map to the playbooks. Artifacts are versioned, keep the evidence they were built from, and where two people disagree both versions are kept and flagged. You review and steer every change.

​

Keep it current and put it to work

How the knowledge is used depends on the use case. On a project like an ERP migration or an AI deployment, the store is the deliverable: the knowledge you hand to the integrator or the team building the agent, as the specification of how you actually work. When the store itself is the tool, you use it directly, inside Clarifeye with Chat for quick questions and Task for long-running work, or from Claude, ChatGPT, Copilot or a custom client through the Clarifeye skill, so the client applies your logic instead of generic answers. Knowledge also goes stale. As people use it, they surface corrections, gaps and drift. Clarifeye centralizes these as signals, and Clara turns them into the next step, sometimes a new interview. That is what keeps the knowledge getting better as the project runs.

​

How this maps to Clarifeye

Everything for one project lives inside a knowledge store, a self-contained body of expertise. The store’s navigation follows the loop:

• Home: where Clara proposes what to do next, based on what is happening in the store
• Work with Clara: scope the project, review what comes in, and update the artifacts together (Scope, Consolidate)
• Artifacts: the consolidated output (brief, project plan, mental map, general knowledge, playbooks, and any custom artifact your methodology needs)
• Sources: the documents, connected drives and external MCPs behind it (Contextualize)
• Inbox: the store’s email address, for mail and attachments sent to the store (Contextualize)
• Meetings: calls Clara joins to transcribe and surface gaps live (Interview)
• Interviews: capture knowledge from the people who hold it (Interview)
• Signals: everything flowing in that should update the store (Keep it current)
• Put knowledge to work: Chat and Task apply the knowledge to get real work done (Put it to work)

Two surfaces are easy to confuse: Work with Clara changes the knowledge, while Chat and Task use it as it stands today. See Work with Clara.

​

Next steps