Faster onboarding, less key-person risk
New hires step into a structured asset. Critical know-how stops walking out the door.
AI-native infrastructure for process & knowledge
Capture the unwritten.
SKILL is only as good as what's documented. Clarifeye turns the undocumented into context by using AI to interview your best people.
Compatible with the AI stack you already use
Claude
ChatGPT
Microsoft Copilot
Dust
Writer
Glean 
Clara turns what your people know
into usable context.
Clara sits with your people, reads your docs, and asks the follow-up questions no one ever wrote down. The output: structured, versioned knowledge your teams and your AI can use.
Clara, Interview agent
Talk to Clara like a top consultant, knows your field, reads anything fast, never forgets.
Document-aware interviews built for non-tech subject matter experts. Multiplayer across teams. Contradictions surfaced. Every change versioned.
Collaborative MCP-compatible Document-aware Context-aware
Knowledge Store
Clara
When a deviation is detected mid-batch, what's the very first check before you log it?
Whether the step is recoverable. If we can re-perform it within spec, the line handles it. If not, we stop the batch and escalate to QA immediately.
Clara
Got it, adding that as a branch point in the triage logic. Is the recoverability call always the batch owner's to make, or can the line supervisor override?
Overview
Playbooks
Mental map
Sources
High-level overview of the deviation handling process.
When a process deviation is detected, the first decision is recoverability. Recoverable deviations are handled at line level by the batch record owner; non-recoverable ones trigger immediate QA escalation and batch hold.
The boundary between minor and major deviations, and the conditions for a full CAPA versus a local correction, are rarely documented in SOPs. They live in the judgment of experienced operators and QA leads.
Escalation thresholds, recovery windows, and override authority vary by product family and shift, and are almost never written down.
How it works
From people's heads to your AI stack, and back.
Three steps, on a loop. Each step is useful on its own. Adopt one block at a time.
Step 01
Capture and structure knowledge by talking to Clara.
Clara, your always-on AI interviewer, asks questions, reads your docs, pulls out what isn't written down, builds consensus and reorganizes everything into structured, reusable knowledge. Many people contribute. Every change versioned.
Build your knowledge store with Clara
Clara
Let's map the deviation process. When something goes off-protocol on the line, what's the first call you make?
I check the batch record first. If the deviation happened before a critical control point, we can still recover. After that, it gets escalated immediately.
Clara
That recovery window, is there a defined time limit before it defaults to a full CAPA? I don't see it spelled out in the SOP you uploaded.
Chat with Clara or click the mic to talk
Step 02
Your AI asks. The knowledge store answers.
A team member asks Claude: "What's the protocol for a step 4.3 deviation?" Claude calls /clarifeye, retrieves the structured playbook, and answers with the exact escalation rule, grounded in what your expert actually said, not a guess.
Step 03
Improve continuously, with the loop closed.
Production signals, corrections, complaints, new edge cases, flow back into Clarifeye. Clara surfaces them and proposes the next conversation. A human reviews and validates before any change is promoted. The knowledge gets sharper every week.
Knowledge Store
1
gap detected
Clara
New signal from production
Claude was asked to classify a deviation as minor vs. major 7× this week, but no classification criteria exist in the playbook.
Clara
Users are asking Claude to classify deviations, but the minor/major threshold isn't documented. Want to schedule 15 minutes with Sophie to capture the criteria?
Why Clarifeye
Pick the seat you sit in.
Different problems, same platform. See what changes for your role.
Knowledge centralized, at scale
One place where how your company works actually lives. Not scattered across Slack, drives, and heads.
No technical setup. Just talk.
Your teams contribute by having a conversation with Clara. No wikis to maintain, no forms to fill.
Surface consensus, spot contradictions
Several people contribute. Where they agree becomes the source of truth. Where they don't, you decide.
One knowledge layer for all your AIs
Same asset feeds Claude, Copilot, ChatGPT, and internal systems. Stop rebuilding context per tool.
Ship AI 100× faster
Stop waiting on specs and gathering context. Your people put their knowledge in directly.
Architect instead of gather
Free your AI team from spec-hunting. Let them focus on what they're actually paid to do.
Compounding effects, no rework
Governed, centralized, reusable. Every new initiative builds on the last instead of starting from zero.
Stop hardcoding context
Logic lives in artifacts, not buried in brittle system prompts. Improve continuously, not endlessly.
Monitor across every AI frontend
Feedback from Claude, Copilot, ChatGPT, all captured in one place. End the endless UAT cycle.
Data lineage, built in
Every artifact versioned. Every source traceable. Production-ready governance from day one.
Universal skill builder
Build once, optimize for every frontend. MCP-native with progressive discovery and context optimization.
Built for the way you work.
Zero data training.
Your knowledge is never used to train models. Ever. Your IP stays your IP.
Model agnostic.
Decouple business logic from the AI layer to prevent vendor lock-in.
Secure & sovereign.
SOC2 type II compliant. VPC and hybrid deployment options. EU and US data localisation.
Your people know things no document captures.
Make that knowledge a shared asset, so your team works faster, onboards smoother, and your AI finally has the context it needs.