Use case
The same bugs keep appearing.
No one connects the dots.
Recurring bugs are a symptom of lost context. Kleio surfaces patterns across sprints by linking captures to their decision history.
The cost of pattern blindness
- The same class of bug gets filed multiple times because nobody sees the connection across sprints.
- Each occurrence is treated as a one-off fix instead of addressing the underlying decision that caused it.
- Institutional memory lives in individual engineers' heads — when they're unavailable, patterns go unnoticed.
- Bug trackers show symptoms. They don't show the decision patterns that produce them.
- AI-generated code introduces subtle regressions that only surface weeks later.
How Kleio helps
Semantic deduplication
When a new work-item capture matches an existing backlog item, Kleio links them instead of creating duplicates — making recurring patterns visible.
Signal links across time
Captures, decisions, checkpoints, and PRs are all connected. When the same area of the codebase keeps generating work items, Kleio surfaces the trail.
Backlog pattern analysis
Filter and search your backlog to see which areas generate the most follow-up work. Turn reactive bug-fixing into proactive architectural decisions.
Decision context on each bug
Every backlog item carries the context of when and why it was captured. Fixing a bug with its decision history means fixing the root cause, not just the symptom.
See it in action
An agent plans a migration. Kleio captures every signal along the way.
Agent Plan
Task: Migrate auth from Firebase to Clerk
- 1.Scaffold Clerk provider and session hooks
- 2.Replace Firebase login/signup flows
- 3.Update API middleware for Clerk JWT validation
Press Build to run the capture demo.
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