01
Every number traces to its evidence
All aggregate metrics now read from one canonical metrics layer. Every headline number on a report drills down to the exact AI responses behind it, with one win, loss, and tie definition everywhere.
June was about trustworthy numbers: one metrics spine behind every score, run-over-run changes you can inspect, and better paths from evidence to the next move.
Permalink: https://knitknot.ai/changelog/2026-06/
Release ledger
June was about trustworthy numbers: one metrics spine behind every score, run-over-run changes you can inspect, and better paths from evidence to the next move.

01
All aggregate metrics now read from one canonical metrics layer. Every headline number on a report drills down to the exact AI responses behind it, with one win, loss, and tie definition everywhere.
02
The new Measurement view shows what changed since the last run: mentions, entities, citations, and competitive outcomes. When a score moves, you can inspect the answers and sources behind the movement.
Measurement period
Compared with prior run
Previous
64
→
Current
72
03
KnitKnot’s MCP server gives supported assistants workspace-scoped access to competitive position, score trends, issues, playbooks, source intelligence, demand topics, prompts, and coverage.
04
Playbook detail was rebuilt around a tighter reader journey, stronger evidence, and clearer proof for important misrepresentations. Teams can generate or rebuild a playbook directly when they need to turn a finding into a next move.