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Knotr vs RELAI

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Knotr compared with RELAI
Detail KnotrRELAI logo RELAI
What it doesYour voice and skills in every AI tool you use The continual learning engine for AI agents
StageStartup Not given
FoundedNot given Not given
Based inUS Not given
Pricing modelFreemium Freemium
PricingFree: 1 Profile, 10 Skills, 50 Documents, 10 Artifacts, 1 Style Guide from Starter Pack, 100 Hosted Chat AI credits/month. Pro: $24/mo or $20/mo billed annually, 5 Profiles, 75 Skills, 500 Documents, 75 Artifacts, 1 Style Guide, 1,000 Hosted Chat AI credits/month. Max: $49/mo or $40/mo billed annual RELAI Free: $0. RELAI Enterprise: Custom pricing.
Key features
  • Profiles to define voice, tone, and judgment
  • Style Guides to enforce writing rules
  • Knowledgebases to store trusted documents and notes
  • Skills to package repeatable workflows
  • Artifacts for versioned, shareable outputs
  • Hosted Chat with custom runtime
  • MCP integration with external tools
  • Team workspaces with shared knowledge
  • Skill Catalog for discovery and import
  • Collections to group and present artifacts
  • Capture signals from code, requirements, and production traces
  • Build and maintain EvalsWiki as versioned behavioral knowledge
  • Find missing eval coverage and unclear expectations
  • Generate complete evals with scenarios, environments, and evaluators
  • Optimize harness across prompts, tools, workflows, and code
  • Multi-objective optimization for quality, cost, latency, and KPIs
  • Model selection, migration, and routing with evaluation
  • Regression-controlled improvement with evaluation evidence
  • Versioned storage in repository with pull requests
What makes it different
  • Output sounds like you, not generic AI, because every tool reads the same profiles and style guides
  • Stop re-uploading docs and rewriting instructions for each new tool
  • Individual voice, shared standards on teams
  • Built-in changelogs instead of Git for managing skills
  • Works with any MCP-compatible AI client without vendor lock-in
  • Turns failures into replayable learning environments
  • Prevents regressions by keeping previous evals in the loop
  • Integrates with existing observability stacks and traces
  • Preserves business intelligence versioned in repository
  • Adapts harness to model changes without redefining use cases
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)2 no change since last check23 no change since last check
Trust Flow (Majestic)413
Citation Flow (Majestic)2525
Referring domains (Majestic)2779

5 details filled in for both companies.

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