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Reqode vs Brain Scanner

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

Reqode compared with Brain Scanner
DetailReqode logo Reqode Brain Scanner
What it doesFrom Requirements to Code. Verified. Review and pivot your AI coding agent in one place
StageNot given Not given
FoundedNot given Not given
Based inCY Not given
Pricing modelNot given Free
PricingStartup: Free for up to 3 users and 3 projects. Cloud Starter: $9 per user/month with unlimited users and projects. Cloud Pro: $19 per user/month with unlimited users, projects, 1,000 included AI credits per licensed user per month, and priority support. Self-hosted: Starting at $3,000 per instance/ Free plan with 900 free credits for new users. Contact for more usage credits or Founders plan details.
Key features
  • Specification Management: Manage connected requirements, data entities, APIs, interfaces, wireframes, and test cases in a versioned product model with traceable relations and branch-aware revisions
  • Change Management: Define each product change through a Change Request that connects goal, affected specifications, project branch, implementation impact, and linked issue
  • Architecture & Software Units Blueprints: Define subsystem architecture and software unit blueprints with implementation rules, responsibilities, dependencies, and file boundaries linked to product sp
  • AI Assistants: AI Analyst, AI Test Designer, AI Architect, and AI Product Expert for refining specifications, designing tests, developing architecture, and reviewing implementation
  • MCP for AI Coding Agents: Give coding agents branch-aware specifications, architecture guidance, software units, file traces, and Change Request context through MCP
  • Specification and Code Alignment Verification: Check that code matches specifications and follows architecture rules as the system evolves
  • Test Management: Organize test cases, test runs, execution, and result history
  • MCP for QA Agents: Give QA agents prepared test runs, cases, steps, and expected outcomes through MCP to record execution results
  • Session reviews with agent summaries, decisions, checks, blockers and file diffs
  • Project graph showing files, symbols and subsystems in one connected view
  • Change review with recorded updates and before-and-after details
  • Session context keeping goal, work and outcome connected
  • File browser with mapped folders and file details
  • Queued tasks scoped by type and source
  • Implementation estimates with evidence range and confidence
  • MCP integration with Claude Code and Codex
What makes it different
  • Preserves product context across tasks, agents, and code changes through a structured, verified product model
  • Reduces handoffs and coordination by delegating whole features with connected specifications and implementation scope
  • Prevents architecture drift by connecting implementation with subsystem architecture rules, unit guidelines, and component responsibilities
  • Built for complex products where a single feature touches connected data, APIs, interfaces, and business rules across multiple repositories and systems
  • Keeps product model current as specifications and implementation change using AI assistance and team review
  • Decision trail keeps context in one place instead of scattered in chat scrollback
  • Review agent changes and make notes without retyping recaps
  • Connected project graph shows how work fits together
  • Session recovery brings all context to the next session
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)28 no change since last checkNot checked yet no change since last check
Trust Flow (Majestic)180
Citation Flow (Majestic)3413
Referring domains (Majestic)6818

4 details filled in for both companies.

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