The Platform

Optimize AI Coding for Your Codebase

Go from vibes to evals. Measure what works, optimize what doesn't, and prove the ROI.

BenchmarkingContext EngineTaskFlowMeasurement

Repository Benchmarking

Benchmark AI coding agents on YOUR code

Stop guessing which AI coding tool works best. Create evals from your actual task and PR history, then benchmark every AI coding agent and model against your real codebase.

  • Create evals from your actual task/PR history
  • Benchmark N AI coding agents × M models on YOUR code
  • Compare: Claude Code vs Cursor vs Copilot vs Codex vs Gemini
  • Track performance as context engineering changes
Agent & Model Leaderboard
your-repo
AgentModelE-1E-2E-3Avg
●Claude CodeOpus 4.696919494
Claude CodeSonnet 4.592889090
CursorSonnet 4.584898787
CodexGPT-5.3-Codex82798381
CursorComposer 1.580778280
CopilotGPT-5.278817678
Gemini CLIGemini 375787074
Context v3 ↑ 4.2% avg vs v23 evals · 7 combos

Context Engineering Engine

Context that improves automatically

Your AI coding context should get smarter with every change. ContextBridge automatically extracts learnings from pull requests, repository changes, and code reviews, then evaluates updates against your evals.

  • Extract learnings from pull requests and repository changes
  • Capture recurring review patterns and rework signals
  • Evaluate context changes against your codebase
  • Agent-agnostic: works across Claude, Cursor, Copilot
Context Engine+4 learnings
## Code Style
- Use TypeScript strict mode
- Prefer named exports
+- Always run lint before committing generated code
+- Prefer composable hooks over HOCs for shared state
 
## Architecture
- Server components by default
+- Use server actions for mutations, not API routes
+- Colocate data fetching with the component that needs it
Synced to:
CLAUDE.md.cursorrulesAGENTS.mdGEMINI.mdcopilot-instructions.md

TaskFlow

Paved road from task to high quality PR

Replace ad-hoc prompting with a structured workflow. ContextBridge orchestrates planning, implementation, and PR preparation with precision feedback loops at every stage.

  • Three-phase workflow: Planning → Implementation → PR
  • Human-in-the-loop checkpoints at each phase
  • Precision review: comment on every line, not just a single input box
  • Integrates with Linear, Jira, GitHub
TaskFlow
Task-247
Planning
AI Planning
Plan Review
Sync Plan
Implementation
AI Implementation
AI Code Review
Local Review
Prepare PR
Create Commits & PR
Review PR Details
AI Human
Phase 2 of 3

Velocity & Quality Measurement

Prove the ROI with real data

Track what matters: task completion velocity, PR quality, rework rates, and team-level performance. Know exactly what's working and what's not.

  • Task completion velocity tracking
  • PR quality metrics (comments, rework, complexity)
  • Team-level dashboards
  • Identify what's working, what's not
AI Coding Impact
Last 30 days
Delivery Velocity
Tasks/Dev/Week
8.3
was 4.1
Cycle Time
2.1 days
-34%
Time to Merge
3.2 hrs
-47%
Code Quality
First-pass Approval
87%
+16 pts
AI Slop Rate
12%
-50%
Post-merge Rework
6%
-40%
AI Contribution: 64% of code
2x dev velocity

Integrations

Works with your existing stack

ContextBridge layers on top of your existing AI coding tools and connects to your project management and source control systems. Your team keeps using the tools they already know.

AI Coding Tools

Claude Code
Cursor*
Copilot*
Codex*
Gemini CLI*

Project Management

Linear
Jira
Asana
Shortcut

Source Control

GitHub
GitLab*

* Coming soon

Ready to find out if you're getting 10x?

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