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 EngineOrchestratorMeasurement

Repository Benchmarking

Benchmark AI agents on YOUR code

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

  • Create evals from your actual task/PR history
  • Benchmark N 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 496919494
Claude CodeSonnet 492889090
CursorSonnet 484898787
CopilotGPT-4o78817678
CodexGPT-5.282798381
CursorComposer80778280
Gemini CLIGemini 3.075787074
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 session. ContextBridge automatically extracts learnings from coding sessions, PR feedback, and code reviews — then evaluates changes against your evals.

  • Extract learnings from coding sessions automatically
  • Learn from PR feedback, comments, rework
  • 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

Agent Orchestrator

Paved road from task to merged 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
Agent Orchestrator
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?

Request a demo and see how ContextBridge can optimize your AI coding workflow

or
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