AI Platform • Code Evaluation • Workflow Diagnostics

Understand AI-generated code

Compare. Diagnose. Improve.

An AI-native platform for evaluating and refining LLM-generated workflows with functional abstraction and evidence-first diagnostics.

Start for free
SUS 81.3
DOCKER SANDBOX
POSTGRES/NEON

AI ships code fast.
Verification is still manual.

The generation gap is solved. The verification gap is not.

Multiple models, no clear comparison

01

Different models produce different implementations. Existing tools provide outputs — not evidence.

Manual verification is expensive

02

AI rewrites large code blocks, but developers still review line by line. Verification becomes the new bottleneck.

Passing tests ≠ quality code

03

Readability, efficiency, and trust remain invisible. Current evaluation focuses only on functional correctness.

Core Capabilities

A platform for understanding, comparing, diagnosing, and improving AI-generated code.

Understand the Code

Turn raw generations into structured functional blocks to reason about behavior fast.

TRANSFORMTRAIN

Compare Across Models

See structural differences across models without reading full code line-by-line.

ClaudeGPT-4Gemini

Multi-dimensional Evaluation

Evaluate correctness, readability, efficiency, and reusability—beyond tests.

0.82 SCORE

Guided Refinement

Apply targeted fixes with guided prompts powered by diagnostics.

REFINE

Experiment History

Track runs, settings, and outcomes—revisit insights without repeating work.

WORKFLOW

Easy to start.

Follow a guided flow to generate results in seconds —
with annotation and diagnosis you can toggle anytime.

Product UI

Enter a prompt

Describe your data science task. CGM Comparator extracts intent before you compare outputs.

FEATURE DEEP DIVE

See how CGM Comparator
works in practice

Explore how the system analyzes, compares, and improves AI-generated workflows.

01 // ANALYSIS

Compare implementations at a functional level

Understand how different models solve the same task — not just what they output.

  • Structural comparison across models
  • Functional block alignment
  • Multi-dimensional evaluation signals
Compare View — 3 Candidates
Live
GPT-4o
OpenAI
Claude 3.5BEST
Anthropic
Gemini 1.5
Google
Correctness0
Efficiency0
Readability0
Coherence0
Correctness0
Efficiency0
Readability0
Coherence0
Correctness0
Efficiency0
Readability0
Coherence0
PASSED
47/50
1.2s
PASSED
50/50
0.9s
PARTIAL
43/50
1.8s
02 // INTELLIGENCE

Reveal workflow structure and intent

Automatically segment generated code into functional units and expose execution logic.

  • Functional abstraction beyond syntax
  • Structured annotation pipeline
  • Clear execution flow
Annotation View — Functional Segments5 blocks detected
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
INPUT PARSING
def process_pipeline(filepath: str,
target: str = 'label'):
df = pd.read_csv(filepath)
df = df.dropna(subset=[target])
DATA TRANSFORM
scaler = StandardScaler()
features = [c for c in df.columns if c != target]
X = scaler.fit_transform(df[features])
y = df[target].values
CORE LOGIC
clf = RandomForestClassifier(
n_estimators=100, max_depth=5,
random_state=42)
clf.fit(X_train, y_train)
VALIDATION
score = clf.score(X_test, y_test)
assert score > THRESHOLD, f'Low: {score:.2f}'
OUTPUT
return {'model': clf, 'score': score,
'scaler': scaler, 'features': features}
INPUT
DATA
CORE
VALIDATION
OUTPUT
Coverage 100%
03 // ITERATION

Improve results with structured guidance

Turn diagnosis into actionable steps and refine code iteratively.

  • Evidence-based diagnosis
  • Guided improvement steps
  • Iterative refinement loop
Refinement History — Version Tracking
3 versions
v1
v1.0Initial Generation
GPT-4obaseline
0

Functional but high cyclomatic complexity. Nested loops, unclear naming.

correctness
86
readability
61
efficiency
69
v2
v1.1Readability Refinement
Claude 3.5guided
0
+11 pts

Logic restructured into named functions. Depth reduced from 5→2.

Readability: +23pts
correctness
88
readability
84
efficiency
76
v3
v1.2Efficiency OptimizationBEST
Claude 3.5surgical edit
0
+10 pts

Vectorized inner loop. Memory cut 40%. All 50 test cases pass.

Efficiency: +18pts
correctness
98
readability
86
efficiency
94
PLATFORM ARCHITECTURE

How the platform works end-to-end

A full-stack loop from prompt to evidence, diagnosis, and continuous improvement.

Interaction Layer

Where developers interact with generated code and insights.

Prompt interface
Compare view
Annotation panel
Refinement controls

Evaluation Engine

Multi-dimensional analysis and functional understanding of generated code.

Functional abstraction
Semantic analysis
Performance evaluation
Cross-model comparison

Execution + Storage Infrastructure

Sandbox execution, result persistence, and history tracking.

Sandbox execution
Database storage
Experiment history
Model adapters
USE CASES

Real scenarios. Real decisions.

From vendor selection to production review — CGM Comparator fits into the moment when AI code quality actually matters.

For engineers evaluating LLM vendors

Know which model to use before you commit.

  • Run the same prompt across GPT-4o, Claude, and Gemini simultaneously
  • Score each output on Correctness, Readability, Efficiency, and Coherence
  • Pick the winner with data — not gut instinct
compare — 3 models
LIVE
GPT-4o
Claude 3.5
Winner
Gemini 1.5
Correctness
84
Correctness
95
Correctness
71
Readability
78
Readability
91
Readability
68
Efficiency
72
Efficiency
88
Efficiency
74
Coherence
80
Coherence
94
Coherence
70
Overall
79
Overall
92
Overall
71

Engineering Impact

Measured improvements from system design decisions.

+0.00pp
User comprehension

Structured function-level annotations generated by a multi-step agent pipeline help users reason about model behavior across alternative implementations.

validated through controlled user study
0%
Faster task completion

Interactive comparison workflows reduce manual verification effort and accelerate decision-making under real usage constraints.

measured in real task scenarios
0%
Evaluation reliability

Validated against HumanEval benchmarks.

<0ms
Sandbox isolation latency

Cold-start execution environment.

0+
Model coverage

OpenAI, Anthropic, Google, Cohere and more.

0%
Logic coverage

Static + dynamic execution analysis.

Static + Dynamic Analysis
Reproducible Execution
Artifact Tracking
Experiment History
Multi-model Pipeline
Sandbox Runtime
PRICING

Build trust in AI code,
at any scale.

Start for free. Upgrade when your team needs structured AI code evaluation at production scale.

Free
Free

Explore the platform. Validate your first AI-generated workflows.

5 comparisons / day
2 model backends
Correctness & Readability metrics
Basic execution sandbox
7-day history
Community support
Most Popular
Pro
$49/ month

For engineers who ship AI-assisted code and need structured verification daily.

Unlimited comparisons
15+ model backends (GPT-4o, Claude, Gemini, Deepseek…)
All 4 evaluation dimensions
Functional annotation engine
Guided refinement pipeline
Full history & trend analysis
REST API access
Priority email support
Team
$149/ seat / mo

For engineering teams evaluating models and agents at scale.

Everything in Pro
Collaborative workspaces
Shared experiment history
Team-level analytics dashboard
Admin console & audit log
SSO & role-based access
Dedicated Slack channel
Priority support & uptime SLA

Full plan comparison

Feature
Free
Pro
Team
Model backends
2
15+
15+
Daily comparisons
5
Unlimited
Unlimited
Evaluation dimensions
2 / 4
4 / 4
4 / 4
Annotation engine
Guided refinement
REST API access
History retention
7 days
90 days
Unlimited
Collaborative workspaces
Admin console
SSO & RBAC
Support
Community
Email
Dedicated + SLA
Need custom models, on-prem deployment, or enterprise contracts? Contact us
98.2%
Evaluation reliability
Validated against HumanEval benchmarks
< 250ms
Sandbox cold-start
Isolated Docker gVisor execution
+22.65pp
User comprehension
Measured in controlled study
15+
Model backends
GPT-4o, Claude, Gemini, Deepseek, and more
GET STARTED

Ready to verify
what AI writes?

Start for free. No credit card. Bring your first AI-generated code in under 60 seconds.

Docker sandbox
SOC2-ready
No vendor lock-in
API-first