Debug vision models in hours, not weeks

Know exactly what to fix before you waste another experiment.

14-day trial • No credit card required

Status Quo

The Problem

What you do today

  • Step 1Generate GradCAM manually2-4 hours
  • Step 2Stare at heatmaps trying to figure out what's wronghours to days
  • Step 3Try fix #1: Add more data → doesn't help3 days • $5K compute
  • Step 4Try fix #2: Change architecture → doesn't help4 days • $6K compute
  • Step 5Try fix #3: Adjust loss function → barely helps3 days • $4K compute
  • Step 6Finally figure out it's a lighting augmentation issueweek 3

2-3 weeks wasted, $15K+ in compute, delayed deployment.

What you do with Incite

  • Add 3 lines of code to your training script
  • Incite auto-generates GradCAM, LIME, and error analysis
  • Get instant diagnosis: Model focusing on background texture in 23% of false negatives
  • Try the recommended fix: texture-invariant augmentation → immediate improvement

2 days, $2K compute, problem solved.

Quick Setup

How It Works

Get started in minutes with just three lines of code

Add Our SDK

2 Minutes

Add three lines of code to your training script and you're ready to go.

from incite import CVDebugger

debugger = CVDebugger(project="defect-detection")

@debugger.track_experiment
def train_model():
    # your existing training code
    train(model, data)
Core Features

What You Get

Automated interpretability and AI-powered recommendations built specifically for computer vision

Automated Interpretability

GradCAM, LIME explanations, error clustering, and training dynamics—all generated automatically. No more writing custom visualization code for every experiment.

  • GradCAM / GradCAM++ attention maps
  • LIME explanations for failed predictions
  • Automatic error clustering by class and confidence
  • Gradient flow and activation statistics

AI-Powered Recommendations

We don't just show you pretty pictures—we tell you what to do. Each recommendation includes priority, confidence score, expected impact, and reasoning.

  • Prioritized fix recommendations (HIGH/MEDIUM/LOW)
  • Confidence scores (0-100%)
  • Expected impact estimates
  • Reasoning based on similar debugging sessions

Built for Computer Vision

Deep CV expertise, not generic ML interpretability. Understands CV-specific failure patterns and manufacturing QI issues.

  • CV-specific failure pattern recognition
  • Optimal interpretability methods per architecture
  • Manufacturing quality inspection expertise
  • Support for classification, detection, segmentation
Pricing

Simple, Transparent Pricing

Choose from our tiered plans based on your team size and needs

Starter

$1,000per month

Perfect for small teams getting started

  • Up to 5 engineers
  • Unlimited experiments
  • All interpretability features
  • Community support
  • 14-day free trial
MOST POPULAR

Growth

$5,000per month

For teams scaling their CV operations

  • Up to 15 engineers
  • Advanced error analysis
  • Priority support
  • Quarterly success reviews
  • Custom integrations

Enterprise

Custompricing

For large organizations with specific needs

  • Unlimited engineers
  • On-premise deployment
  • Custom integrations
  • Dedicated success manager
  • SLA guarantees

Join the waitlist to get early access when we launch

FAQ

Frequently Asked Questions

Everything you need to know about Incite

Stop Guessing, Start Fixing

Join engineering teams debugging computer vision models 5-10x faster

14-day trial • No credit card required • Cancel anytime