Debug Foundation Models in Minutes, Not Months
Visualize feature spaces layer-by-layer with spectral clustering. Identify failing layers. Swap in better features without retraining. Built on 25 years of research from the inventors of Normalized Cuts.
● Feature clusters forming
! Layer 12: Degradation detected
The Problem
- Foundation models fail in production
- Hallucinations, bias, edge case failures
- Traditional fix: retrain everything for $2M and 3 months
The Old Way
- Blind debugging: add more data, hope it helps
- Full model retraining: expensive and slow
- No insight into WHERE models break
"We spent 6 months adding data, only to find the issue was in layer 14."
Debug models with precision, not guesswork
Our three-step process gives you complete visibility into your model's internal representations.
Visualize
See inside your model layer-by-layer
NCut spectral clustering reveals semantic structure. Identify exactly where features degrade.
- Layer-by-layer feature visualization
- Semantic cluster identification
- Real-time analysis pipeline
Debug
Track feature evolution across layers
Compare model architectures side-by-side. Batch analysis across edge cases.
- Side-by-side architecture comparison
- Edge case batch analysis
- Automated degradation detection
Fix
Swap in high-performance feature spaces
No retraining required. Benchmark improvements instantly.
- Feature space swapping
- Zero retraining deployment
- Instant benchmarking
Model Observability for Foundation Models
See exactly where your model's features degrade with layer-by-layer spectral analysis.
Layer Selection
NCut Visualization — Layer 12
⚠️ Layer 12: Feature separation degrades here
Cluster entropy increased by 34% from Layer 11. Consider feature space replacement.
Included Features
- Real-time spectral analysis
- Layer-by-layer comparison
- Batch debugging across edge cases
- Export visualizations and reports
Quick Start
from spectral_labs import SpectralDebug
debug = SpectralDebug(api_key="your_key")
debug.connect_model(model="llava-1.5-7b")
result = debug.analyze(images=batch, layer=12)
result.visualize()How It Works
Get started in three simple steps. No complex setup required.
Connect
Install SDK and connect your model via API key
Works with PyTorch, HuggingFace, JAX
Visualize
Run NCut analysis on your feature spaces
See layer-by-layer semantic structure
Fix
Identify weak layers, swap in better features
Benchmark improvements, ship to production
Built on Proven Research
Standing on the shoulders of 25 years of foundational computer vision research.
Built for Mission-Critical AI
Trusted in industries where model failures have real-world consequences.
Autonomous Vehicles
Debug pedestrian detection failures before deployment. Ensure safety-critical models meet performance standards.
- Edge case detection
- Safety validation
- Real-time monitoring
Medical Imaging
Meet regulatory requirements with layer-by-layer transparency. FDA explainability for medical imaging AI.
- Regulatory compliance
- Audit trails
- Sensitivity analysis
Robotics
Fix grasp planning failures in production environments. Debug manipulation and perception systems.
- Grasp planning
- Object recognition
- Motion prediction
Simple, Transparent Pricing
Start free and scale as you grow. No hidden fees.
Free
Students & Researchers
- 50 runs/month
- 3 models
- Community support
- Basic visualizations
Pro
Individual Teams
- 5,000 runs/month
- Unlimited models
- Priority support
- Advanced visualizations
- Export & API access
Enterprise
Large Organizations
- Unlimited runs
- On-prem deployment
- Dedicated support
- Custom integrations
- SLA guarantee
Trusted by Researchers Worldwide
GitHub Stars
PyPI Downloads
By researchers worldwide
Trusted by researchers at leading universities and research labs
Start Debugging Your Models Today
Join researchers and engineers using Spectral Labs to build more reliable AI systems. Start free, scale when you're ready.