Built on 500k commits.
GNEISS is powered by our custom Graph Neural Network trained on 500,000 real-world commits. Our model learns structural coupling patterns across massive codebases to predict architectural decay before it impacts deployment velocity.
500k Commit Training: Our GNN was trained on half a million commits from diverse repositories, learning deep structural patterns that traditional static analysis cannot detect.
Custom Architecture: We designed and built our own graph neural network architecture specifically for dependency analysis, not adapted from generic models.
Structural Prediction: The model identifies high-risk integration lines and predicts structural decay probability based on learned coupling patterns.
Model Performance
Trained on real-world commit data for accurate structural analysis.
500,000 Commits
Our GNN model was trained on half a million commits from open-source repositories, learning structural coupling patterns that predict architectural decay.
500k real commits
Custom GNN architecture
Our GNN is connected to Gemini.
GNEISS routes structural graph outputs into Gemini for architectural review. The system receives dependency metadata from our GNN and converts findings into plain-language risk analysis.
Gemini Review Bridge: GNEISS packages graph metrics, risk scores, and dependency context for Gemini so structural predictions become actionable engineering review.
Deep Structural Remediation: Gemini translates mathematical dependency bottlenecks into human-readable remediation guidance for the team reviewing the repository.
Active Integration
Our GNN outputs are connected to Gemini for analysis.
Gemini
Gemini is the analysis partner for the GNEISS pipeline, converting GNN risk signals into concise architecture guidance after the graph model completes its scan.
GNN metrics and graph metadata
Gemini architecture review
Why It Matters
Structural decay is silent but devastating. Without early detection, architectural debt compounds exponentially.
Future Change Cost
Features become slower and more expensive to add because everything depends on everything else.
Refactor Risk
One change in a module can ripple through many others, causing unexpected side effects.
Maintainability Decline
Engineers spend more time understanding the system than building it.
Reliability Erosion
The code may still compile, but it becomes more failure-prone as complexity rises.
Scaling Bottlenecks
Parts of the system become hard to extend, split, or optimize later.
GNEISS identifies these risks before they become critical, enabling proactive architectural decisions.