Security & Privacy
How STET handles your data, ensures auditability, and maintains transparency.
Overview
STET is designed for financial reconciliation where security and auditability are non-negotiable. Our architecture prioritizes:
Desktop-First Processing
Production workflows execute inside the desktop app, not a hosted execution surface.
Full Transparency
Every decision includes evidence links and reasoning.
Deterministic First
Rule-based matching before any ML enhancement.
No Model Training
Your data is never used to train ML models.
Data Handling
In-Memory Processing
Production file processing happens locally in the desktop app. Files are read, parsed, matched, and kept on-device; document content is never written to STET-controlled servers.
No Data Retention
STET does not retain original document content on STET servers. Local app storage may keep on-device review artifacts, caches, and audit outputs so the operator can continue work.
Stateless Architecture
The website/backend handles account, billing, and support surfaces. Production analyst workflows are intentionally outside the hosted request path and run inside the desktop app boundary.
Deterministic Logic
The core matching algorithm is entirely deterministic:
- Same inputs always produce same outputs. No random seeds, no non-deterministic ordering.
- Rule-based matching runs first. Passes 1-3 use exact thresholds (85% similarity, ±3 days, etc.).
- ML enhances, never fabricates. Semantic matching only provides similarity scores; it doesn't invent matches.
Reproducibility Commitment: Given the same input files and thresholds, STET will produce identical results every time. This is verified via the file hash in the audit certificate. Accuracy of results depends on the quality and completeness of input data.
ML Transparency
STET uses a pre-trained sentence transformer model for semantic matching. Here's what you need to know:
Model Used
sentence-transformers/all-MiniLM-L6-v2A small, efficient model for computing text embeddings. Open-source and widely audited.
No Training on Your Data
The model is pre-trained and frozen. Your transaction descriptions are used only for inference (computing embeddings). They are never stored, logged, or used for training.
No AI Fabrication
Unlike generative AI, semantic matching computes similarity between existing texts. It does not generate or fabricate transactions. Final verification remains the analyst's responsibility.
Auditability
Every audit produces a certificate that enables third-party verification:
- File Hash: SHA-256 of combined input files
- Threshold Configuration: Similarity %, date tolerance, etc.
- Match Log: Every match with pass used and confidence score
- Discrepancy List: Every flagged item with reasoning
- Timestamp: ISO 8601 completion time
Anyone with the same input files can re-run the audit and verify they get identical results.
Deployment Options
Desktop App
The primary production deployment model. Users run STET locally with the engine embedded in the desktop app.
On-Premise
Air-gapped and enterprise-managed deployments remain available where buyers require infrastructure control.