# Stoffel Analytics

> Privacy-first product analytics for sensitive workflows

Stoffel Analytics helps teams understand product adoption and behavior without making central collection of readable user histories the default architecture. Clients keep fixed-schema product events local, contribute protected values to approved analyses, and return aggregate product insight to the dashboard and existing reporting stack.

Canonical pages:
- Product overview: https://analytics.stoffelmpc.com/
- Stoffel documentation: https://docs.stoffelmpc.com/
- Stoffel source: https://github.com/Stoffel-Labs

Product workflow:
1. Client applications record fixed-schema product events locally.
2. The team defines an approved analysis, participation threshold, and time window.
3. Clients protect the selected values locally and submit them directly to configured compute services.
4. The services run the selected numeric sum or average program.
5. Encrypted aggregate shares are forwarded to the dashboard for decryption by the query owner.
6. The approved product insight can be reviewed in the dashboard or exported as CSV.

Strong-fit contexts:
- Product teams need aggregate insight across sensitive workflows
- A customer, partner, regulated deployment, or security review blocks central readable event histories
- Event owners, compute-service operators, minimum participation, and the authorized insight recipient can be named
- Existing analytics and reporting tools can consume the approved aggregate rather than each participant’s event history

Current implementation evidence:
- Fixed-schema screen, flow, app, error, crash, and CTA events
- Rust client, C FFI, and Swift wrapper
- Numeric sum and numeric average analysis programs
- Project, node, and query lifecycle management
- Result decryption, dashboard review, and CSV export

Not claimed:
- Consent-independent, ATT-proof, or cookie-proof analytics
- Automatic GDPR, CCPA, or HIPAA compliance
- Automatic anonymity
- Current production support for funnels, retention, cohorts, paths, experimentation, or a complete behavioral analytics suite
- Session replay, unrestricted user-level drill-down, arbitrary SQL, or arbitrary readable event export inside the private path
- Replacement of every existing analytics, clean-room, warehouse, BI, support, or debugging tool
- Published latency, throughput, availability, or production-scale guarantees
- Differential privacy unless a separate noise and privacy-budget mechanism is implemented

Last substantive review: 2026-08-19