Stellitron: Urania
Secure LLM Feature Usage Analytics
Secure LLM Feature Usage Analytics
The Data Paradox in Enterprise AI
Enterprises need granular feature usage analytics (e.g., code generation vs. creative modes) to optimize LLM products, but compliance requirements (GDPR, CCPA) forbid using traditional analytics that expose or infer individual user behavior. Optimization is stalled by privacy risk.
- ⚠Regulatory Risk: Traditional analytics platforms cannot guarantee individual PII is protected from internal reports.
- ⚠Optimization Blindness: Inability to track feature popularity and engagement accurately due to necessary data masking.
- ⚠Operational Complexity: High cost and slow speed of manual compliance audits for usage data.
Urania: Secure Analytics Layer
Urania, powered by Stellitron, is a privacy-preserving analytics layer for LLMs. It uses proprietary Differential Privacy (DP) algorithms to aggregate feature usage statistics, guaranteeing that individual user behavior cannot be reverse-engineered from the resulting optimized reports.
1. Secure Ingestion
Usage events (feature IDs, engagement time) are captured and immediately privatized using Stellitron’s DP library.
2. Aggregation & Guarantee
Data is aggregated across millions of users, mathematically ensuring a privacy budget is maintained, preventing inference of specific user actions.
3. Actionable Insights
Product teams receive real-time, compliant reports on feature popularity and engagement, enabling risk-free optimization.
Market Opportunity: Compliance Necessity
“The mandate for Privacy-Preserving Analytics (PPA) is now high, driven by the rapid rise of proprietary/internal LLM deployments in regulated sectors.”
Competitive Landscape: The Privacy Gap
Competitive Landscape
| Feature | PostHog (Product Analytics) | Elastic (Observability) | Mozilla.ai (Secure Platform) | Stellitron: Urania |
|---|---|---|---|---|
| LLM Feature Granularity | High | Medium | Medium | High |
| Established Enterprise Trust | Medium | High | Medium | Medium |
| Mathematical Privacy Guarantee (DP/SMC) | Low | Low | Implied/Medium | High |
| Focus on Regulatory Compliance | Low | Medium | High | High |
Business Model: Value-Based Pricing
Enterprise Subscription
Annual licensing based on number of active enterprise users and compliance assurance level (e.g., HIPAA-readiness).
Usage-Based Secure Events
Pricing based on the volume of secure analytics events processed (e.g., per 1 million DP-aggregated feature interactions), aligning cost with utility.
Traction & Validation (Q4 2024)
“Urania allowed us to move beyond simple data masking and finally gain insight into which new LLM features are driving real engagement without risking user privacy. It’s a compliance necessity.”
Financial Projections: Scaling Secure Analytics
Yearly Revenue Projections
Key Performance Indicators
The Ask: Fueling Compliance & Scale
Use of Funds
Exit Strategy: Strategic Acquisition
Exit Scenarios
Comparable Exits
Risk Analysis & Mitigation
Risk Analysis & Mitigation
Intense competition from established players (Elastic, PostHog) expanding LLM analytics features.
Focus exclusively on mathematically verifiable privacy guarantees (Differential Privacy) that monolithic competitors cannot easily integrate.
Achieving performant secure analytics at enterprise scale introduces significant latency and computational overhead.
Invest heavily in optimized cryptographic primitives and hardware acceleration (GPU/FPGA) for secure aggregation.
Potential for classification as a 'data processor' handling sensitive PII, increasing liability.
Architect the platform so PII is never visible to the analytics provider and proactively achieve SOC 2 Type II certification.
Sources & References
Generated by
Stellitron AI
Data Sources
Stellitron Internal Market Sizing
Exa AI Web Search (December 25, 2025)
Public Financial Data (Crunchbase, Reuters)
References
Global Tech Market Forecast, 2024 To 2029
Market Analysis (TAM/Growth)
Introducing any-llm managed platform: A secure cloud vault and usage-tracking service
Competitor Analysis (Mozilla.ai)
LLM analytics and observability
Competitor Analysis (PostHog)
Search startup Glean's valuation hits $7.2 billion in AI funding boom
Comparable Exits/Valuations
Cyera Doubles Customer Base in Six Months, Reaching $6 Billion Valuation
Comparable Exits/Valuations
For inquiries, contact:
contact@stellitron.comThis pitch deck is AI-generated for illustrative purposes. All financial projections, valuations, and market data are estimates and should be validated with professional advisors.