Proven across regulated, high-stakes industries
STADLE is not a research prototype. It runs in production for global enterprises where data privacy, latency, and scale are non-negotiable.
Dynamic driver understanding from every signal
A global automotive OEM (name withheld) uses STADLE to build a persistent representation of driver preference from chat, driving behavior, vehicle state, and trip context — powering personalization that no single data source could deliver alone.
Critically, STADLE coordinates across vehicle ECUs, mobile apps, and backend systems without consolidating raw telemetry. The representation is built and updated at the edge — only compressed model updates cross system boundaries.
Risk scoring that improves continuously
Policyholder representations built from claims history, checkup data, and lifestyle signals allow underwriting models to refine predictions as new information arrives — without waiting for annual re-training.
Patient profiles that cross care settings
Hospital, clinic, and lab data remain siloed by regulation. STADLE fuses them into a persistent patient representation that updates with each interaction — enabling care-coordination AI without raw data consolidation.
Regulatory-ready privacy architecture
HIPAA and GDPR compliance is built into the model. Patient records never leave their source system. Only model updates cross boundaries, and each update is differentially private.
Continuously updated risk and patient profiles
Persistently updated policyholder and patient representations, built from claims, checkups, labs, and lifestyle signals — supporting risk scoring and decision support that goes beyond point-in-time snapshots.
Fraud and risk intelligence across institutions
Fraud patterns don't respect institutional boundaries — but regulations do. STADLE enables fraud and risk models that improve continuously from distributed transaction data, coordinated across institutions without centralizing the underlying records.
Each bank contributes model updates, not transaction logs. The aggregated representation captures cross-institution patterns that no single player could detect alone — privacy preserved by design.
Why federated fraud detection matters
Why not share transaction data directly?
PCI-DSS, GDPR, and competitive concerns prevent raw data sharing across institutions. STADLE's federated approach lets model intelligence cross boundaries that raw data cannot.
How does cross-institution learning work?
Each institution's STADLE Agent trains on local data and shares only encrypted model updates. The Aggregator merges these into a shared representation without ever seeing individual transactions.
What about false positive rates?
Cross-institution representations capture behavioral patterns invisible to single-institution models, reducing false positives while catching coordinated fraud rings that span multiple banks.
The representation layer for AI agents
LLMs are powerful. But without a persistent, evolving understanding of the user, every session starts from scratch. STADLE provides the memory layer that makes AI agents genuinely personal.
Persistent user representations
STADLE builds and maintains a latent vector per user that encodes preferences, communication style, context, and history — updated continuously without storing raw conversation logs.
Privacy-preserving personalization
User representations are compact and opaque. No raw transcripts leave the user's device or organization. The LLM is conditioned on the representation, not the conversation history.
Cross-session context
STADLE representations persist across sessions, devices, and LLM versions. Users don't have to re-explain their context. The AI remembers — compactly, privately, and continuously.
See STADLE in your industry.See STADLE in your industry.
We can demo STADLE against your data shape and use case. Most pilots run in 4–6 weeks.