Foreseer
Foreseer — Predictive Intelligence Platform
Encrypted, fail-closed prediction APIs and what-if modeling for regulated industries.
Status: Alpha
Backend suite under active development — 244 of 250 tests passing. Data sources are demo-only; live connectors are wired by operators. Compliance scaffolding is included, but Foreseer is not a certified compliance product.
- 244 of 250 tests passing (backend suite under active development)
The problem
Why this exists
Regulated organizations need predictions they can audit and explain — black-box forecasts do not survive compliance review.
Sensitive data must be encrypted at the field level, and the system must fail closed: never store sensitive fields unencrypted on a misconfiguration.
Demos routinely blur demo data and live data. Operators need that labeling to be explicit, not implied.
Compliance frameworks need audit hooks and reporting primitives without vendors claiming certifications they do not hold.
The approach
How it works
Foreseer combines ensemble predictive models (ARIMA, Bayesian networks, Monte Carlo, chaos theory) with a Prime Radiant interface: interactive 3D causal graphs for exploring relationships and running what-if intervention scenarios.
Field-level encryption fails closed — the service refuses to start if encryption keys are missing or left at placeholder values, so sensitive fields are never silently stored unencrypted.
Hash-chained immutable audit trails, PII detection and redaction, and GDPR/HIPAA reporting scaffolding give compliance teams hooks to work with — with no false certification claims.
Capabilities
Key features
Under the hood
Architecture overview
Backend — FastAPI (Python 3.11)
Plugin-based data connectors (MySQL, REST, Kafka) with health monitoring and retry logic; operators wire their own sources.
Graph layer — Neo4j
Ontological knowledge representation for regions, events, indicators, and interventions, with Cypher-powered graph operations.
Streaming — Kafka
Real-time data ingestion feeding the prediction pipeline in batch or streaming modes.
Modeling
ARIMA time-series forecasting, Bayesian causal inference, Monte Carlo simulation, and chaos-theory analysis.
Frontend — React + Three.js
Prime Radiant: 3D causal graphs, what-if scenario modeling, and real-time dashboards via Docker Compose.
Terms
Licensing & pricing
What's next
Roadmap
Real data connectors enabled by default, replacing demo-only sources.
Full integration of the analysis pipeline (currently placeholder in parts).
Load testing with measured — not targeted — performance figures.
Frontend unit-test coverage for the React interface.