Engineering Rigor for High-Stakes Credit
Low-latency scoring engines, real-time SHAP explainability calculations, and multi-tenant isolation built for production regulated lending.
< 480ms
P99 Decision Latency
End-to-end model inference + SHAP computation
< 15ms
Rule Evaluation Speed
Deterministic FCRA & hard constraint filtering
0.847
Model AUC Performance
Validated on thin-file & near-prime credit datasets
100%
Audit Trail Compliance
Immutable event hashing per decision payload
System Flow
Four-Stage Decision Pipeline
Every request flows through deterministic rule evaluation before machine learning inference.
API Ingestion
Async JSON payload processing with Pydantic v2 schema validation and HMAC signature authentication.
Rule Engine
Hard constraint filtering for FCRA compliance gates and immediate rejection policy evaluation.
ML Scoring & SHAP
Gradient-boosted decision trees compute risk score alongside dynamic SHAP feature weight attributions.
Immutable Audit
Response formatted with FCRA adverse action codes and persisted to immutable audit store.
Tech Stack
Production Technology Stack
Built on modern, type-safe, and high-performance open-source foundations.
Python 3.11, LightGBM, PyTorch, SHAP Engine
FastAPI, SQLAlchemy, Pydantic v2, AsyncIO
PostgreSQL, Redis Cache, Row-Level Security
Next.js 14 (App Router), TypeScript, TailwindCSS
FCRA §615 Rules Engine, ISO 8601 Audit Log
GCP App Hosting, Docker, Single-Tenant VPC Support
Inspect Code & API Specs
Explore our interactive Product Lab or review open-source repositories and integration patterns.