The Chronos-X Engine
A microservices architecture orchestrating Amazon Chronos T5, Polars, and Next.js 16.
Data Ingestion
Raw UIDAI logs are processed via ChronosDataEngine.
- Parquet Compression (Snappy)
- Zero-Copy Arrow Memory
- Polars Lazy Execution
Hybrid Predictor
Ensemble model combining AI forecasting & spatial search.
ai = chronos_t5.forecast(pin)
spatial = knn_search(pin)
return weighted_avg(ai, spatial)
Context Delivery
Real-time insights served via WebSocket & D3.js.
- ContextEngine Injection
- LocationResolver (Fuzzy)
- React Server Components
The Pulse Vector
We don't just count transactions. We measure the heartbeat of every district.
Tracks new family settlements via 0-5 age enrolments.
Monitors urbanization drifts via address updates.
Detects workforce shifts using biometric authentications.
Vp,t = [ Gnorm, Fnorm, Lnorm ]* Z-Score Normalized for cross-regional comparability
Spatial Parallel Search
Traditional models fail on sparse data. We invented Behavioral Cloning for demographics.
def find_parallels(target_pin):
# 1. Vectorize Target Behavior
vec_t = encode(target_pin.history)
# 2. Search Universal Database (19k+ PINs)
matches = kNN(vec_t, k=3, metric='cosine')
# 3. Clone Future Trajectories
return [m.future for m in matches]The "Time Machine" Logic
Pattern Matching
Scans 19,000+ PIN codes instantly to find regions with identical growth patterns to your target.
Parallel Universes
Finds a suburb in Bangalore that behaves exactly like your target in Pune did 6 months ago.
Trajectory Cloning
Projections are not just mathematical guesses. They are cloned from the actual futures of parallel regions.
The Engine Room
Built on a modern, high-performance stack.
Next.js 16
Server Components & App Router
FastAPI
Async Python Backend
Polars
Rust-based DataFrame Engine
Amazon Chronos
Foundation Forecasting Model
D3.js
60fps Vector Visualization
TypeScript
Strict Type Safety