Built for
Enterprise Scale.
Processing multimodal inputs (audio, handwriting, text) at scale requires serious infrastructure. Our architecture separates the fast-path API routing from the heavy, asynchronous AI inference workloads.
Stateless Compute Clusters
We utilize horizontally scaling worker nodes (Celery/Redis) that spin up from zero to thousands of instances in seconds. This prevents queue bottlenecks during high-traffic exam periods and eliminates idle costs during the night.
Beyond Basic Wrappers
Basic LLM wrappers rely on a single, fragile "mega-prompt" that easily hallucinates. The RALE Engine (Rubric Assessment Learning Engine) combines heavy Python state machines with AI, executing up to 14 distinct neural calls per assessment. One node extracts, another maps syntax, a third audits coherence, and a final node compiles. This pipeline strictly enforces proprietary pedagogical logic and fails fast if parameters are breached.
Forensic Data Trails
Every byte of telemetry—tokens used, model latency, inference hashes, and raw prompt chains—is stored in our ai_logs database. If an educator contests a grade, we can instantly retrieve the exact mathematical context that led to the result.
Regional Sovereignty
We support strict data residency mandates. Deployments in Seoul (asia-northeast3), Singapore, and the EU ensure student data never crosses restricted borders.