Innova AI Engine is the asynchronous AI layer powering SuperProfe. It consumes events from AWS SQS, runs knowledge-tracing calibration, error classification, OCR, guide ingestion, and student-submission grading, then writes results back to Postgres — all without a monolithic server.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/vruizz22/innova-ai-engine/llms.txt
Use this file to discover all available pages before exploring further.
Introduction
Understand what the engine does, how it fits into the SuperProfe platform, and which problems each worker solves.
Quickstart
Install dependencies, configure environment variables, and run your first worker locally in minutes.
Architecture
Full pipeline diagram, Clean Architecture breakdown, and deploy-order constraints between the backend and engine stacks.
Workers
Reference for all ten Lambda functions: triggers, timeouts, memory, and payload schemas.
Core capabilities
BKT Calibration
Nightly Bayesian Knowledge Tracing parameter fitting via brute-force grid search over 4 skill parameters.
IRT 2PL Fitting
Item Response Theory calibration with L-BFGS-B MLE — discrimination and difficulty per exercise, no GPU needed.
LLM Error Classifier
Batched Claude Haiku classification against a 2,600+ entry procedural error taxonomy with prompt caching.
OCR Vision Pipeline
Gemini 2.5 Flash primary OCR with automatic Claude vision escalation when confidence falls below threshold.
Get started
Install dependencies
Use
uv to create the virtualenv and install all packages, including dev extras:Start shared infrastructure
The engine shares Postgres, MongoDB, and LocalStack with the backend. Start them from the backend repo:
The engine must be deployed after the backend stack (
innova-backend-serverless). SQS queues, S3 buckets, and SSM parameters are all created by the backend stack and referenced by ARN in serverless.yml.