Lumina EdTech · Education
RAG tutoring assistant for 200k learners
A curriculum-grounded AI assistant answering with 94% measured accuracy.
- Industry
- Education
- Services
- RAG Development, AI Development, LLM Applications
- Stack
- Python · LangChain · Claude · PostgreSQL
- Timeline
- 4 months
- Region
- India / SEA
94%
answer accuracy on golden set
1.4M
learner questions answered/mo
58%
support tickets deflected
$0.011
cost per answered question
01
The challenge
Lumina's learners asked hundreds of thousands of questions monthly that outpaced human tutor capacity. Generic chatbot pilots hallucinated beyond the curriculum and damaged trust with parents and schools.
02
The solution
We built a retrieval-augmented assistant grounded strictly in Lumina's curriculum: syllabus-aware retrieval, grade-level answer calibration, and hard refusal outside curriculum scope. Every answer cites the lesson it draws from, and low-confidence retrievals route to human tutors.
03
Architecture & engineering
An ingestion pipeline keeps 40,000 curriculum documents synced and chunked; hybrid retrieval with a reranker feeds Claude through a versioned prompt system. An evaluation harness scores 1,200 golden questions on every release, gating deploys on accuracy regressions.
04
The outcome
The assistant now answers 1.4M questions a month at 94% measured accuracy, tutor escalations dropped by more than half, and per-question cost is a rounding error against tutor hours.
“Sydova treated accuracy like an engineering metric, not a marketing claim. That rigor is why schools trust the product.”
Services used
How we did it
RAG Development
Retrieval-augmented generation over your documents, data, and knowledge — with measured accuracy.
AI Development
End-to-end AI product engineering — from model selection to production-grade shipping.
LLM Application Development
Production applications built on frontier models — prompt systems, tool use, and evals included.
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