Education · Generative AI & RAG

The handbook, answerable at 2am.

Retrieval over handbooks, course material and policy documents, answering with citations in the student's language — and saying so when the answer isn't there.

  • Retrieval pipelines
  • Fine-tuned domain models
  • Multimodal (text · image · voice)
  • Eval + guardrails

What we build

Generative AI & RAG for education, specifically.

  • 01

    Policy and administrative answers

    Attendance, extensions, fees and grading answered from the current handbook with the section cited, at the hours students actually ask.

  • 02

    Course material Q&A

    Scoped to a module's own readings and lectures, so answers reflect what was taught rather than the internet's average view of the topic.

  • 03

    Programme and eligibility guidance

    Prospective students get accurate entry-requirement answers with the source, instead of a prospectus PDF and a contact form.

What we measure

  • Accuracy on a staff-graded question set
  • Share of enquiries resolved without a staff touch
  • Out-of-hours resolution rate
  • Answer quality across supported languages

Instrumented in week one and reported weekly. These are the numbers the engagement is judged on — not a forecast of what they'll be.

Constraints

What has to be true in education.

  • 01

    Academic help is Socratic by design

    For coursework, the system asks and hints rather than answering, and hands off to a human when a student is genuinely stuck. Building the answer-machine version is easy and actively harmful to the institution buying it.

  • 02

    Minors change the data rules

    Under-18 cohorts bring stricter consent, retention and moderation obligations under DPDP, GDPR and COPPA-equivalent regimes. That shapes the architecture, not just the privacy notice.

  • 03

    An educator reviews anything assessed

    Generated assessment content and feedback drafts go to an educator before a student sees them. Grading with consequences stays human.

How it runs

The Generative AI & RAG engagement, step by step.

  1. 1

    Data inventory

    What docs / databases / APIs / feeds matter? What's PII vs public? What changes daily vs quarterly? We map it before writing code.

  2. 2

    Retrieval architecture

    Hybrid (BM25 + vector) retrieval, chunking strategy, reranking, citations. Tuned for your domain not a generic benchmark.

  3. 3

    Eval suite

    Real-world question set with golden answers. Faithfulness, context precision, latency. So you know when changes ship a regression.

  4. 4

    Guardrails

    Prompt-injection defense, PII scrubbing, refusal patterns, output schemas. Especially critical for customer-facing deployments.

  5. 5

    Production + iterate

    Cost-effective inference (caching, fallback models), monitoring, weekly eval reports. Fine-tune when the data justifies it.

Integrations

Systems we wire into for education.

  • Moodle & Canvas
  • Google Classroom
  • Student information systems
  • Salesforce Education Cloud
  • Twilio & WhatsApp Business
  • Zoom & Google Meet
  • Payment gateways for fee collection

FAQ

RAG for course material and student support: your questions.

  • How do we stop it answering assessment questions?

    Assessment content is excluded from the index, and coursework-shaped queries route to the Socratic mode rather than the retrieval answer. Both behaviours are scored in the eval suite so a model change can't quietly relax them.

  • Our policies change every year. How do we keep it current?

    Version-aware indexing keyed to the academic year, with the cohort's year as a retrieval filter — a returning student's rules and a fresher's are not the same document.

  • Should I use RAG or fine-tuning?

    RAG for facts that change. Fine-tuning for style, format, or domain reasoning. Most production systems use both — and we'll tell you which mix is right after a 1-week discovery.

  • Can I use my own LLM (open-weights)?

    Yes. We work with Llama, Mistral, Qwen, and similar — useful for data residency, cost predictability, and air-gapped environments.

Contact

Talk to us about generative ai & rag for education.

Two or three sentences about the workflow you'd start with. We reply within one business day.

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admin@neuroxai.com · +91 70149 99768

Remote-first team across India · US · EU · HQ in Udaipur, India