Industry · Education
Support the student. Don't do the work for them.
Education AI is a design problem before it's a technical one. The useful systems answer administrative questions instantly and make academic help Socratic rather than extractive.
- Generative AI & RAG
- AI Agents
- Prototype → Production
Why now
Admissions and student-services teams field the same few hundred questions every cycle, at volumes that spike hard around deadlines. That's the clearest win. Academic uses are more delicate: a system that hands over answers undermines the thing you're selling, so the design has to make assistance productive rather than substitutive.
The problem
Where education teams lose time today.
- 01
Admissions queues collapse at the deadline
Eligibility, document and fee questions arrive in a two-week wave that no staffing plan covers, and slow answers cost enrolments.
- 02
Students can't find what the handbook already says
Policy on attendance, extensions and grading exists across PDFs and portal pages, so staff answer the same question repeatedly.
- 03
Course content production is slow
Assessments, variants and accessible formats take educator hours that don't scale with cohort size.
Constraints
What makes this harder than a generic chatbot.
- 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.
Integrations
Systems we wire into.
- Moodle & Canvas
- Google Classroom
- Student information systems
- Salesforce Education Cloud
- Twilio & WhatsApp Business
- Zoom & Google Meet
- Payment gateways for fee collection
Services
What we build for education.
- RAG for course material and student supportRetrieval over handbooks, course material and policy documents, answering with citations in the student's language — and saying so when the answer isn't there.Read
- AI agents for admissions and student servicesAgents that qualify applicants, chase documents, answer eligibility questions and book counselling calls across WhatsApp, voice and email.Read
- EdTech prototype to productionAuth, roles, data protection for minors, and load behaviour added to an edtech prototype before an institution's procurement team asks about any of it.Read
How an engagement is judged
- A scope small enough to finish, agreed in writing in week one
- An eval suite built from your own data, versioned in your repo
- Weekly numbers on the metrics we agreed, including the bad ones
- Code, IP and repo yours from day one — no proprietary runtime of ours
We publish no per-industry result metrics because almost all client work is under NDA and we won't quote numbers we can't evidence. What we can show is the process and the in-house work on /work, which is public and running.
Pricing ladder
Three ways to start.
Sprint validates · Build productionizes · Retainer scales. The Sprint fee credits toward Build.
Step 1 · Validate
30-day Sprint
Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.
$4,500 fixed
Learn more- Most teams land here
Step 2 · Build
Prototype → Production
Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.
from $6,000
Learn more Step 3 · Scale
Managed Retainer
Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.
from $750/mo
Learn more
FAQ
AI in education: the questions we get.
Won't students just use it to cheat?
Not if it's built to refuse that role. Coursework interactions are scoped to hints, questions and worked-example walkthroughs with the final step left to the student, and the interaction log gives educators visibility they don't have today.
Can it handle multiple languages?
Yes, and for Indian institutions it usually must. We evaluate quality per language rather than assuming parity — quality drops off unevenly, and knowing where is the point of the eval.
What about students in distress?
Wellbeing and safeguarding signals route immediately to your existing human pathway with the conversation attached. That branch is built first and tested hardest.
Contact
Talk to us about AI in education.
Tell us where you are now and where you want to be. We reply within one business day.
Or skip the form — book a Calendly slot directlyadmin@neuroxai.com · +91 70149 99768
Remote-first team across India · US · EU · HQ in Udaipur, India
Other industries
Similar constraints, different vertical.
- AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
- AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
- AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read