Industries · Education
Banao builds and deploys AI for schools, universities, and edtech platforms — adaptive learning paths, automated grading, at-risk student prediction, and AI tutoring — wired into your LMS and student information system.
Every system below is live somewhere, marking real submissions and flagging real students. We hand over deployed software, not a research prototype.
Studylab AI— adaptive practice and auto-tagged content, shipped into the product.
Each item below maps to a cost teachers and registrars already carry — hours of marking, students lost to dropout, admissions queues. We start where the time goes.
Adaptive sequencing that moves each student to the next concept on evidence, not on a fixed syllabus clock — built on your existing content and assessment data.
Models that mark multiple-choice, short-answer, and code submissions, with a confidence score and a human-in-the-loop queue for anything borderline.
Engagement, attendance, and assessment signals combined into an early-warning score that reaches advisors weeks before a student disengages for good.
Transcripts, certificates, and application forms read, validated, and pushed into your SIS — so admissions staff review exceptions instead of typing every field.
A subject-aware tutor that answers in your curriculum's language, shows its working, and declines to guess — grounded in your course material, not the open web.
Timetabling, enrolment queries, fee reminders, and helpdesk tickets handled by agents wired into your existing systems, so staff keep the exceptions.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we will not publish a number before it is verified.
Studylab AI
Studylab AI needed practice content faster than a small academic team could write it. Banao built a generation and tagging pipeline that drafts questions from the existing syllabus, tags them by topic and difficulty, and routes every draft to a subject reviewer before it reaches a learner.
A multi-campus university group
A multi-campus university group could see dropout only after a student had already stopped logging in. Banao combined LMS engagement, attendance, and assessment signals into a weekly at-risk score that lands inside the advisor's existing caseload view, with the reasons attached so the first conversation is already informed.
Banao runs a ~300-person engineering business on its own AI before a client ever logs in. InterviewGod screens every engineer we hire. Vikaas runs our own demand generation. We are our own first user, every working day.
For education that matters twice over: assessment and screening at scale are exactly what InterviewGod does inside our own hiring, so the grading and tutoring patterns we bring you are not theory — they already decide who joins the team.
Plenty of vendors will sell a school any model with 'AI' on the invoice. We would rather tell you where it won't pay back — it is why heads of department take our second call.
You have likely been pitched edtech that demoed well and died on adoption. We start by proving where the marking time and the dropout actually cost you, not by quoting a build.
On campus if it helps. You leave with a ranked list of AI opportunities, the baseline numbers behind each, and a go/no-go per item — yours to keep regardless. Proceed, and the Sprint fee is credited against the build.
Data work first, then the model. We build the data pipeline as a deliverable and integrate with your LMS, SIS, and assessment tools — the systems you already run, not a replacement for them.
Rollout with a human-in-the-loop queue and a dashboard staff will actually open, plus training for faculty and registrars. The models keep improving as each term's data comes in.
Our LMS and student records are old and scattered. Does that rule us out?
No — it is the normal starting point. Banao has integrated AI with legacy LMS installs, spreadsheet-based records, and on-prem student systems. The model needs a data signal, not a modern stack. Week one is an integration audit.
We don't have clean student data. Can we still start?
Yes. No institution does. We need some data, not perfect data. The first two weeks of any engagement is data engineering, and the cleaning pipeline is part of what we hand over, not a prerequisite you have to meet first.
How is student and minor data kept safe?
Privacy is designed in, not bolted on. We can deploy on your own infrastructure, keep student records inside your tenancy, and scope each model to the minimum data it needs. We work to your data-protection and consent rules and document where every field goes.
We tried an edtech tool and faculty never used it. Why is this different?
Most education AI dies on adoption — it works in the demo, teachers don't trust it. Our delivery treats faculty and registrar training as a non-negotiable deliverable, and every model keeps a human in the loop so staff stay in control of the call.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, you keep the opportunity model and the baseline numbers whether or not you continue. Worst case you have a free assessment; best case your budget paper writes itself.
How fast can something reach students and staff?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week rollout timed to a term boundary so it lands when staff have room to adopt it. Banao's ~300-engineer bench means work starts in weeks, not the months a single new hire takes to ramp.
Bring your heaviest marking load, your dropout numbers, or your admissions backlog. In 45 minutes we'll map the AI opportunity and the maths behind it.
Book a 45-min scoping call