AI Nexus builds 31 named AI agents across two platforms. Each does a single task someone would otherwise do by hand. Every agent below is listed with the input a user gives, the output it produces, and the constraint that governs it.
Product examples on this site are illustrative.
Hospital operating system. Run your hospital. Staff it with AI.
Answers every call in your hospital's name, resolves FAQs, and books appointments straight into your schedule.
Calls every discharged patient on day 2 and day 7, checks recovery, and records structured outcomes.
Turns a recorded consultation into a structured clinical note, ready for the doctor to edit.
Builds a Patient-360 brief — visits, labs, meds, allergies, vitals trend — before the doctor walks in.
Drafts doctor and patient summaries plus a draft prescription, checked against drug interactions and telemedicine schedules.
Explains lab and imaging results in plain language in the patient portal, the moment they release.
Reads inbound referrals, drafts urgency and specialty, and chases missing information before the patient travels.
Classifies why a claim was denied and drafts the appeal, within value and review thresholds you set.
Predicts wait times and proactively updates waiting patients on WhatsApp, rescuing slots before they walk.
Cross-checks medications against RxNorm and openFDA at every admission and discharge, and flags discrepancies.
Renders patient communication into Hindi, Marathi, Kannada and more — and falls back to English rather than guess.
K-12 school operating system. AI that teaches from your textbook.
Answers any question from the school's own textbook, with the page number attached.
Takes spoken questions and answers aloud, grounded in the chapter, in the student's own language.
Turns every chapter into narrated audio or a two-host podcast.
Composes a song from the chapter, after a teacher approves the lyrics.
Builds a five-artifact lesson kit from one sentence, grounded in the chapter.
Generates board-ready questions tagged by board pattern and Bloom level.
Gives hints, never answers — and flags the teacher when a student is genuinely stuck.
Coaches writing with anchored comments, and declines to write it for you.
Answers questions about the school from live operational data, with citation chips.
Gives role-aware help on every screen, plus a spoken daily briefing.
Schedules spaced repetition, compressed to the days remaining before the exam.
Grades a student's own explanation against the book, quoting the pages they missed.
Turns a chapter into a mind-map where every node carries a page citation.
Sets board-pattern papers and marks them the way an examiner would, step by step.
Runs a three-minute daily practice ritual, with streaks and a weekly parent digest.
Works through maths step by step and finds the first wrong step, not just the wrong answer.
Scores pronunciation in the browser, so a child's recording never leaves the device.
Trains mental maths with spoken-answer grading, handwriting OCR and a hard screen-time cap.
Runs scenario interviews and produces a discussion kit for parents in 13 languages.
Runs predict-observe-explain against page-cited concept boards and simulations.
The same discipline runs through both platforms. These are controls implemented in the software, not commitments in a policy document.
PHI and personal information are stripped before any model call — nine categories of Indian personal identifier, including Aadhaar, PAN, GSTIN, bank account and passport number.
School answers are checked against the page they came from. What cannot be verified is removed and the answer says so — never rendered with a caveat.
DPDP consent is checked before client data reaches a model, and the check fails closed: no consent, no call.
No agent files, prescribes, sends or bills on its own. A clinician signs the note, a pharmacist signs the reconciliation, a biller approves the appeal.
Every tenant sets a ceiling. At the cap the model downshifts automatically rather than failing, and every invocation is counted against it.
One flag turns any single agent off instantly, per institution — no deployment, no support ticket.
Every AI invocation is written to an append-only audit trail with the request that produced it.
Each agent ships a deterministic engine returning the same schema as the model path, so the anti-hallucination gates apply identically whether or not a model is called.
A 30-minute demo of any platform, run on your own facility type, board, practice area or city — or a conversation about partnership. We reply within one business day.
