IVFPULSE // PROPRIETARY CLINICAL AI

HOW FERTIS
WORKS_

India’s only clinical intelligence model purpose-built for IVF — trained on Indian data, validated on Indian outcomes, improving with every case.

1,00,000+Indian cycle records
8+ yrsof outcomes data
3857%projected pregnancy rate lift*
₹38,734avg saved per cycle
SECTION 01 // THE PROBLEM

IVF today is expensive, uncertain, and built on someone else’s data

Most protocols were designed for Western populations. The result: preventable failures, avoidable costs, and families who give up too early.

35–40%National first-cycle success rate. Most families need 2–3 attempts.
₹1.5–2.5LAverage cost per cycle. Two to three cycles means ₹4.5–7.5L out of pocket.
<5%Of clinical AI models used in IVF globally are trained on Indian patient data.
The root cause — hundreds of decisions, made with:
Generic protocols derived from Western population studies
Intuition built over years — not systematic pattern recognition across thousands of comparable cases
No real-time view of how similar Indian patients responded to similar decisions
AI tools, if used at all, trained on datasets that don’t reflect Indian hormone profiles or genetics
SECTION 02 // WHAT FERTIS IS

Not general AI adapted to IVF. Purpose-built clinical intelligence.

Trained exclusively on Indian IVF data to make every clinical decision sharper than the last.

INDIAN-FIRSTBuilt for Indian biologyIndian hormone response curves, PCOS prevalence patterns, AMH distributions. Calibrated for the population it serves — not approximated from Western benchmarks.
PURPOSE-BUILTIVF-specific, not general medicalEvery model parameter, training objective and validation metric is specific to IVF decision points — dose, scan, lab, transfer.
HUMAN-IN-THE-LOOPDoctor always in controlFertis proposes. The clinician approves. No recommendation reaches a patient without clinical sign-off — every output transparent and traceable.
HOW A RECOMMENDATION REACHES A PATIENT
Hormones, scans, history — structured into 400+ clinical features.
SECTION 03 // MODEL ARCHITECTURE

An ensemble of specialists, one calibrated answer

Fertis is not a single model — specialist sub-models each read a different clinical signal, and their outputs combine into one weighted recommendation with a calibrated confidence score.

Time-Series RegressionModels hormone trajectories (E2, LH, P4) and follicle growth velocity across stimulation days — a curve shape at Day 8 predicts over-response better than any single value.LSTME2 velocityFollicle cohort index
Gradient Boosted ClassifierTabular clinical data — AMH, BMI, age, prior cycles, sperm parameters — through an ensemble of decision trees, with feature-importance explainability.XGBoostSHAPFeature importance
Bayesian InferenceQuantifies uncertainty. A 94% confidence score is a calibrated probability verified against historical holdout outcomes — not a raw logit.Calibrated probabilitiesUncertainty boundsHoldout validated
SECTION 04 // THE TRAINING PIPELINE

From clinic to model

How raw patient cases become the intelligence behind every recommendation. All data is anonymised at Stage 2 — no PII ever enters a model pipeline.

STAGE 1Case ingestionClinic EMRLab integrationsScan records
STAGE 2DPDP anonymisationPII removalID hashingConsent verified
STAGE 3Feature engineeringHormone ratiosGrowth velocityPhase normalisation
STAGE 4Outcome-weighted trainingLabelled outcomesPenalty weightingEnsemble training
STAGE 5Holdout validation20% held outClinical sign-offVersioned release
FEATURES WE EXTRACT
E2 growth velocity between scan visits
Follicle cohort tightness index
AMH-adjusted gonadotropin dose ratio
PCOS phenotype classifier (Rotterdam criteria)
Sperm quality composite score
WHAT FERTIS LEARNS TO PREDICT
Stimulation dose response class (under / on-target / over)
OHSS risk score at trigger day
Blastulation probability (Day 3 → Day 5)
Endometrial receptivity at transfer
Clinical pregnancy probability per transfer
SECTION 05 // CONTINUOUS LEARNING

Every outcome makes the next prediction sharper

Fertis is not static. Every recommendation — followed, edited, or overridden — becomes a labelled training signal. Override patterns teach it exactly where to improve.

What trains the modelApproved recommendations with confirmed outcomes, doctor edits and why, override patterns across the network, verified pregnancy results, lab metrics.
Update cadenceQuarterly validated releases. Confidence recalibration after each batch. Regression-tested against the prior holdout set before every deployment.
The network effectEach clinic that joins contributes signal and receives a smarter model — federated aggregation, no raw data sharing. More clinics, smarter Fertis for all.
SECTION 06 // WHO BENEFITS

Built for doctors. Felt by patients.

FOR DOCTORS1Confident borderline decisionsWhen a case sits between two protocols, Fertis surfaces how 1L+ comparable Indian cases resolved.
FOR PATIENTS1Lower cycle costOptimised doses and fewer unnecessary scans. Average saving: ₹38,734 per cycle — ₹77K+ across a two-cycle journey.
FOR DOCTORS2Defensible documentationEvery approved recommendation is logged with reasoning and evidence — decisions document themselves.
FOR PATIENTS2Better first attemptsProjected first-attempt clinical pregnancy rate improves from 38% to 57% in clinics using Fertis.*
FOR DOCTORS3Reduced cognitive loadOn a 30-consultation day, Fertis surfaces the most time-sensitive decision for each patient.
FOR PATIENTS3Honest prognosis from day 1A personalised success probability based on your actual profile against comparable Indian cases — not a clinic average.
FOR DOCTORS4Continuous educationReviewing Fertis’s reasoning reveals Indian-specific patterns — PCOS sub-phenotypes, dose–age interactions.
FOR PATIENTS4Fewer unnecessary interventionsIf a scan adds no information or a dose change isn’t warranted, it doesn’t happen — and isn’t billed.
SECTION 07 // CLINICAL OUTCOMES

Measured, not promised

Validated against holdout data and verified pregnancy outcomes across active clinics.

BEFORE FERTIS WITH FERTIS

*Projected live-birth rates based on holdout validation. Real-world deployment data is being tracked for publication as a peer-reviewed study — treat as projected outcomes pending full RCT validation.

SECTION 08 // THE MISSION

Make IVF affordable and predictable for every Indian family

AffordableOptimised protocols cut unnecessary spend; fewer repeat attempts cut lifetime cost. IVF within reach of the middle-income family.
PredictableAn honest, personalised success probability from day one — not a generic clinic average.
Continuously improvingEvery case treated makes the model smarter for the next. Intelligence that compounds.
SECTION 09 // COMPLIANCE
ISO 27001Certified information security management — storage, access control, incident response, vendor management.
HIPAA-alignedUS health-data privacy standards as our baseline — encryption at rest and in transit, strict access controls, audit logs.
DPDP Act 2023Compliant with India’s data protection law — consent, data-principal rights, purpose limitation, anonymisation.
FERTIS-01 // READY

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