Local Evidence

Grounded in biological truth with 20,000 validated cycle-level records.

20,000 Records
0.77 F1-Score
Clinically Validated
Start Your Pilot

Grounded in Biological Truth

Deploying an algorithmic decision-support tool in reproductive healthcare requires rigorous empirical validation. The Conceive platform is structurally opposed to opaque, unvalidated AI. Our foundational architecture was developed and trained on a robust dataset of 20,000 cycle-level records curated from 50 anonymized patients.

Dataset Overview

20,000 Cycle-Level Records

20,000
Cycle Records
Curated from 50 anonymized patients
17
Numeric Features
Demographic, hormonal, and clinical metrics
6
Categorical Variables
Diagnoses, lifestyle, and treatment data
50
Patients
Diverse demographic and clinical profiles

Data Categories

Demographic Age, BMI
Hormonal Panels Estradiol, FSH, LH, Progesterone
Medical Diagnoses PCOS, Endometriosis
Lifestyle Indices Diet Type, Workout Type, Stress Level

Validation Results

Rigorous Testing Against Clinical Benchmarks

0.77
Validation F1-Score
Significant reliability in predicting per-cycle conception outcomes
0.60
Precision
High precision across diverse patient demographics
100%
SHAP Attribution
Full explainability for every prediction

SHAP Feature Attribution

Dominant predictors across age groups, directly aligning with established clinical literature

Implementation Methodology

How to Pilot the Conceive API

For clinical networks, fertility specialists, and digital FemTech applications seeking to integrate advanced predictive intelligence, we provide a structured, low-risk implementation pathway. Adopting the Conceive Score API is managed through a comprehensive 12-week institutional pilot program.

1 Weeks 1-3

Integration & Baseline Mapping

Secure API integration via our FastAPI backend. We map the client's existing Electronic Health Record (EHR) data structures or mobile app telemetry to our required input ontology, ensuring seamless data ingestion.

2 Weeks 4-6

Shadow Deployment

The Conceive API runs in a shadow capacity. The model processes cycle data and generates individualized treatment effects and conception probabilities, which are securely logged and compared against historical or baseline outcomes without altering the direct user interface.

3 Weeks 7-9

Clinical Auditing & Fairness Review

Clinical partners and data scientists review the Explainable AI (SHAP) outputs. We jointly conduct subgroup calibration audits, specifically verifying the model's accuracy across older-age and high-BMI cohorts to ensure zero probability distortion.

4 Weeks 10-12

Full Deployment & Scale Recommendation

Synthesis of validation data into a formal impact report. The microservice transitions to active prediction, empowering the platform to deliver real-time, explainable conception insights directly to patients and providers.

Start Your Pilot

Product Roadmap

Disciplined Clinical Scaling

Now

Prototype & Validation

  • Operating the MVP with Dockerized backend
  • MLflow for drift monitoring
  • Finalizing SCM counterfactual simulations
In Progress
Next

B2B API Licensing

  • Launching enterprise API tier
  • Regional fertility app integration
  • Cycle-indexed graph reasoning
Upcoming
Future

Clinical Multi-Site Expansion

  • Domain adaptation for diverse cohorts
  • Multi-site clinical trials
  • Long-term treatment recommendations
Planned

Ready to Validate Conceive in Your Practice?

Join our 12-week pilot program and experience the power of graph-based fertility intelligence.

Start Your Pilot Explore Business Model