XaiRec provides a platform for building and deploying explainable AI recommendation systems. It focuses on transparency by offering tools to understand model predictions and feature importance in real-time. This allows businesses to deploy trustworthy, auditable personalization engines across various customer touchpoints.
Funding
$100M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Pharmaceutical and biotechnology companies often experience prolonged development cycles and inflated costs because clinical trial designs are based on limited historical insight, leading to inefficient patient recruitment and suboptimal protocol structures.
Solution
The platform applies supervised and unsupervised machine‑learning models to large repositories of historical trial outcomes, real‑world evidence, and patient registry data. By generating predictive enrollment curves and risk‑adjusted endpoint forecasts, it enables sponsors to iteratively refine inclusion criteria, dosing regimens, and site selection before launch. Adaptive simulation tools evaluate multiple protocol variants in silico, highlighting designs that maximize statistical power while minimizing sample size. Integrated analytics dashboards present actionable recommendations to study teams, accelerating go‑/no‑go decisions and reducing the overall time‑to‑market for investigational drugs. The solution is delivered as a SaaS offering that connects to existing clinical‑trial‑management systems via secure APIs, ensuring seamless data flow and compliance with regulatory standards.
Target Audience
The primary customers are pharmaceutical sponsors, biotech firms, and contract research organizations that design and execute Phase I‑III clinical studies and seek data‑driven optimization of trial protocols.
Features
- Automated data ingestion pipeline that normalizes heterogeneous trial datasets, electronic health records, and real‑world evidence sources
- Predictive enrollment modeling using gradient‑boosted trees and Bayesian survival analysis to forecast site‑level recruitment rates
- Protocol optimization engine that applies multi‑objective genetic algorithms to balance statistical power, patient burden, and cost constraints
- In‑silico adaptive trial simulation environment supporting virtual cohort generation and scenario analysis
- Real‑time monitoring dashboard with KPI visualizations, variance alerts, and what‑if scenario tools
- Bi‑directional API connectors for CTMS, eCRF, and EDC platforms enabling automated data exchange
- Built‑in GCP‑compliant security framework with role‑based access control, audit logging, and end‑to‑end encryption