Medovac raises $85M Series B led by Andreessen Horowitz
The round accelerates research into causal inference and the Atlas semantic layer.
Medovac turns raw operational data into governed, production-grade intelligence. Our platform automates the data science lifecycle: ingestion, feature engineering, modeling, causal analysis, and monitoring, so analytics teams move from question to decision in hours.
Built with and trusted alongside
Each engine automates a stage of the data science lifecycle. Together they take you from raw records to a decision you can defend.
Automated feature intelligence
Signal profiles your warehouse, proposes candidate features, and promotes the ones that survive statistical validation into a governed feature store.
Learn moreProbabilistic forecasting
Forecast blends temporal fusion transformers with classical statistical baselines and returns full predictive distributions, not point estimates.
Learn moreAnomaly and drift monitoring
Sentinel monitors data quality and model health with unsupervised anomaly detection and adaptive thresholds tuned to each metric's own history.
Learn moreCausal inference and experimentation
Causal estimates treatment effects from experiments and observational data using doubly robust estimators and synthetic controls.
Learn moreSemantic analytics layer
Atlas is a semantic layer and retrieval-augmented analyst that translates natural language into governed queries against certified metrics.
Learn moreShared lineage, shared governance, shared security. Adopt one engine or all five: they speak the same data plane.
See how it fits togetherAggregated across Medovac production deployments in regulated industries.
Real patterns from Medovac deployments: faster delivery, lower error, and throughput that scales with your business.
Median across comparable enterprise projects. Lower is better.
Records processed per month, in billions, trailing 12 months.
Mean absolute percentage error on held-out backtests. Lower is better.
Medovac closes the last mile of analytics. Every stage is automated, governed, and observable, so your team spends its judgment where it matters.
Read-only connectors profile every table and build a governed semantic model.
Signal engineers features, Forecast and Causal model outcomes, Sentinel monitors health.
Atlas answers plain-language questions with lineage, and recommendations flow to where work happens.
Representative telemetry from a live Medovac data plane.
The round accelerates research into causal inference and the Atlas semantic layer.
Ask questions in plain language and get answers grounded in certified metrics with full lineage.
Deepening GPU-accelerated training and inference across the forecasting and causal engines.
Book a technical walkthrough with our field data science team. We will connect a sample of your data and show governed, production-grade intelligence in under an hour.