platoseedplatoseed
AI native Palantir for mid market.
Netter (YC P26) helps companies regain control over their data, so teams can pilot their activities with high precision. Most companies are sitting on years of valuable data, spread across different formats and disconnected tools that don't talk to each other. Netter brings technical capacity to companies that don't have an army of data engineers, and tackles their most complex data challenges. - A nursing home group was chasing unpaid invoices manually across disconnected tools. Netter centralized the collection workflows, automated reminders, real-time visibility, task routing. - A retail chain had years of loyalty card data sitting unused. Netter cross-referenced it with public demographic data to build precise customer segments and trigger the right promotions to the right people.
Netter provides an AI native data platform that centralizes scattered data, structures it into a living ontology, and enables teams to deploy dashboards, workflows, and ML models with conversational tooling. It emphasizes building solutions without requiring engineers, offering 120+ native connectors and end-to-end traceability from data to running systems.
Netter connects 120+ data sources, cleans and enriches data into a unified ontology, and lets users describe desired outcomes in natural language to build dashboards, workflows, and apps. It supports incremental data syncing, versioned and auditable steps, Python scripting for customization, and observable, secure operations with audit logs and RBAC. The platform ships live outputs quickly (e.g., 4 hours from idea to deployed app) and enables end-to-end traceability from source data to dashboards and alerts.
Who it’s for: Mid-market companies across industries (e.g., healthcare, retail, manufacturing, logistics, real estate) that have data in multiple systems and need analytics, workflows, and ML without relying on extensive engineering teams.
Listed features and real-world use cases across multiple industries imply product-market fit and traction; mentions of live deployments and ROI benchmarking suggest early-to-mid traction, with a focus on enterprise-grade capabilities.
Co-founder & CEO at Netter (P26). Studied Mathematics and CS at Mines Paris.
Netter helps companies regain control over their data so teams can pilot their activities with high precision.
Netter deploys data science agents to mid-market companies to clean data and build operational solutions like payment reconciliation, HR anomaly detection, and occupancy forecasting across disconnected systems.
▲ 5
AI Cost Management: Track and attribute AI spend across every provider
Agent harnesses for asset managers
