NEXTGRES provides product and growth teams with a self‑service personalization engine that lets them define audience segments in natural language, preview experiences, and deploy recommendations without relying on engineering pipelines. By handling data signals, model training, and integration internally, it reduces personalization rollout from months to hours, enabling faster conversion, engagement, and retention improvements.
Funding
Funding not disclosed
Founders
Product
Problem
Product and growth teams often cannot launch personalization initiatives quickly because relevant user data is scattered across multiple systems, and building the necessary data pipelines and ML infrastructure requires extensive engineering effort and long backlog cycles.
Solution
NEXTGRES offers a self‑service personalization engine that connects read‑only to existing databases and event streams without requiring new pipelines or schema changes. Teams define audience segments using natural language, preview the resulting experiences, and deploy real‑time recommendations within hours. The platform automatically identifies and ingests the behavioral signals needed for personalization, providing explainable recommendations that product and growth teams can control directly, eliminating dependence on data engineering or ML teams.
Target Audience
Primary customers are product managers, growth marketers, and analytics teams at consumer‑facing digital platforms that need fast, data‑driven personalization without relying on engineering resources.
Features
- Read‑only, schema‑agnostic connectors to existing databases and event streams, avoiding any changes to source systems
- Automatic detection and real‑time ingestion of the most relevant behavioral signals for each personalization use case
- Natural‑language interface for defining audience segments, enabling non‑technical users to create targeting rules quickly
- Live preview and observation tools that let teams see how recommendations will appear before they are shipped
- Explainability dashboard that shows why each recommendation was made, supporting trust and iterative optimization
- End‑to‑end deployment workflow that moves from definition to production in hours rather than weeks or months