Neuralk provides a single API that generates instant classification, regression, or time‑series predictions on large, mixed‑type tabular datasets without requiring users to train models or build pipelines. Leveraging a foundation model pretrained on billions of tables, it automatically handles missing or noisy data, performs feature engineering, and delivers scalable results (up to 50 M rows) with auditability and flexible deployment options.
Funding
$4M 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
Enterprises often need to generate accurate predictions from large, messy tabular datasets but must invest significant time and resources to build, train, and maintain custom machine‑learning pipelines, especially when handling millions of rows and heterogeneous feature types.
Solution
Neuralk offers a single API that delivers instant classification or regression predictions on structured data without requiring users to train models or construct data pipelines. Its foundation model is pretrained on billions of tabular datasets, enabling it to understand patterns across rows and columns and to handle mixed data types at scale. Users simply upload their dataset (up to 50 million rows) and receive predictions within seconds, with built‑in handling of incomplete or noisy data. The service can be deployed in the cloud, on‑premises, or in a hybrid environment, ensuring data never leaves the organization without explicit permission. An agentic layer performs automated feature engineering and iterative refinement, providing auditable, compliant outputs suitable for production use.
Target Audience
Primary customers are data science and ML engineering teams within large enterprises that need rapid, scalable predictions on high‑volume structured data, such as finance, retail, manufacturing, and healthcare organizations.
Features
- Pretrained tabular foundation model covering classification, regression, and time‑series tasks
- One‑call API delivering predictions for up to 50 M rows of mixed‑type data in seconds
- Automatic handling of missing, dirty, or heterogeneous data without manual preprocessing
- Agentic feature‑engineering loop that autonomously creates and documents new features
- Flexible deployment options: cloud, on‑premises, or hybrid, with TLS‑encrypted connections
- Built‑in auditability and compliance tooling for enterprise governance