NeoSpace provides NeoData, a unified platform that links raw structured and unstructured data directly to real‑time AI predictions without custom pipelines. It includes built‑in dataset governance, versioning, and traceability, integrates model training and evaluation, and delivers ultra‑low‑latency, high‑throughput inference that can be pushed into existing business systems.
Funding
$18M 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 face fragmented data pipelines and high engineering overhead when building AI models on massive, mixed structured and unstructured datasets, leading to slow experimentation, inconsistent training data, and latency‑bound inference that cannot scale to real‑time business needs.
Solution
NeoSpace offers NeoData, a unified platform that connects raw data directly to real‑time AI predictions without requiring custom pipelines. The system provides built‑in dataset governance, traceability, and versioning to keep training data consistent and reproducible. Integrated model training and evaluation accelerate experimentation, while the inference engine delivers ultra‑low‑latency, high‑throughput predictions that can be pushed into existing business systems. By modeling structured and unstructured data together, NeoData enables enterprises to scale from billions of rows to petabytes of information while maintaining reliable production‑ready AI.
Target Audience
Primary customers are data science, machine learning, and analytics teams within large enterprises that need to operationalize AI at scale across diverse data sources.
Features
- End‑to‑end workflow that links dataset creation, model training, and real‑time inference in a single environment
- Built‑in data governance, lineage, and reproducibility tools for both structured and unstructured data
- High‑throughput inference engine delivering up to 5× faster response times across massive datasets
- Scalable architecture capable of processing billions of events and petabytes of data with ultra‑low latency
- Automatic synchronization between training data and production inference to ensure model consistency
- API and connector framework for seamless integration of predictions into existing business applications