Nettle provides an AI‑driven data platform that automatically ingests raw data, infers schemas, and generates production‑ready batch and streaming pipelines from natural‑language use cases, eliminating manual coding. The cloud‑native engine includes a self‑healing runtime and consensus‑based validation to ensure 100 % data consistency and zero‑maintenance operation for enterprise data engineering teams.
Funding
Funding not disclosed
Founders
Product
Problem
Data engineers typically allocate over half of their time to building, debugging, and maintaining complex data pipelines, which leads to inconsistent schemas, frequent job failures, and prolonged development cycles. These inefficiencies hinder timely data delivery and increase operational overhead for enterprises that rely on batch and streaming analytics.
Solution
Nettle delivers an AI‑driven autonomous data platform that eliminates manual pipeline construction by ingesting raw data, automatically indexing and validating it, and generating production‑ready transformations from natural‑language use cases. The system builds a comprehensive data model, resolves inter‑pipeline dependencies, and deploys the entire workflow in minutes without any code. A built‑in consensus algorithm guarantees 100 % data consistency across runs, while continuous self‑healing mechanisms detect and remediate errors in real time. The cloud‑native execution engine scales elastically to billions of events, providing zero‑maintenance operation for both batch and streaming workloads. Engineers interact through a web UI or API, receiving instant feedback on data quality and lineage, which accelerates time‑to‑value and frees resources for higher‑impact analytics.
Target Audience
The primary customers are enterprise data engineering teams in sectors such as financial services, retail, and e‑commerce that manage large‑scale batch and real‑time pipelines, as well as product groups that need rapid, code‑free data transformation capabilities.
Features
- Natural‑language interface that translates business use cases into a full data model and transformation code automatically
- Automatic schema inference, indexing, and validation with consensus‑based convergence to ensure 100 % consistency
- Self‑healing runtime that monitors pipeline health, identifies anomalies, and applies corrective actions without human intervention
- Zero‑code orchestration engine that constructs dependency graphs, retry logic, and ordering for complex batch and streaming jobs
- Cloud‑native, auto‑scaling execution environment capable of processing billions of events with sub‑second latency
- Integrated data lineage, audit logs, and compliance reporting for regulatory‑heavy industries
- Secure, role‑based access control and end‑to‑end encryption for data in transit and at rest
- RESTful API and SDKs (Python, Java) for seamless integration with existing data warehouses, lakes, and BI tools