DaBot provides an AI‑driven ingestion platform that automates data onboarding by detecting schema changes, data drift, and applying reusable data‑quality rules stored in a centralized SmartHub. The system replaces hand‑coded pipelines with self‑healing bots, integrates with existing warehouses and orchestration tools, and can reduce manual pipeline work by up to 70 %. This enables data engineering and DataOps teams to accelerate reliable data delivery to analysts.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises spend a large portion of data engineering time building and maintaining custom ingestion pipelines, coping with frequent schema changes, data drift, and undocumented data quality rules. This manual effort drives high operational costs and slows the delivery of reliable data to analysts and business users.
Solution
DaBot delivers an Intelligent Ingestion platform that replaces hand‑coded pipelines with AI‑driven bots. The bots ingest data from internal and external sources, automatically detect and adapt to schema and data drift, and apply reusable data‑quality and mapping rules stored in a centralized SmartHub. By learning from historical runs, metadata, and user feedback, the system can automate up to 70 % of manual onboarding tasks, delivering a 10× speedup for typical data acquisition workflows. The platform integrates with existing data infrastructure, enabling self‑healing pipelines and a single canonical model that consolidates diverse sources. As a result, data engineers can redirect effort from routine pipeline maintenance to higher‑value analytics and insight generation.
Target Audience
The primary customers are data engineering and DataOps teams in mid‑size to large enterprises that need to scale data onboarding while minimizing manual effort, as well as data scientists and analysts who rely on timely, high‑quality data feeds.
Features
- AI bots that continuously learn from data, metadata, prior executions, and user inputs to drive automated ingestion decisions
- Automatic detection and remediation of schema changes and data drift, providing self‑healing pipeline behavior
- SmartHub repository for centralized storage, versioning, and sharing of data‑quality rules, mappings, and transformation logic across teams
- One‑click (5‑click) data onboarding workflow supporting a wide range of source formats and target canonical models
- Ability to replace thousands of legacy pipelines with a single bot deployment, reducing infrastructure complexity
- Real‑time monitoring dashboard showing pipeline health, drift alerts, and resource utilization metrics
- Seamless integration with existing data warehouses, lakes, and orchestration tools via standard connectors and APIs
- Cost‑reduction analytics quantifying build‑time savings (up to 70 %) and maintenance‑time reductions (up to 45 %)