Webhound is an AI agent designed to autonomously construct datasets by extracting information from the web. This service streamlines the data acquisition process for machine learning and analytics applications. Users can quickly generate custom, structured datasets tailored to their specific project requirements.
Funding
Funding not disclosed
Founders
Product
Problem
Creating large, high‑quality training datasets from web sources requires extensive crawling, cleaning, labeling, and compliance checks, which consumes significant engineering time and expertise. Teams often resort to ad‑hoc scripts that lack scalability and produce inconsistent data, slowing model development cycles.
Solution
Webhound offers an autonomous AI agent that orchestrates the full dataset construction workflow from web data. Users specify target concepts or schema through natural‑language prompts or API calls, and the agent automatically discovers relevant sites, extracts structured content, normalizes fields, de‑duplicates records, and applies quality filters. Large language models drive semantic entity extraction and schema mapping, while built‑in compliance modules respect robots.txt, copyright policies, and data‑privacy rules. The resulting datasets are delivered in standard formats (CSV, JSONL, Parquet) with versioned storage and audit logs. Webhound integrates with common machine‑learning pipelines via a RESTful SDK, enabling seamless ingestion into TensorFlow, PyTorch, or Hugging Face workflows. Continuous monitoring updates datasets as source content changes, ensuring data freshness without manual intervention.
Target Audience
Primary customers are machine‑learning engineers, data scientists, and AI product teams that need large, web‑derived training datasets, as well as research labs and enterprises building domain‑specific models.
Features
- AI‑driven crawler that adapts navigation strategies based on target domain characteristics
- Semantic entity extraction and schema alignment powered by large language models
- Automated data cleaning pipeline: de‑duplication, normalization, missing‑value handling, and outlier detection
- Built‑in compliance engine that enforces robots.txt, copyright, and GDPR/CCPA constraints
- Versioned cloud storage with immutable snapshots and audit trails for reproducibility
- REST API and Python SDK for programmatic dataset requests, schema definition, and incremental updates
- Export options in CSV, JSONL, Parquet, and direct connectors to data lakes (AWS S3, GCP Cloud Storage)
- Dashboard for monitoring crawl progress, data quality metrics, and cost estimates in real time