1-2days
Free sample dataset turnaround after scope confirmation - test quality before committing
Managed Data Service · Data for AI
Domain-Specific WebData —Delivered Ready for YourAI Pipeline.
Structured, deduplicated web datasets for LLM fine-tuning, RAG knowledge bases, and AI agent monitoring — scoped to your exact domain, delivered in JSONL, Parquet, or directly to your warehouse. No scraper to build. No pipeline to maintain.
Fine-tuning · RAG · Agent data feedsJSONL · Parquet · S3 · BigQueryDeduplicated · normalized · provenance-taggedFree sample · No infra required
Common Crawl has everything. Your model needs the right 1%. Tell us your domain, target sources, and required fields — Octoparse scopes the collection workflow, handles deduplication and normalization, and delivers a production-ready dataset without a single scraper for you to build or maintain.
200+
Source types and domains covered - from product reviews to financial content to technical forums
99.9%
SLA-backed delivery reliability for recurring dataset feeds and agent monitoring pipelines
From$699
Per dataset - $599/month recurring delivery - enterprise custom scope available
4.8/5
G2 rating - verified B2B data service reviews
4.7/5
Capterra rating - trusted by global data teams
Three workflows, one service
What AI teams actually use this for.
Fine-tuning a model, building a RAG application, or powering an AI agent — the bottleneck is always the same: clean, domain-specific, structured data that's actually ready to ingest. Generic crawls aren't it. DIY pipelines take months. This is the managed alternative.
Fine-tuning & Training Data
Domain-specific text corpora for LLM fine-tuning, continual pre-training, and RLHF datasets — scoped to your exact sources. Product reviews, forum discussions, news articles, technical documentation, industry content. Delivered deduplicated, normalized, and provenance-tagged with token count estimates included.
Best for: domain adaptation, instruction tuning, evaluation dataset creation, RLHF preference data collection.
JSONLParquetCSVS3 / GCS
RAG & Knowledge Base Feeds
Recurring structured content feeds from your target sources — news, documentation, product pages, regulatory content, competitor sites — delivered with timestamps, source URLs, and consistent schema for direct ingestion into vector databases or retrieval pipelines. Set the cadence; we handle the rest.
Best for: LLM applications needing fresh knowledge, enterprise knowledge bases, citation-grounded AI products.
JSONLJSONBigQuerySnowflake
AI Agent Data Feeds
Structured, event-like signals for AI agents that need to monitor the real world — price changes, new product listings, competitor moves, job posting patterns, news events, regulatory filings. Delivered on a recurring schedule your agent can ingest directly, without building or maintaining a single collection workflow.
Best for: autonomous agents, competitive intelligence systems, market monitoring AI, real-time pricing engines.
JSONREST APIWebhookDB push
Why not the alternatives?
Every AI team hits the same wall. Here's what the other paths actually cost.
There are four common approaches to getting domain-specific web data. Three of them have hidden costs that most teams only discover after months of lost engineering time.
Octoparse vs. the alternatives — how they actually compare
Common Crawl
- The whole web — not your domain
- Raw HTML requiring a major pipeline to parse and clean
- Noise ratio makes training data quality unpredictable
- Monthly release cadence — not aligned to your schedule
DIY Scraping
- Weeks to months of engineering before first usable dataset
- Ongoing maintenance as source sites update or block
- Your team owns dedup, normalization, and QA pipeline
- Scales with headcount, not with data requirements
Bright Data / Apify
- Tools — you still write the collection and parsing logic
- You build and maintain the normalization pipeline
- Infra cost plus engineering time plus ongoing monitoring
- No managed service — you operate it
Octoparse managed
- Domain-specific, scoped to your exact sources and fields
- Structured, deduplicated, provenance-tagged output
- JSONL / Parquet / warehouse-ready from day one
- Recurring delivery — no infra for your team to maintain
About this service
Octoparse Data for AI is a managed web data service that provides domain-specific, structured datasets for LLM fine-tuning, RAG knowledge bases, and AI agent data pipelines. Teams define their target domains, sources, and required fields; Octoparse handles collection, deduplication, normalization, and structured delivery in JSONL, Parquet, CSV, or direct push to S3, GCS, Snowflake, or BigQuery. Unlike generic crawls such as Common Crawl, Octoparse delivers purpose-built corpora with full provenance metadata — source URL, publish timestamp, language tag, domain classification, and token count estimates — on a one-time or recurring schedule. Supported data types include product listings and reviews, news and editorial content, financial commentary, job postings, forum and community discussions, technical documentation, company profiles, and social media content. Projects start from $699/project for one-time datasets and $599/month for recurring feeds. Free scoped samples are delivered within 1–2 business days.
LLM fine-tuning dataRAG knowledge base feedsAI agent data pipelinesJSONL · Parquet · S3 · BigQueryDeduplicated · provenance-taggedFree sample · no commitment
Data quality for AI
The difference between data that trains a better model and data that corrupts one.
Quality issues in training data compound during fine-tuning. Duplicates inflate model confidence on repeated patterns. Schema inconsistencies break ingestion pipelines. Missing provenance makes evaluation impossible. Octoparse applies a structured QA pipeline before every delivery.
Deduplication
Exact and near-duplicate detection applied across every batch. For recurring deliveries, deduplication runs against prior batches — only new or meaningfully changed records are included in each update, keeping your training corpus clean as it grows.
Provenance metadata
Every record includes source URL, domain, publish timestamp, and language tag. Essential for RAG citation grounding, training data filtering by source credibility, and evaluation dataset construction with controlled source distribution.
Language detection
Detected language and script are tagged at the record level — enabling locale filtering, multilingual training set composition, and per-market data separation without post-processing on your side.
Schema consistency
Field names, types, and formats are standardized across all sources in every delivery. One schema, regardless of how many source sites are in scope — so your ingestion pipeline handles one format, not one per source.
Token count estimates
Approximate token counts are included per record — useful for training budget planning, context window management in RAG, and dataset composition decisions before fine-tuning runs begin. Basis: GPT-4 tokenizer.
Pre-delivery QA
Every delivery is reviewed for completeness, structural integrity, and field coverage before shipment. If there are gaps in scope coverage, we flag them rather than delivering partial data silently — and re-scope before proceeding.
Delivered schema
Standard delivery schema — every field an AI pipeline needs, included by default.
Fields are confirmed at project scoping based on what is publicly accessible from your target sources. The schema below represents a standard text-content corpus delivery; structured data (prices, listings, profiles) follows a separately defined schema per project.
idsourceurldomainpublish_timelangtitletexttoken_countcategoryauthorregion
| Field | Description | Example value |
|---|---|---|
| id | Unique record identifier, stable across batches | r_20260408_001 |
| source | Source domain or publication name | techcrunch.com |
| url | Canonical URL of the source content — provenance for RAG and evaluation | https://techcrunch.com/2026/04/... |
| domain | Topic or industry classification label | technology / ai / startups |
| publish_time | ISO 8601 UTC timestamp of original publication | 2026-04-08T09:14:00Z |
| lang | Detected language code (BCP 47) | en / zh-CN / ja / ko |
| text | Extracted and cleaned body text, normalized encoding | The funding round brings total... |
| token_count | Approximate token count (GPT-4 tokenizer basis) | 487 |
| category | Source-assigned or inferred content category | Funding / Product launch / Analysis |
JSONL — LLM training standardParquet — large-scale processingCSV / JSONS3 / GCS — direct bucket pushSnowflake · BigQuery · RedshiftREST API — for agent ingestionBuilding at model scale? For teams requiring multi-domain corpora, custom deduplication strategies, PII filtering pipelines, or integration with existing ML infrastructure — we scope these separately with a dedicated data expert.
Representative workflow · one engagement of many
How AI teams get from data sourcing problem to first fine-tuning run — without a data engineering sprint.
Featured Case Study
How a fintech AI startup built a domain-adapted LLM for investment research without hiring a data engineer
LLM fine-tuningFinance domain corpusJSONL · GCS delivery
A fintech AI startup building a domain-adapted LLM for investment research needed a high-quality financial text corpus — earnings analysis, investor forum discussions, market commentary, and company news — structured, deduplicated, and ready for fine-tuning. Building their own collection pipeline was estimated at 2–3 months of engineering time and would require ongoing maintenance as source sites changed. Their ML team needed to focus on model work, not data infrastructure.
Scope
Initial one-time corpus of 500,000+ records across financial news sites, investment forums, and earnings-related content — JSONL format with source URL, timestamp, domain tag, and token count per record. Follow-on recurring weekly delivery of new content to keep the training corpus current over time.
What Octoparse delivered
First scoped sample dataset within 2 business days. Full corpus delivered in JSONL, deduplicated to ~94% of raw collection volume, with complete provenance metadata and consistent schema across all sources — pushed directly to the team's GCS bucket, ready for the fine-tuning pipeline without transformation steps.
Business outcome
The ML team went from data sourcing problem to first fine-tuning run in under two weeks — without building or maintaining a single scraper. Recurring weekly delivery keeps the corpus fresh without engineering involvement after initial setup.
RAG knowledge base for a legal AI product
A legal tech startup building a contract analysis AI needed weekly updates of case law summaries, regulatory filings, and legal commentary — normalized, timestamped, and chunked for ingestion into a Pinecone vector store. Recurring delivery keeps their retrieval layer current without manual curation cycles.
E-commerce AI agent with live pricing signals
An AI pricing agent needed structured, recurring price and inventory data across 50+ competitor product pages — delivered in JSON on a daily cadence, directly to the agent's ingestion API. No scraper to maintain; the agent focuses on decision logic, not data collection.
Multilingual training data for a global NLP model
An NLP team needed domain-specific text in English, Chinese, Japanese, and Korean — language-tagged, schema-consistent, delivered in Parquet for large-scale preprocessing. Language detection and per-locale filtering enabled controlled dataset composition without additional post-processing.
Public workflow datasets & engineering case studies
See how Octoparse turns messy public web data into AI-ready structured outputs.
These case studies show the same data engineering principles AI teams need: hard-source collection, multi-source crawling, schema normalization, signal evaluation, visual matching, warehouse delivery, review buckets, and public workflow datasets for technical evaluation.
Hard-source ecommerce dataset
Temu pricing data pipeline case study for ecommerce intelligence
Review how Octoparse operated a managed Temu pricing and inventory pipeline for a de-identified ecommerce intelligence client, delivering 8M+ monthly records in Phase 1, scaling toward 16M+ records, and shipping JSONL outputs to Snowflake with QA controls.
Temu pricing dataJSONL to Snowflakeworkflow dataset
Read the Temu pricing data pipeline case studyPublic workflow datasetMulti-platform product matching case study for furniture and appliance retail
See how Octoparse crawls public data from Wayfair, The Home Depot, Lowe's, Walmart, Target, and other retail sources, normalizes every platform into one product schema, and uses multi-signal plus visual matching to identify true product matches.
retail product matchingnormalized product schemavisual matching dataset
Read the multi-platform product matching case studyAI visual matching proofAI visual product matching workflow for noisy marketplace candidates
Review a managed visual matching workflow that turns noisy candidate retrieval, pre-vision filtering, wrong-part rejection, visual scoring, and structured output buckets into an AI-ready workflow preview.
AI visual matchingpre-vision filteringstructured output
Read the AI visual product matching workflowFAQ
Questions AI and ML teams ask before their first dataset.
Your model's quality ceiling
is your data quality ceiling.
Octoparse delivers domain-specific, structured web datasets for AI teams who need the right data — not the whole internet — ready to ingest without a data engineering project in between.
From $699/project · $599/month recurring · Enterprise custom scope · Free sample in 1–2 days