Your model knows everything.
Except your domain.
Domain-specific web data for supervised fine-tuning and RAG grounding. 200+ ready-to-use data packages. Custom extraction from any public source. Always fresh, always structured, always compliant.
Trusted by 20,000+ customers and 70% of AI labs
Two ways to improve your model
Fine-tuning vs. RAG.
Both need better data.
Whether you are retraining weights or retrieving context at runtime, the quality of your data determines the quality of your output.
Teach your model new skills
Fine-tuning adjusts model weights using domain-specific examples. The model permanently learns new patterns, product categorization, review sentiment, industry terminology, compliance language.
Give your model live context
RAG retrieves external data at inference time and injects it into the prompt. The model answers using current, verified information, no retraining required. Reduces hallucinations. Keeps answers fresh.
How it works
From raw web to
training-ready data.
Structured extraction pipelines that output SFT datasets and RAG-ready documents.
Choose your data source
Pick from 200+ ready-to-use data packages or define a custom source. Trustpilot reviews, Amazon products, LinkedIn profiles, Reddit threads, SEC filings, any public source.
Extract & structure
Web Unlocker handles anti-bot defenses. Data is extracted, cleaned, and structured into the format your training pipeline expects. JSON, JSONL, CSV, or custom schemas.
Deliver & refresh
One-time snapshots for fine-tuning runs. Continuous feeds for RAG vector stores. Data lands in your S3, GCS, or vector database, on your schedule.
See how domain-specific data improves model accuracy.
Browse data packages →200+ data packages by vertical
Pre-built extraction pipelines for the most common fine-tuning and RAG use cases. Free samples available for every package.
| Vertical | Fine-tuning use case | RAG use case | Sources | Scale |
|---|---|---|---|---|
E-commerce | Product categorization, review sentiment, price prediction | Live pricing, inventory, competitor monitoring | Amazon, Shopify, eBay | 800M+ products |
Social & reviews | Sentiment analysis, intent detection, topic classification | Trending discussions, brand mentions, user feedback | Reddit, Trustpilot, G2 | 1B+ posts/day |
News & media | Summarization training, NER, event extraction | Breaking news grounding, press monitoring | 50K+ outlets | Continuous |
Jobs & talent | Job matching, skills extraction, salary prediction | Live listings, company insights, market signals | LinkedIn, Indeed, Glassdoor | 20M+ listings |
Finance & legal | Document understanding, compliance classification | SEC filings, earnings data, regulatory updates | SEC, courts, exchanges | Daily |
Real estate | Price estimation, property description generation | Live listings, market comparables, agent data | Zillow, Realtor, MLS | 50M+ listings |
Free samples for all packages. Custom verticals and extraction schemas on request.
The problems that kill
model accuracy.
Generic models with stale data produce generic answers. Your customers notice.
Your model answers with year-old information. Customers get outdated prices, discontinued products, wrong regulations.
Continuous web data feeds keep your RAG pipeline grounded in today's reality. SERP API delivers fresh search results at inference time.
Generic base models hallucinate on domain-specific questions. They guess instead of knowing your vertical.
Fine-tune on 200+ domain-specific data packages, real reviews, real products, real industry language. Your model stops guessing.
Human labelers are slow and expensive. 10,000 labeled examples costs $1,000+ and takes weeks. Quality varies by annotator.
Web data is naturally labeled. Review stars = sentiment. Product categories = classification. Job titles = NER. Extract structured labels at scale.
Your RAG pipeline embedded data 3 months ago. Prices changed, products launched, regulations updated. Answers are confidently wrong.
Scheduled data refreshes keep your vector store current. Daily, weekly, or continuous, your embeddings reflect the live web.
Plug into your existing pipeline.
Data packages output SFT-ready JSONL or RAG-ready markdown with source metadata. Works with any vector store, any training framework, any cloud.
from brightdata import SyncBrightDataClient
# pip install brightdata-sdk
with SyncBrightDataClient() as client:
# Collect Trustpilot reviews via Web Scraper API
reviews = client.scrape.trustpilot.reviews(
url="https://trustpilot.com/review/revolut.com"
)
# Transform to SFT format (instruction/input/output)
sft_data = []
for review in reviews.data:
sft_data.append({
"instruction": "Classify the sentiment of this review",
"input": review["text"],
"output": "positive" if review["rating"] >= 4 else "negative"
})
# Export as JSONL for fine-tuning
from brightdata.datasets import export
export(sft_data, "training_data.jsonl")Start with a free sample dataset.
Get free sample →Find the right sources
before you fine-tune.
Discovery tools help you locate domain-specific content at scale, so your training data covers the right topics with the right depth.
SERP 100
Get the first 100 Google results for any query, not just 10. Build comprehensive training sets from search intent. Map entire topics by scraping deep into the SERP.
Discover API
High-recall discovery engine that finds relevant domains and pages based on your intent, not just keywords. Feed it a description of what you need and get ranked URL lists ready for extraction.
Training data you
can defend in court.
In 2024, Bright Data won court cases against Meta and X, the first web scraping company to be scrutinized in U.S. federal court. Twice. And win.
All data is publicly available. Collection complies with GDPR, CCPA, and all applicable data protection regulations. SOC 2 Type II, ISO 27001:2022, ISO 27017, ISO 27018, and CSA STAR certified.
View Trust CenterWon cases against Meta and X in U.S. federal court. Legal precedent for ethical web data collection for AI training.
Annual audit of security controls, availability, processing integrity, confidentiality, and privacy.
International standard for information security management systems.
Full compliance with EU and California data protection regulations. DPA available for enterprise.
“Bright Data's browser infrastructure helps scale our AI agents for complex tasks. It lets us focus on delivering real value to our customers instead of wrestling with browser infrastructure.”
Common questions
Generic models give generic answers.
Domain-specific web data for fine-tuning and RAG. 200+ packages. Free samples.
No credit card required for free tier