Combine traditional scraping with LLM-powered extraction to collect structured data from unstructured, irregular, and complex web content — where rule-based parsers fail. Entity extraction, relationship mapping, and schema-driven data collection from any web source.
LLM-assisted web scraping combines traditional HTML extraction with large language model inference to extract structured data from web content that rule-based parsers cannot reliably handle. Conventional scrapers work by identifying specific HTML elements — a price in a span with a given class, a title in an h1 tag — and extracting their text. This works well for consistently structured pages. It fails completely when pages are irregular, content is embedded in free text, the same information appears in different formats across pages, or the data to be extracted requires semantic understanding rather than pattern matching.
The practical scope of this problem is large. Company information pages describe team size, founding date, and products in narrative prose — not structured HTML fields. Legal filings contain party names, dates, and financial figures embedded in formal document text. Job descriptions mention required skills, compensation, and seniority in unstructured paragraphs. News articles contain entity mentions, event relationships, and factual claims that only become structured data through semantic extraction. For all of these, an LLM instruction prompt — 'extract company name, founding year, headcount, and products from this text as JSON' — dramatically outperforms any feasible rule-based approach.
DataFlirt's LLM scraping pipelines are engineered for cost-efficiency as well as accuracy. LLM inference at scraping scale is expensive if not carefully designed. We use a tiered approach: traditional extraction first for any fields that can be extracted reliably with rules, LLM inference only for fields where rules fail or confidence is low, with model selection optimised for the accuracy-cost tradeoff of each use case. For high-volume pipelines, smaller instruction-tuned models handle straightforward extraction tasks while larger models are reserved for complex understanding requirements.
The output of LLM-assisted scraping is structured JSON conforming to your defined schema — with confidence scores for extracted fields, source attribution, and optional reasoning traces. This makes the output directly usable in downstream data pipelines without additional transformation, and the confidence scores allow you to flag low-confidence extractions for human review rather than silently accepting errors.
Comprehensive extraction built for reliability, accuracy, and scale.
Define your output schema as a JSON structure and our pipeline instructs the LLM to extract exactly those fields from any page content.
Extract named entities — companies, people, locations, products — and the relationships between them from unstructured web text.
LLM-assisted extraction from PDF annual reports, legal documents, and policy files where layout-aware parsing alone is insufficient.
Every extracted field comes with a confidence score, enabling you to route low-confidence records to human review rather than accepting silent errors.
Tiered extraction architecture uses rules for reliable fields and LLM inference only where needed — minimising token cost per record.
Traditional scraping handles structured elements; LLM inference handles the remainder — combining speed and cost efficiency with semantic capability.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "status": "success", "method": "llm_assisted_extraction", "model": "gpt-4o-mini", "source_url": "example-unstructured-site.com", "prompt_tokens": 1840, "extracted": { "company": "Acme Technologies", "founded": "2018", "headcount": "120-150", "products": ["CloudSync","DataBridge"], "hq": "Bengaluru, India", "confidence": 0.96 } }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
instructor library enforces structured JSON output from LLM responses — eliminating free-text parsing and schema validation failures.
Routing logic selects the smallest capable model per extraction task — GPT-4o-mini for simple fields, GPT-4o or Claude for complex reasoning.
LangChain orchestrates multi-step extraction pipelines — content fetching, chunking, LLM inference, and output validation in a single managed workflow.
Long documents chunked intelligently at semantic boundaries before LLM inference — preserving context while respecting token limits.
Async LLM API calls with intelligent batching maximise throughput while respecting rate limits across OpenAI and Anthropic APIs.
Extraction prompts engineered and tested per use case — with few-shot examples, schema injection, and chain-of-thought for complex extractions.
From solo analysts to enterprise data teams — here's how organizations use this data.
Rule-based scrapers can handle the structured minority of the web well. The unstructured majority — narrative company pages, legal documents, news articles, analyst reports — historically required expensive human review to extract structured intelligence from. LLM-assisted extraction changes this equation: DataFlirt pipelines now extract structured data from any web content with accuracy that approaches human review, at machine speed and cost.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
