← Glossary / Information Extraction

What is Information Extraction?

Information extraction is the automated process of retrieving structured, machine-readable facts from unstructured or semi-structured text. In modern scraping pipelines, it bridges the gap between raw HTML fetching and database ingestion. Instead of relying purely on brittle CSS selectors, AI-driven extraction uses language models to identify entities, relationships, and attributes directly from the semantic content of the page, making pipelines resilient to DOM changes.

NLPEntity ResolutionLLM ParsingSchema MappingUnstructured Data
// 02 — definitions

Structure from
chaos.

How we turn paragraphs of human-readable text into typed database rows without writing a thousand regex rules.

Ask a DataFlirt engineer →

TL;DR

Information extraction (IE) transforms raw text into structured data. Traditional IE relied on regex and DOM paths, which break the moment a site redesigns. Modern IE uses LLMs and NLP to map semantic meaning to a strict JSON schema, drastically reducing maintenance overhead while handling edge cases that break deterministic parsers.

01Definition & structure
Information extraction (IE) is the subfield of Natural Language Processing (NLP) concerned with pulling structured data from unstructured text. In a scraping context, it replaces or augments DOM parsing. Instead of writing a rule that says "get the text inside the third <div class="specs">", IE models read the entire text block and output a JSON object containing the requested fields, inferring the values from semantic context.
02How it works in practice
A modern IE pipeline takes raw HTML, strips the boilerplate to isolate the main content, and feeds it to an inference engine alongside a strict JSON schema. The model performs Named Entity Recognition (finding the nouns), Relation Extraction (linking the nouns to attributes), and outputs a structured record. A validation layer then checks the output against the schema to ensure type safety before writing to the database.
03The shift to LLM-based extraction
Historically, IE required training custom models (like Spacy or CRF) for every new domain. Today, Large Language Models act as zero-shot or few-shot extractors. You simply define the schema and provide the text. This has shifted the engineering burden from training bespoke NLP models to optimizing prompts and managing inference latency.
04How DataFlirt handles it
We treat AI extraction as a fallback layer, not the primary engine. Our pipelines attempt deterministic extraction first. When a site layout changes and selectors fail, the pipeline automatically routes the page to our internal, fine-tuned 8B parameter extraction models. This repairs the pipeline in real-time, preventing data loss while alerting our engineers to update the deterministic rules for the next run.
05The hallucination risk
The biggest danger in LLM extraction is silent hallucination — the model confidently returning a value that isn't in the source text. To combat this, production pipelines must use constrained decoding (forcing the model to only generate tokens valid for the schema) and prompt instructions that explicitly require the model to return null if the information is absent, rather than guessing.
// 03 — extraction metrics

How accurate
is the model?

Information extraction is evaluated using standard NLP metrics. DataFlirt tracks precision and recall per field across every AI-driven pipeline to ensure schema compliance and data fidelity.

F1 Score = 2 × (Precision × Recall) / (Precision + Recall)
The harmonic mean of precision and recall. The primary metric for extraction quality. Standard NLP Evaluation
Precision = True_Positives / (True_Positives + False_Positives)
When the model extracts a price, how often is it actually the price? Standard NLP Evaluation
DataFlirt Confidence Threshold = P(field_match) > 0.95
Records falling below this probability score are routed to human-in-the-loop review. DataFlirt extraction SLO
// 04 — llm extraction trace

From raw text
to typed JSON.

A live trace of our hybrid extraction worker parsing a messy real estate listing where the CSS selectors failed due to a site update.

hybrid-extractschema-validationfallback-triggered
edge.dataflirt.io — live
CAPTURED
// input context
source.url: "https://target-realty.com/listing/8492"
parser.primary: failed // selector .price-tag not found
fallback.trigger: "llm-extract-v4"

// raw text payload
text.chunk: "Beautiful 3bd/2ba home in downtown Austin. Asking $1.2M. HOA is $400/mo."

// llm inference
model.target: "df-extract-instruct-8b"
schema.enforced: "PropertyListing_v2"
latency.inference: 340ms

// structured output
extract.bedrooms: 3
extract.bathrooms: 2
extract.price_usd: 1200000
extract.hoa_usd: 400

// validation
schema.compliance: passed
record.status: committed
// 05 — failure modes

Where AI extraction
goes wrong.

Ranked by frequency across DataFlirt's AI-assisted pipelines. LLMs solve selector rot, but they introduce new, probabilistic failure modes that require strict schema validation to catch.

PIPELINES MONITORED ·   140+ hybrid
EVALUATION WINDOW ·  ·    30d trailing
UPDATED ·  ·  ·  ·  ·  ·  2026-05-19
01

Hallucinated values

% of AI errors · Model invents a plausible value not present in text
02

Type coercion failures

% of AI errors · Returning '1.2M' instead of integer 1200000
03

Context window truncation

% of AI errors · Target entity exists outside the token limit
04

Implicit coreference failure

% of AI errors · Model maps attribute to the wrong subject
05

Multi-language semantic drift

% of AI errors · Poor extraction on non-English source text
// 06 — our architecture

Deterministic fallback,

AI when you need it, code when you don't.

LLMs are incredibly powerful for information extraction, but they are slow, expensive, and probabilistic. DataFlirt uses a hybrid extraction architecture. We run fast, deterministic parsers first. If a selector fails or a required field is missing, the pipeline dynamically routes the raw text to a fine-tuned, schema-constrained extraction model. This gives you the resilience of AI with the unit economics and latency of traditional scraping.

Hybrid extraction worker

Live status of a single record passing through the fallback extraction layer.

pipeline.stage hybrid-extract
parser.primary css-selectorfailed
parser.fallback df-extract-instruct-8b
fallback.trigger selector_rot_detected
llm.latency 340ms
schema.validation passed
record.status committed

Stay ahead of the pipeline

Data engineering
intel, weekly.

Anti-bot shifts, scraping infrastructure updates, dataset delivery patterns, and business outcomes from our pipelines. Short, technical, no fluff.

By submitting, you acknowledge our Privacy Notice.

// 07 — FAQ

Common
questions.

About NLP, LLM parsing, hallucination risks, and how DataFlirt scales AI extraction in production.

Ask us directly →
What is the difference between web scraping and information extraction? +
Web scraping is the entire pipeline: fetching the HTML, bypassing anti-bot systems, and storing the result. Information extraction is the specific step of taking the raw fetched content (like a block of text) and pulling out structured entities (like names, prices, or dates) to fit a database schema.
Do you use LLMs for all data extraction? +
No. Running an LLM on every page of a 10-million-record catalog is financially ruinous and completely unnecessary. We use deterministic CSS/XPath selectors for 98% of extractions. We use LLMs as a fallback when selectors break, or for highly unstructured fields like parsing product specifications out of a free-text description.
How do you prevent LLM hallucinations? +
Through strict schema enforcement and temperature control. We run our extraction models at temperature 0 to ensure deterministic outputs. More importantly, we use constrained decoding — the model is forced at the token level to output valid JSON that matches your exact schema types. If it hallucinates a string where an integer belongs, the validation layer catches it immediately.
Can information extraction handle PDFs or images? +
Yes. For non-HTML sources, the pipeline adds a preprocessing step. We use Document AI and OCR to convert the visual layout into a text stream, which is then fed into the extraction model. The spatial coordinates of the text are often preserved to help the model understand tabular data.
Is AI extraction legally different from regular scraping? +
The act of fetching the data carries the same legal considerations regardless of how you parse it. However, using scraped data to train a commercial LLM is highly contested right now. If you are just using an LLM to extract facts (like pricing) for your own database, it generally falls under standard data extraction precedents. Always consult counsel for your specific use case.
How does DataFlirt scale this without massive GPU costs? +
We don't use GPT-4 for routine extraction. We use smaller, fine-tuned open-weight models (like 8B parameter variants) deployed on our own inference infrastructure. Because they are fine-tuned specifically for JSON schema extraction rather than general chat, they are faster, cheaper, and often more accurate for this specific task.
$ dataflirt scope --new-project --target=information-extraction READY

Tell us what
to extract.
We do the rest.

20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed across millions of records — we scope, build, and operate the pipeline.

Start a pipeline → View pricing
hello@dataflirt.com  ·  Bengaluru  ·  IST  ·  typical reply < 4h
// 08 — keep reading

Related glossary terms

AI Scraping

Named Entity Recognition (NER)

The foundational NLP task of identifying specific entities within unstructured text.

Read →
AI Scraping

Relation Extraction

Mapping how extracted entities connect to each other to build knowledge graphs.

Read →
AI Scraping

Few-Shot Extraction

Prompting an LLM with a small number of examples to define the extraction schema.

Read →
AI Scraping

Document AI

Applying extraction models to visually complex formats like PDFs and scanned invoices.

Read →