WEB DATA EXTRACTION & ONTOLOGY MAPPING

AI-powered data extraction turns scattered data into structured knowledge, while multi-agent systems autonomously research, validate, and map it into your business systems.

From scattered internet sources to a structured knowledge base

AI agents research, gather, and
cross-reference data across the web and documents, mapping it into your ontology using AI data extraction and knowledge structuring.

Multi-agent orchestration at scale

A coordinator agent plans and delegates tasks to specialized agents (research, parsing, profiling), enabling scalable data enrichment and intelligent document processing without manual intervention.

Evidence-backed, schema-validated outputs

Every attribute is supported by sources, quotes, and confidence scores, then validated against strict schemas before entering your enterprise data systems.

THE PROBLEM

Organizations across industries need to build structured databases that go far beyond basic records. Whether it’s profiling restaurants, enriching property listings, mapping competitors, or building candidate databases, the challenge is the same: turning scattered, unstructured information into a consistent data model.

Manual research does not scale

Teams spend hours navigating websites, reading documents, and cross-referencing sources to populate a single entity. This effort becomes repetitive and difficult to maintain as the dataset grows.

Information is scattered and inconsistent

The data needed to populate one entity's profile lives across websites, review platforms, social media, map services, PDFs, and images. No single source has the full picture, and formats vary wildly.

Data quality degrades at volume

As the database grows, maintaining consistency across thousands of entries becomes unmanageable. Different analysts interpret the same information differently, leading to inconsistent tagging and classification.

Existing tools miss the nuance

Traditional web scrapers and data aggregators can pull raw text, but they cannot make qualitative assessments, interpret context across sources, or map findings to a complex ontology with hierarchical tags, confidence levels, and evidence trails.

Applicable across domains

Used in domains where data is scattered: hospitality (restaurant profiles, venue attributes, menu data), real estate (property enrichment, neighborhood analysis), recruitment (candidate profiling, company research), market intelligence (competitor analysis, industry mapping), healthcare (provider profiling, facility attributes), travel and tourism (destination databases, experience cataloging), and e-commerce (product enrichment, supplier profiling).

THE SOLUTION

An AI-powered multi-agent data extraction system that takes an entity identifier and available source materials as input, then autonomously researches, extracts, profiles, and structures the complete entity data against a predefined ontology.

Core capabilities

STEP

Autonomous task planning

Before any extraction begins, the orchestrator agent creates a structured plan of all tasks needed (document parsing, research areas, entity profiling, final compilation), then tracks progress through each step.

STEP

Parallel document extraction

Source documents (PDFs, images, web pages) are processed simultaneously, extracting structured data fields from any format using vision-capable models that understand layout and context, not just raw text.

STEP

Multi-source research

Internet search engines, platform APIs, entity websites, and review platforms are queried and cross-referenced to build a comprehensive picture of each entity's attributes.

STEP

Dynamic skill loading

The research agent loads domain-specific investigation strategies on demand. Each ontology dimension follows its own playbook, defining which sources to prioritize, what evidence to look for, and how to evaluate findings.

STEP

Ontology mapping and entity profiling

Extracted information is matched against a curated ontology of predefined tags, ensuring alignment with the target data model. Sub-entities (such as menu items, property features, or organizational roles) are independently analyzed and structured.

STEP

Evidence-backed outputs and validation

Every tag, classification, and assessment includes source URLs and direct quotes, creating a full audit trail from raw source to structured data, with outputs validated before entering the system.

What sets it apart

Orchestrated multi-agent architecture

Rather than relying on a single model, specialized agents handle different tasks such as document parsing, research, and profiling. An orchestrator coordinates them based on what information is available at each step.

Skill-based research strategies

Each ontology dimension follows its own investigation playbook, defining which sources to prioritize, what evidence to look for, and how to evaluate findings. New dimensions can be added by defining a new skill, without changing the underlying system.

Confidence and evidence scoring

Every tag and attribute is assigned a confidence level and backed by evidence. This allows downstream systems to distinguish between
well-supported findings and lower-confidence assessments, and to apply approval thresholds where needed.

Validated and resilient output

Each agent produces output validated against strict schemas before it moves to the next stage. The system also uses different AI models depending on the task, with retry and fallback mechanisms built in to support reliability.

See how this would integrate into your current architecture

How quality is measured

Evaluation

Extraction quality is evaluated across multiple dimensions
(from document parsing accuracy to tag precision) using a combination of curated test datasets and production monitoring.

Dataset approach

A curated set of entities with known attributes serves as ground truth, covering diverse entity types, source complexities, and ontology coverage levels
Each test case includes verified source data, expected tags, correct classifications, and validated research outputs
The dataset grows as new edge cases are encountered (eg. unusual document formats, entities with sparse online presence, or rare attribute combinations)

Online validation

Production extractions are reviewed by domain experts, and corrections are tracked to identify systematic errors.
Accuracy trends are monitored per ontology dimension to surface regressions early.

Key metrics

End-to-end accuracy measures how much of the ontology is successfully populated for each entity.
Per-field accuracy gives individual scores for each extraction target (dates, amounts, entities, categories) to pinpoint exactly where improvements are needed
Entity matching accuracy determines how often the system links extracted data to the correct record in the target system
AI-assisted evaluation is used for subjective or borderline cases (e.g., equivalent but differently structured outputs), a secondary AI model acts as a quality judge

Why this approach

This approach ensures quality is measured across the full data pipeline, not just at a single level
It allows issues to be identified early, directs improvements to the most impactful areas, and supports continuous iteration without requiring manual review of every case

Architecture

Core System Integrations

Search and Discovery APIs

Internet search tools query review platforms, entity websites, and domain-specific sources to gather qualitative and factual information.

AI Models (LLM)

Multi-provider architecture using different models for orchestration, parsing, research, and profiling, each chosen for
cost-performance fit.

Document Processing

Handles diverse source formats (PDFs, images, web pages) through vision-capable models that interpret layout, typography, and content.

Cloud Infrastructure

Scalable compute that handles parallel agent execution across document parsing, research queries, and entity profiling simultaneously.

Observability & Tracing

Full logging of agent decisions, tool calls, and intermediate results for debugging and quality auditing.

Database and Catalog

Tag catalogs, entity databases, and structured ontology definitions that agents match against, ensuring consistency across all extractions.

IN PRODUCTION

It's already running in an app dedicated to healthy food

Collab AI · UK · Food & Lifestyle

We turned scattered internet sources and unstructured documents into structured, validated data.

A multi-agent system plans the extraction, researches across sources, processes documents, and maps results into your data model. Each agent handles a specific task, coordinated by an orchestrator that adapts to available information.

The result is production-ready structured data, backed by evidence and confidence scores, built at a scale that manual research cannot match.

Technical case-study coming soon

Clear answers FOR

Common Client Concerns

”How do we know the AI isn’t making up data?”

CORE FEAR

Fabricated data entering the production database, leading to incorrect entity profiles and eroded trust.

How it’s addressed:

Every tag, classification, and attribute is backed by evidence (source URLs and direct quotes from the original material). No tag is assigned without a traceable justification.
Confidence scoring (high, medium, low) is applied to every finding, letting downstream systems enforce thresholds (e.g., only auto-approve high-confidence tags, flag the rest for review).
Tags are matched against a predefined catalog rather than invented by the model. The system cannot introduce classifications that don't exist in the schema.
A separate extraction agent independently validates tags from the research conversation, providing a second-pass validation layer.

”Our ontology is very specific, can this handle it?”

CORE FEAR

A generic AI tool that produces shallow outputs that don't align with a detailed, domain-specific data model.

How it’s addressed:

The system is built around the client's ontology from the ground up. Tag dimensions, entity categories, classification hierarchies, and attribute types all map directly to the target schema.
Each dimension is mapped to your data model, and research strategies are tailored accordingly. New dimensions can be added without changing the system architecture.
Schema validation at every agent boundary ensures outputs conform to the exact data structure expected. Structurally invalid data is rejected before it reaches the database.

“What if there is very little data available?”

CORE FEAR

The system produces empty or unreliable profiles for entities with limited web presence, creating gaps in the database.

How it’s addressed:

The multi-source approach (search engines, platform APIs, entity websites, source documents) means the system rarely depends on a single source. Even entities with minimal web presence typically have some platform data and source documents.
When data is limited, confidence scoring reflects that. Missing information is left empty rather than guessed, ensuring data integrity.
Source documents (menus, catalogs, brochures) are rich data sources. Sub-entity profiling works entirely from document content, independent of online presence.
The system reports what it finds honestly rather than fabricating attributes to fill gaps. Empty fields are preferable to fabricated data.

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