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Data for AI
★★★★★
4.8 / 5
★★★★★
4.8 / 5
Fully managed data acquisition for AI teams.
Forage AI finds, collects, structures, verifies, and maintains data from difficult websites, complex documents, and long-tail sources. Delivered in the schema your training, retrieval, evaluation, and grounding workflows require.
60M+
URLs monitored yearly
10M+
Documents monitored yearly
99.7%
Extraction accuracy
Ongoing
Pipeline maintenance
Sample schema
Your sources. Your schema. Ready for your pipeline.
We turn web, document, and multimodal sources into structured records aligned to your fields, formats, metadata, and chunking rules.
Document record
- Document ID and source URL
- Type, language, and version
- Structured text by section and page
- Tables and document hierarchy
- Published and updated dates
Web record
- Page URL and title
- Main content and custom fields
- Author, entity, and date
- Links and media references
- Crawl and update status
Multimodal record
- Asset ID and modality
- Image, audio, or video URL
- OCR text or transcript
- Captions and annotations
- Duration, dimensions, or format
Entity record
- Canonical name and ID
- Attributes and identifiers
- Relationships and aliases
- Cross-source matches
- Confidence score
Evaluation record
- Input and reference output
- Labels and scoring rubric
- Review and adjudication status
- Training, validation, and test split
- Dataset version
Quality and provenance
- Source URL and collection timestamp
- Extraction method
- Validation status
- Review status
- Lineage and version history
Need custom fields?
JSONL, JSON, CSV, Parquet, or a project-specific format. Delivered through S3, APIs, secure files, or your warehouse.
Built for AI teams
Built for difficult sources and long-tail coverage.
One managed pipeline across dynamic websites, scans, long documents, complex tables, regional sources, and multimodal content.
Mapped
The source universe, mapped.
60M+URLs monitored yearly, long tail included
Regions
Languages
Publishers
Document types
Long-tail sources
+ your list
One pipeline
Difficult inputs, handled.
⧉
Dynamic pages rendered and parsed in full
4,182
◷
Scans and long documents read with structure kept
11,904
↻
Complex tables extracted intact
7,331
Handled within the same pipeline
Measured
Quality, measured before delivery.
99.7%
Maintained
Pipelines that keep working.
Unmaintained pipeline
24% healthy
Maintained by Forage AI
99% healthy
Source health checks
Extraction updates
Revalidation
Recurring delivery
+ your cadence
Scoped up front
Controls agreed before we build.
Agreed in scoping
source_access
permitted_use
retention
deployment
Forage AI enforces
approved_sourcesonly
usage_flagsper record
retention_rulesapplied
model_termstracked
Your team defines the dataset. Forage AI builds, runs, and maintains the pipeline.
Why Forage AI
One team for the full AI data pipeline.
Web, documents, and multimodal data. Collect text, tables, images, charts, audio, and video through one managed engagement.
Source discovery included. Map known, regional, and long-tail sources before the pipeline is built.
Built to your data model. Fields, metadata, chunking, labels, and dataset splits follow your downstream requirements.
Quality measured clearly. Coverage, recall, field completion, and accuracy are tracked as separate measures.
Refresh and maintenance managed. Source changes, format drift, pipeline failures, and recurring delivery remain with Forage AI.
Delivered into your stack. Receive JSONL, JSON, CSV, Parquet, or custom outputs through S3, APIs, secure files, or your warehouse.
Use cases
Build with verified data.
Use one managed data operation across model development, evaluation, and production.
Training and post-training data
Domain-specific web and document corpora, filtered, deduplicated, and structured for pretraining, fine-tuning, or continued training.
RAG and grounding data
Structured records with source metadata, document hierarchy, and refresh rules, built around your retrieval and chunking design.
Evaluation and ground truth
Reference sets labeled to your rubric, reviewed for consistency, and versioned for repeatable model, agent, and retrieval evaluation.
Vertical and regional expansion
Add geographies, languages, domains, or document classes without building a separate acquisition operation for each one.
Case study
Fintech data pipeline
150K+
Transaction and deal records delivered
11K+
Firms processed
2.8M
News articles analyzed
95%
Data quality benchmark
The approach
“An AI-powered deal intelligence pipeline that continuously finds, validates, and enriches deal activity across public sources, turning scattered signals into structured, production-ready intelligence, delivered daily.”
Resources
All resources →
Plan and evaluate an AI data pipeline.
Use these resources to scope the data gap, compare delivery options, and estimate the cost of usable data.
E-book
How to Get Started With Your Data Acquisition Strategy for AI
Assess data-acquisition maturity, define the gap, and compare the available delivery models.
Get the ebook →
Pricing guide
Unpacking Data Extraction Pricing
Compare data options using recall, accuracy, cleanup, and maintenance rather than price per attempted record.
Read the guide →
Free tool
Data Extraction TCO Calculator
Estimate usable records, internal cleanup costs, and the true cost of competing data-delivery options.
Open the calculator →
Forage AI is best suited to production-scale projects where the main challenge is finding, extracting, structuring, verifying, or maintaining data from web and document sources.
Common projects include training corpora, RAG inputs, evaluation sets, grounding data, and expansion into new domains, languages, or regions. Small one-time pulls or labeling-only projects may be better suited to a self-service tool or specialist labeling provider.
Coverage can include public websites, approved portals, filings, reports, PDFs, scans, tables, images, audio, video, databases, and other project-relevant sources.
Multilingual and multimodal inputs can be included where the source and intended use are feasible. The available source coverage and any limitations are confirmed during discovery.
Forage AI maps the target source universe and tests the pipeline on a representative sample before production.
Coverage, recall, field completion, and known misses are measured separately from accuracy. This shows both whether the required data was found and whether the extracted values were correct.
Quality is tested against project-specific field definitions and reviewed sample records.
Automated validation and confidence scoring run at scale. Uncertain records and selected QA samples receive human review. Acceptance criteria, reporting, and escalation rules are agreed before full delivery.
Yes. Fields, metadata, chunking, labels, identifiers, dataset splits, and versioning can be mapped to the existing data model.
Outputs can be delivered in JSONL, JSON, CSV, Parquet, or a custom format through S3, APIs, secure files, databases, or a warehouse. Refresh cadence and pipeline maintenance are also defined during scoping.
Source access, licensing, permitted use, retention, privacy, third-party model use, and deployment requirements are reviewed before the pipeline is built.
The final workflow is limited to approved sources and designed around the client’s data-governance and security requirements.
Start foraging
Turn the data gap into a managed pipeline.
Tell us which sources, regions, document types, or use cases remain expensive to cover and maintain. We will scope the sources, quality bar, delivery method, and refresh plan.
- Source health check using your actual targets
- Coverage and delivery plan scoped to the use case
- Sample output before full rollout
Get in touch