Structured Data from Your Documents. Precision You Can Trust.
The YellowPad API extracts structured data from unstructured documents with greater precision than traditional document AI solutions.
Join us in early access.
The document AI industry has it backwards
Traditional solutions depend on static models. When document types change, teams must gather labeled data and retrain. YellowPad API inverts this model entirely.
Traditional IDP Solutions
- Requires labeled training data for each document type
- Costly retraining cycles when formats change
- Rigid templates break with document variations
- Black box outputs with no explainability
- Vendor lock-in to specific model providers
Our Approach
- Classify document, select schema families
- Deploy modular extraction agents per document type
- Validate and reconcile into normalized metadata layer
- Full traceability to prompts and evidence
- Works with any LLM provider
Modular Extraction Architecture
Modular extraction agents adapt to each document type, enabling higher precision, faster onboarding, and automatic scalability.
Schema-Driven Precision
Traditional document AI treats each field independently. YellowPad uses schema families to ensure every extraction is consistent, validated, and bound to your data model. No more inconsistent outputs or data quality issues.
- Schema-enforced consistency
- Cross-field validation
- Structured metadata output
Modular Extraction Architecture
Modular extraction agents are optimized for each document type, delivering higher precision and faster onboarding.
- Optimized extractors per document type
- Automatic scalability
- Higher precision extraction
Future-Proof Architecture
Continuous validation loops ensure ongoing quality improvement. Always powered by the latest and most capable LLMs — automatically.
- Automated quality validation
- Continuous evolution
- Always powered by the latest LLMs
What Sets Us Apart
YellowPad API delivers precision, adaptability, cost efficiency, explainability, and continuous improvement, all while remaining LLM-agnostic.
Precision & Explainability
Schema-bound output ensures consistent, accurate metadata. Every extraction can be traced to its prompt, schema, and evidence span; no black boxes, complete auditability.
- Schema-enforced consistency
- Full traceability to source evidence
- Enterprise-grade auditability
Adaptability & Cost Efficiency
Plug in new document types or schema fields instantly with no model retraining, no labeled data collection, no costly ML cycles.
- Zero retraining required
- Instant schema updates
- Dramatic cost reduction
LLM Agnostic & Self-Improving
Works with any model: OpenAI, Anthropic, Gemini, or your own. Continuous validation and refinement through automated testing, improving without touching the underlying models.
- Any LLM provider supported
- Continuous prompt evolution
- Continuous validation & improvement
Metadata Infrastructure for Enterprise AI
YellowPad reimagines Intelligent Document Processing with industry-leading accuracy and source traceability.
Legal Operations
Automatic extraction of clauses, parties, terms, and governing law from contracts, NDAs, and amendments.
Procurement & Compliance
Identify risk and key obligations across contracts with consistent metadata for analytics and governance.
Financial Operations
Consistent metadata capture for deal documents, invoices, and amendments across repositories.
Our Vision
"Intelligent Document Processors should be deterministic, predictable, and easily auditable."
We're in early beta and carefully onboarding partners who share our vision. Request access to join us in building the metadata backbone for AI-driven business systems.