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A U.S. financial analytics firm engaged Marsbridge to convert large collections of fixed-income and legal documents into structured, searchable data. Marsbridge modernized it into a hybrid AI pipeline combining layout-aware NLP models, large-language-model components, and maintainable business rules.
The client required an end-to-end workflow to extract key terms, clauses, and entities from highly varied debt-related and legal documents, ensuring both accuracy and explainability. The project blended learning-based models with auditable rules.
Document layouts and formats differed widely across issuers and jurisdictions; many exceeded standard model context limits. The solution needed traceable, field-level results with explanations.
Marsbridge formed a focused Document-AI team—covering NLP, engineering, data, and MLOps—to build a scalable, rule-augmented ML pipeline that balances automation with control.
Unified ingestion for PDFs, scanned images, and DOCX files with OCR fallback. Introduced layout-aware encoders to capture information in tables, headers, and side notes. Defined canonical document schema for downstream analytics.
Trained domain-specific language models for named-entity and relationship extraction. Employed large-language-model interface for structured templates. Retained rule-based parsing for well-defined items. Used active learning for uncertain cases.
Implemented retrieval layer for document search and explainable summarization. Answers reference verified text spans to maintain grounding and compliance.
Integrated data-validation rules and confidence thresholds. Added privacy filters and configurable allow/deny lists. Stored evidence snapshots for audits.
Automated workflow using Airflow orchestration and containerized microservices. Adopted MLflow for version tracking. Built lightweight review interface for human validators.
Transformer models, Layout-aware encoders, LLMs, NER, Relationship extraction
Pattern matching, Business rules, Confidence thresholds
Python, FastAPI, Postgres, Airflow, MLflow, Docker
Evidence packs, Privacy redaction, Audit trails
Discovery—map document types and output schema. Rule baseline—deliver initial rule-based MVP. Model integration—extend with layout-aware and generative models. Retrieval & summarization—add search and explainable summaries. Human-loop & deployment—launch reviewer UI and monitoring. Pilot & expansion—test on live batches and iterate.
Expanded coverage of extractable fields while maintaining high precision through rule-based validation. Reduced manual review time by routing only low-confidence cases to analysts. Audit-ready outputs with supporting evidence for each extracted value. Future-proof architecture ready for cloud deployment.
Facing data chaos in PDFs and contracts? Marsbridge builds LLM-enhanced document-intelligence systems that combine automation with transparency, giving you reliable data you can govern and scale.
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