Contract Intelligence for Fixed Income: From Rules to AI-Driven Document Understanding

Customer

Challenge

Extracting Structure from Unstructured Documents

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.

• Document variability: Layouts and formats differed widely across issuers and jurisdictions; many exceeded standard model context limits
• Accuracy and auditability: Solution needed traceable, field-level results with explanations and evidence links
• Hybrid approach: Certain extractions could rely on deterministic logic, while others required contextual interpretation
• Operationalization: Deliver production-ready system with versioned models, human review, and privacy safeguards

Solution

Hybrid AI Document Intelligence Pipeline

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.

Document ingestion & layout understanding

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.

Clause & entity extraction

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.

Question answering & summarization

Implemented retrieval layer for document search and explainable summarization. Answers reference verified text spans to maintain grounding and compliance.

Quality, privacy, and governance

Integrated data-validation rules and confidence thresholds. Added privacy filters and configurable allow/deny lists. Stored evidence snapshots for audits.

MLOps & delivery

Automated workflow using Airflow orchestration and containerized microservices. Adopted MLflow for version tracking. Built lightweight review interface for human validators.

Technologies & tools

Process

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.

1. Discovery
2. Rule baseline
3. Model integration
4. Retrieval & summarization
5. Human-loop & deployment
6. Pilot & expansion

Results

• High Precision: Rule-augmented ML extraction
• Reduced Review Time: Automated routing of confident cases
• Audit-Ready: Evidence-linked extractions

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.