Fda Document Analyzer Data Extractor
Construction Financial Data Extractor
Messy construction financial reports → structured contract-level metrics extraction → consistent, auditable financial summaries.
01 — The Problem
Construction financial documents (e.g., WIP schedules) are dense, inconsistent, and calculation-heavy, making it difficult to reliably extract key metrics across contracts. Errors or inconsistencies in interpreting costs, revenue, and completion percentages can lead to poor financial visibility and reporting risk. Without a structured approach, analysis is slow, manual, and prone to misinterpretation.
02 — What the AI Does
* Extracts structured financial data from construction documents at the contract level * Calculates missing values using predefined accounting logic (e.g., POC, GP, cost relationships) * Cross-checks multiple calculation pathways for consistency * Summarizes contract-level and portfolio-level metrics * Provides explicit citations to source locations in the document Built on GPT-5.3 with a highly constrained system prompt that encodes construction accounting logic (e.g., JTD, GP, POC relationships) and strict extraction rules. It does not rely on external tools or databases—its behavior is entirely governed by prompt instructions and user-provided documents.
03 — Design Decisions
Embedded financial calculation hierarchy (Preferred vs Alternative methods)
To ensure consistent, auditable outputs even when documents vary in completeness
Prevents arbitrary inference by forcing the model to follow defined calculation paths
Mandatory citation requirement (page + section)
To make outputs verifiable and reduce hallucination risk in financial contexts
Disallows unsupported claims; forces traceability to source material
Contract-by-contract analysis before aggregation
Reflects how construction financials are actually managed and reviewed
Prevents premature summarization and ensures granular accuracy
Explicit prohibition on guessing or filling gaps
Financial accuracy is prioritized over completeness
Model must return “unclear or missing” rather than infer
Domain-specific instruction set (JTD, GP, POC definitions and formulas)
Aligns model reasoning with construction accounting standards instead of generic financial interpretation
Limits flexibility but increases domain precision
Structured output schema (fixed fields for each contract and totals)
Ensures consistency across analyses and usability for downstream workflows
Reduces adaptability to non-standard document formats
No tool or retrieval augmentation
[Creator: add rationale]
Limits analysis strictly to provided documents; no external validation
Emphasis on transparency in calculations and ambiguity reporting
[Creator: add rationale]
Forces the model to expose reasoning rather than just output results
05 — Key Insight
High-stakes AI extraction systems require constrained reasoning frameworks and enforced traceability, not just stronger models.