Automate LIHTC Compliance with AI-Powered Income Recertification

Affordable housing compliance teams rely on PDF readers, calculators, and manual data entry into property management software. A specialist opens a pay stub, finds the YTD earnings, divides by the number of pay periods, multiplies to project future income, and keys the result into RealPage or AppFolio. This is slow and prone to errors, especially with variable hourly wages, tips, or bonuses.

A common failure scenario involves a 15-person firm processing a 500-unit LIHTC lease-up. They receive 3,000 applications, each with 5-10 income documents. Manually reviewing one applicant takes 25 minutes. If 10% of files contain calculation errors, that forces 300 files to be reworked, which risks fair housing complaints and delays building occupancy for weeks.

Generic OCR software fails because it cannot interpret financial context. These tools extract text like "Gross Pay: $1,250.45" but cannot distinguish a bi-weekly paycheck from a one-time bonus. The extracted data is useless for projecting the next 12 months of income, creating more correction work than the tool saves.

Syntora's approach to automating LIHTC compliance documentation and income recertification would begin with a discovery phase. This phase focuses on understanding your specific document ingestion needs, whether from an online application portal or a designated email inbox, and integrating with your existing workflows.

The core of the system would involve a document processing pipeline. We have experience building similar pipelines using Claude API for processing financial documents in other sectors, and the same pattern applies to LIHTC documentation. Claude API would be configured to parse unstructured documents like PDFs, JPEGs, and even photos of pay stubs, extracting key data points such as document type, employer name, pay periods, YTD totals, and hourly rates. We would work with your team to define and achieve specific accuracy requirements for these extractions.

Income projection logic, developed in Python, would then calculate anticipated 12-month income. This logic would account for various income sources, including hourly wages (often using a 2080-hour multiplier), salaried income, and variable sources like tips or commissions, potentially incorporating historical data where available. For properties requiring layered HOME funds, the system would be designed to automatically flag applicants for asset verification based on predefined thresholds.

This specialized logic would be encapsulated within a FastAPI service, designed for deployment on cloud infrastructure like AWS Lambda. This architecture provides on-demand, cost-effective processing capabilities. The service would then be integrated with your property management software, such as RealPage or AppFolio, to push the calculated annual income and assigned AMI bucket (e.g., 50% AMI) directly into custom fields via their respective APIs. This integration aims to reduce manual data entry and potential transcription errors.

To ensure compliance and auditability, every calculation and decision made by the system would be logged to a Supabase database, creating a clear audit trail. Furthermore, a human-in-the-loop mechanism would be a critical feature: if the Claude API confidence score for a document falls below a predefined threshold, the system would flag that document for human review within a simple dashboard. This ensures a compliance specialist can address edge cases requiring expert judgment. The delivered system would include this dashboard and the full codebase.