RAG + rule validation with source-level traceability
We built an NLG engine designed to transform complex financial activity into clear, compliant monthly summaries for both customers and analysts.The platform was created to convert raw transactions, account activity, and financial metrics into human-readable narratives that could be easily understood while still maintaining the traceability and compliance standards required in regulated financial environments.
Ptera implemented the AI infrastructure behind the workflow, combining retrieval-augmented generation, rule-based validation, and source-level traceability to produce intuitive financial summaries with built-in auditability and human review support.
Challenge & Strategy
- Deliver plain-English monthly expense/investment summaries that are intuitive and meet compliance standards.
- Convert raw transactions into human-readable narratives with full traceability acceptable to analysts/regulators.
Technical Approach & Integration
- RAG + rule-based validation; every sentence links to its underlying data for internal audit.
- LLM drafts clear summaries of transactions and key metrics.
- Guardrails prevent unsupported statements and enforce financial disclosure rules.
- Integrated into mobile “Monthly Summary” and analyst portal with a continuous feedback loop for fine-tuning.
