Production AI for invoice finance.

Multi-model routing. Each task goes to the model that fits it on quality, latency and cost. Document extraction, long-document analysis and conversation each use a different model, with fallbacks, so no single provider dictates the system.

Retrieval augmented generation. The assistant answers from a curated, embedded knowledge base via semantic search, so responses stay grounded in Grapple's own policies rather than model guesswork. MCP extends its reach to live platform data, not just static documents.

Incremental assessment architecture. An early design forced every document type through one large assessment flow. It was hard to trace and it duplicated work, so we replaced it. Each document now yields structured data into a shared, persisted history per entity, and assessments get richer and more accurate with use.

Structured LLM scoring. Adverse signals are scored by an LLM that returns structured JSON with a score and a reason against a defined risk lens. The output is machine-usable and auditable, not free text.

Cost, latency and observability. LLM results are cached. A dedicated analytics lakehouse powers dashboards without loading the primary store, and every LLM call is tracked and debuggable in production.