Your CFO Wants AI In the Product.
Your Engineers Have Never Trained 
A Model On Financial Data.

Financial accuracy requires more engineering around the model than the model itself. We build retrieval-augmented generation grounded in the customer's actual financial data, implement structured output validation against known financial relationships, add confidence scoring and fallback handling for low-confidence outputs, and build audit trails so every AI-generated number is traceable to a source.

Multi-tenant financial AI has specific architecture requirements: customer data isolation in the retrieval layer, tenant-specific financial schema configuration, and security boundaries that prevent cross-tenant data exposure in LLM context windows. We architect for these from the first sprint. Retrofitting tenant isolation into an AI system is expensive and risky.

NetSuite, Sage Intacct, QuickBooks Online and Desktop, SAP S/4HANA and ECC, Microsoft Dynamics 365, Oracle EBS, Xero, and most mid-market accounting systems. We also build custom connectors for proprietary systems and data warehouses. ERP integration is usually the first build for FP&A AI because without clean source data, the model is working on noise.

Almost always a distribution mismatch. The training or test data does not match real customer financial data structure, accounting conventions, or edge cases. A manufacturing company's chart of accounts looks nothing like a SaaS company's. We run a data distribution analysis, identify the failure modes, and rebuild the affected pipeline with proper data grounding rather than tuning the model on top of a structural problem.

A focused AI feature, one NL interface, one automated reporting pipeline, or one anomaly detection system, typically starts at $55K. A full AI layer including NL queries, scenario modeling, automated reporting, and production inference infrastructure runs $140K to $380K. Fixed-scope estimates after one discovery sprint. No billing surprises.

We have built NL-to-query systems on financial schemas, natural language P&L explanation and variance commentary generators, and conversational planning interfaces. Financial NLI requires schema-aware prompting, structured output validation, handling of ambiguous financial terminology across different accounting standards, and careful scope limiting so the system does not answer questions it cannot reliably answer. We have production patterns for all of these.