Audit-ready AI for financial operations
Finance
LLM pipelines for financial reporting, variance analysis, and audit-ready narratives — with number-grounding validation and regulatory guardrails built in.
What We Build
We apply LLM technology to the structured, high-stakes parts of financial operations where accuracy is non-negotiable: report generation, variance commentary, budget-versus-actual analysis, and risk summaries. Every output is grounded in source data and signed with a full audit trail.
Core Capabilities
Financial Report Generation Automated narrative drafting from ERP or warehouse data — MD&A commentary, variance explanations, and period summaries. Output schemas validated against input figures before any text leaves the pipeline.
Number-Grounding Validation Every figure referenced in a generated narrative is cross-checked against the input payload. Discrepancies trigger structured retries, not silent errors. No inference, no estimation.
Regulatory Guardrails Prompt templates are reviewed against applicable disclosure conventions (IFRS, local GAAP). Forward-looking language is gated behind explicit data authorisation. Version-pinned models prevent style drift across reporting periods.
Variance & Attribution Analysis Structured decomposition of budget-versus-actual gaps, segment-level attribution, and cohort comparisons — delivered as validated JSON or formatted narrative depending on downstream requirements.
Finance-Specific Standards
- All generated figures trace back to a validated source payload — no model-inferred numbers
- Prompt templates are compliance-reviewed and change-controlled
- Model version is pinned in production; updates require explicit re-approval
- Audit log covers input hash, prompt version, model version, and output hash
- Forward-looking statements require explicit authorisation in the input schema
Related Use Cases