AI citation and explainability refer to an AI system’s ability to show how it reached a result and where each piece of information came from. In financial and administrative workflows, where accuracy, compliance, and accountability are non-negotiable, this capability is essential. It transforms AI from a “black box” into a transparent, auditable assistant that teams can trust.
In practice, AI citation means linking every extracted field, classification, or decision back to the exact source inside a document: a line on a PDF invoice, a clause in a contract, a cell in a spreadsheet, or a reference in an ERP export. Instead of receiving raw outputs, users can click and view the original evidence that supports each result.
Explainability goes a step further. It enables the AI to articulate why it made a given decision:
- Why a specific amount was extracted
- Why a line item was classified under a certain category
- Why a discrepancy was flagged
- Why two documents were matched
For finance teams dealing with invoices, purchase orders, delivery notes, payments, or contracts, this is not theoretical, it directly impacts compliance, audit readiness, and operational trust. When an AI system can justify its actions, teams can safely automate more steps while maintaining full control.
Phacet integrates citation and explainability by design. Each AI agent automatically connects its outputs to underlying document sections and surfaces confidence scores, enabling auditors, controllers, and operators to review results with complete clarity. This transparency is a core reason why Phacet is used for high-stakes workflows such as supplier billing control, 3-way matching, treasury reporting, and contract intelligence.
For a practical illustration of explainability in action, see how Phacet structures and validates contractual information in extract key terms from contracts at scale, where every extracted clause is tied back to its original source for full auditability.
