AI Agents

Context

Built for banking within the Commercial lending stack, this agent is used when preparing SME and corporate credit files to evidence that projected cash flows are grounded in actual customer contracts or purchase orders. Typical scenarios include working-capital and term facilities where revenue assumptions hinge on signed agreements, renewals with new anchor contracts, and transactions where concentration, tenure, or termination clauses matter for serviceability.

What it does

The agent reads customer contracts, purchase orders, and schedules, then links them to the borrower’s projections. It extracts counterparties, contract identifiers, start/end dates, renewal/termination terms, price/quantity schedules, delivery or milestone conditions, and invoicing/payment terms. It reconciles these with the cash-flow model—confirming timing, amounts, and cadence—and highlights gaps such as expired or unsigned agreements, mismatched volumes/prices, missing appendices, or clauses that undermine assumed continuity (e.g., termination for convenience). Each check is returned with a concise rationale and a link to the exact clause or table used, so reviewers can verify the tie-out quickly.

Core AI functions

Document classification for contracts, POs, MSAs, SoWs, and schedules; clause and table extraction with normalization of dates, amounts, currencies, and units; counterparty/entity extraction; cross-document consistency checks between agreements and projections; rule evaluation for concentration limits, minimum terms, and dependency risks; and reason-code generation that explains mismatches (“price per unit differs from projection,” “contract end date precedes loan tenor”). Confidence scoring flags low-certainty items and creates targeted exceptions instead of halting the file.

Problem solved

Analysts often rebuild the link between projections and underlying agreements by hand, risking errors and inconsistent treatment of tenure, pricing, and termination. Missing or outdated contracts slip through, creating last-minute rework before committee.

Business impact

Credit preparation becomes faster and better evidenced: projected revenue is tied to real obligations, assumptions are transparent, and rework drops. Committees see a clearer basis for serviceability, concentration, and tenor decisions—supporting better credit decisions and smoother approvals.

Integration and adjacent use cases

Integration is light–moderate: ingest contracts/POs and schedules from DMS or deal rooms; write validations, tie-outs, and prompts into the lending workflow or credit workbench—no changes to cores required

Common combinations in this stack include:

• Financial statement analyzer (standardized historicals and ratios),

• Business plan & use-of-funds validator (purpose and KPI coherence),

• Legal entity & shareholding validator (counterparty and group alignment),

• Collateral & security package validator (to ensure liens and encumbrances align with receivable rights),

• Cash-flow & repayment capacity (DSCR) (to assess servicing based on confirmed cash flows), and

• Commercial lending completeness agent (to confirm the pack is committee-ready).