Stop rejecting good orders because the price didn't match a table
Celthrac's agentic AI resolves three-way match failures the way your best analyst would — by reading the contract, understanding the deviation, and posting the correction. Autonomously.
Stop rejecting good orders because the price didn't match a table
Celthrac's agentic AI resolves three-way match failures the way your best analyst would — by reading the contract, understanding the deviation, and posting the correction. Autonomously.
What it costs today
Every rejected order is a finance analyst opening Salesforce, hunting for the negotiation email or contract attachment, cross-checking fulfilment in SAP, and manually deciding whether the discrepancy is legitimate. At enterprise order volumes, that's thousands of hours a year spent reconstructing context a machine threw away.
Why rule-based fails
Deterministic iPaaS matches on hardcoded tables. The moment a price reflects a side agreement that isn't in ERP master data, the pipeline has no path forward — it cannot read the email that authorized the discount, and it cannot reason about whether the deviation is valid. It fails closed.
What the agent does
When a three-way match fails, our agent activates. It retrieves the unstructured negotiation logs and contract attachments from Salesforce, interprets the pricing-deviation logic, validates the fulfilment record in SAP S/4HANA, and confirms whether the discrepancy is legitimate. Instead of rejecting the entry, it drafts a correcting ledger adjustment or a debit note — routed to finance for one-click validation behind a human-in-the-loop gate.
Reasoning, memory and action — not another rule.
Reads what rules can't.Contracts, emails and attachments become decision inputs, not dead ends.
Reasons instead of rejecting.The agent explains why a price is valid, with a citation trail.
Acts within governance.Corrections above threshold queue for human approval — autonomy without exposure.
What this pattern returns.
You recover the analyst hours currently spent on manual reconciliation, you accelerate cash collection by clearing the exception backlog, and you protect revenue that was previously written off or delayed because no one had time to chase the paper trail. Days-sales-outstanding drops; finance reclaims month-end.
This isn't a rule engine with a chatbot on top. We engineer the reconciliation logic as a cognitive agent on Celigo — the right runtime for SAP-centric estates with strict schemas and audit requirements — so every autonomous decision is governed, logged, and explainable. That's what AI-First looks like in finance operations.
One of 15 agentic AI use cases for CRM, ERP & billing.
Every one converts a recurring source of manual labour, revenue leakage or compliance risk into a governed, autonomous workflow.
From this patternto your platform.
Same approach. Same governance. Your stack next — bring the constraints, leave with the path.