AI automation for Manufacturing

A manufacturer received purchase orders by email, EDI and a customer portal, each in a different format. Two staff spent much of their week re-keying orders into the ERP, and transcription errors caused occasional shipment mistakes and downstream disputes.

We mapped the end-to-end order flow, quantified the hours and error rate, and identified order intake as the highest-return, lowest-risk workflow to automate first. The design kept a human reviewing only exceptions.

An intake service ingests orders from email, EDI and the customer portal, an AI extraction layer normalises them against the product catalogue, and a validation-and-routing layer posts confirmed orders to the ERP while queuing exceptions for review.

We built an AI-assisted intake pipeline that reads incoming orders regardless of layout, extracts and validates line items against the product catalogue, matches them to the right account, and posts confirmed orders into the ERP. Anything ambiguous is routed to a person with the source document and a suggested resolution.

• Mapped the current order flow and baselined volume, effort and error rate.
• Built AI-assisted extraction that reads orders regardless of format.
• Added validation against the catalogue and correct account matching.
• Integrated posting to the ERP with an exception queue for staff.
• Rolled out alongside manual processing until proven, then switched over.

The routine share of orders now flows into the ERP without manual entry, order handling is markedly faster, and transcription errors on automated orders have effectively disappeared. Staff shifted from typing orders to handling genuine exceptions and customers.

Starting with a single, well-bounded workflow made the value measurable within weeks and built the internal confidence to expand automation into invoice matching next.

Extend the same intake-and-validation pattern to supplier invoice matching and automated order acknowledgements.

Supplier invoices arrived in many formats and were matched to purchase orders and keyed into finance by hand. The process was slow, prone to duplicate payments, and difficult to audit.

We mapped the accounts-payable process, baselined its cost and exception rate, and rebuilt it so the common path is automatic and only true exceptions reach a person — with a complete audit trail throughout.

An intake layer captures supplier invoices, an AI extraction layer structures the data, a three-way matching engine reconciles invoice, purchase order and receipt, and an approval-and-posting layer routes exceptions and writes to finance, with every action logged for audit.

We automated invoice capture, AI data extraction, three-way matching against purchase orders and receipts, approval routing, and posting to the finance system, with every action logged for audit.

• Mapped accounts payable and baselined cost and exception rate.
• Built invoice capture and AI data extraction.
• Implemented three-way matching against POs and receipts.
• Automated approval routing and posting to finance with audit logging.
• Rolled out with duplicate and mismatch detection.

Processing cost and cycle time fell, duplicate and mismatched payments were caught before approval, and finance gained a clean, audit-ready trail on every invoice.

Automating a whole process — including exception handling and audit logging — delivers far more value than automating the single step of data entry.

Add supplier self-service and cash-flow reporting on the structured invoice data.