Service · Automation
Overview
They are not good at being the system of record, doing arithmetic reliably, or making decisions that need accountability. So we put AI where it reads and drafts, and keep the ERP — and a person — in charge of what gets posted.
01The problem
Teams key supplier invoices, packing lists and customer POs into the ERP line by line.
Shared mailboxes mixing orders, queries, complaints and spam, sorted by hand.
Agents open order, shipment, invoice and warranty screens to answer one question.
Managers wait days for answers that exist in the data.
02Our approach
We start by picking two or three use cases with measurable volume and clear success criteria — for example, the percentage of vendor bills extracted correctly without edits. We test candidate models on a sample of your real documents before building anything, because accuracy varies widely by document type.
Production builds keep AI as a step in a workflow: extraction creates a draft, matching proposes links, drafting suggests text. A person approves, and the ERP records the action. Prompts and outputs are logged against the record they relate to, so you can audit and improve over time.
03Capabilities
Extract header and line data from supplier documents and match to POs and receipts.
Turn emailed or PDF orders into draft sales orders with product matching.
Classify incoming messages and route them to the right queue or record.
Draft replies grounded in the customer's orders, deliveries and contracts.
Summaries of account history, follow-up suggestions and quote drafts.
Plain-English questions answered from approved, permission-aware data views.
04Technical considerations
The AI only sees data the requesting user is entitled to see in the ERP.
Confidence thresholds decide what goes straight to review and what can be auto-accepted, if anything.
Model providers selected for data residency, privacy terms, accuracy and cost — and swappable.
A held-out set of real documents used to measure accuracy before and after every change.
05Process
Find high-volume, text-heavy tasks with clear success measures.
Test extraction or drafting accuracy on a sample of your real data.
Build the workflow into the ERP with review steps and logging.
Track accuracy, edits and throughput; refine prompts and rules.
06Outcomes
Staff review and correct rather than type from scratch.
Support and sales answers drafted with the right context.
Human approval, permissions and audit trails stay in place.
Where it applies
Related services
Insights
A grounded look at AI in ERP: document processing, support and sales assistance, natural-language reporting — plus the limits, risks, controls and how to choose a first use case.
FAQ
For many document types, extraction accuracy is high, but it varies by layout and quality. That is why we measure accuracy on your documents first and keep a review step for anything that posts to the ledger.
That depends on the provider and plan chosen. We select options with appropriate privacy terms and regional processing where required, and document the data flow.
Yes. Matching extracted lines to products or vendors works poorly against duplicated or inconsistent master data. Cleansing is often the first phase.
No. Rules-based automation remains the right tool for deterministic tasks. AI is added where inputs are unstructured.
Next step
We will identify the use cases with genuine payback and test them on your data.