Sales / AI use case
From inbox to order.
Customers order in their own words. Your order system needs unambiguous line items. NyxAI connects the two through an evidence-backed draft, explicit exceptions and a defined handoff to sales.
NyxAI · Workflow concept with illustrative examples
- 01
Customer request
Email + attachment
Products + quantities
Requested delivery
- 02
Matching
Customer + product
Unit + terms
Open questions
- 03
Order draft
Evidenced lines
Current version
Ready for approval
The business question
The order has arrived. Order entry has not.
An email supplies the requested date, an attachment lists the products, and a later reply changes the quantity. The sales team has to assemble the complete request and establish which version applies.
Customer product names, pack sizes and alternative delivery addresses turn apparently simple orders into clarification work. A plausible product match is not enough to create a binding order.
From input to output
How the workflow can work.
The pilot starts with read access to the mailbox and approved records. An ERP import or interface is connected only after checking its data model, permissions and draft capabilities. Existing business systems remain authoritative.
- Authorized order mailbox, attachments and message history
- Customer and product records with confirmed mappings and units
- Current commercial terms, delivery addresses and order approval rules
- 01
Assemble the request
Connect the message, attachment and identifiable amendments to one case. Keep the source passage attached to each quantity, product reference and delivery request.
- 02
Check each line
Match the customer, product reference and unit against approved records. Ambiguous matches and inconsistent instructions produce a specific clarification request.
- 03
Hand over the draft
Sales receives structured items, open questions and a change comparison. The agreed handoff follows approval; a stable case reference prevents duplicate entry.
The key review boundary
Price differences, unclear units and unconfirmed delivery dates are not silently accepted. Order creation and customer confirmation remain subject to the designated approval.
The resultAn order draft containing the matched customer, evidenced line items, requested delivery date and explicit exceptions. Staff review the case instead of reconstructing it from scattered messages.
One workflow. Three situations.
What changes when …?
Choose a situation and follow the handover. An illustration using fixed sample data, not live AI or a client project.
The example email requests 24 individual units using a confirmed customer product reference. The attachment and product record agree.
Match the customer, product and unit. Record the requested date as a preference, not a delivery promise.
A complete draft is ready for routine order approval.
Ready for reviewStart focused. Compare fairly.
What the pilot should be measured against.
- Time to a reviewable draft
- Include preparation and subsequent human review; compare straightforward orders and exceptions separately.
- Correct line items
- Check the product, quantity and unit against expert-reviewed reference cases, not just extracted text fields.
- Useful clarification
- Track necessary and unnecessary questions as well as conflicts missed during preparation.
A practical initial scope
One order mailbox, one product group and an accountable sales team. Start with historical orders, then operate alongside current entry without automatic customer confirmations.
- Anonymized routine orders and known exceptions
- Product mappings including pack sizes
- An approved expected order for each test case
Agree acceptance criteria first
Agree acceptable correction rates, review effort and stopping rules before the test. Quantity changes, duplicate requests and unreadable attachments must also reach the right handler with usable context.
Discuss a focused pilotThis use case will be selected in the enquiry form.Common questions
Does this replace our order management system?
No. It prepares work for the system you already use. A structured review package is often sufficient for a pilot; write access follows only with agreed controls.
Can scanned purchase orders be included?
Scans can be part of the agreed input scope. Unreadable quantities or product numbers remain marked as uncertain. Suitability is tested against your actual document types.
When would conventional automation be better?
A direct import may be sufficient for consistent, structured order formats. AI becomes useful when wording, attachments and follow-ups vary. Both approaches can coexist.