Surface the conflict
Keep the incoming request and approved reference record visible together.
AgentKaki Order
Reduce rekeying before an order reaches your ERP.
AgentKaki Order is a research-stage workflow for turning incoming customer orders into structured, reviewed work. AI helps interpret messages and documents; approved customer, product and business rules guide validation. Missing or conflicting information is surfaced for review, and the first pilot can stop at a validated draft before any change to an operational system.
THE OPERATING PRESSURE
Where work slows down
Use these patterns to identify what is happening in your own operation.
- Repeated entry of customer and product details
- SKU, quantity and unit-of-measure checks across different formats
- Missing information that holds up order preparation
- Corrections and approval hand-offs before an order can proceed
HOW IT WORKS
A clear path through the work
- 01
Capture
Receive an order from one approved channel.
- 02
Structure
Prepare a draft with the relevant customer, product and quantity information.
- 03
Validate
Check the draft against approved records and business rules.
- 04
Prepare the next step
Route exceptions for review and prepare accepted work within the agreed scope.
THE GZP OPERATING DISCIPLINE
How GZP worksAI interprets. Rules decide. Humans retain authority.
Messy work. Controlled execution. Measurable outcomes.
CONTROLLED EXECUTION PATHILLUSTRATIVE MODEL
- 01Capture
- 02Interpret
- 03Validate
- 04Authorise
- 05Complete
ORDERS AND EXCEPTIONS
The order pauses. The exception gets an owner.
AgentKaki Order may encounter conflicting pack sizes, missing product mappings or approval-dependent prices. Exception Operations is designed to organise the evidence, identify the decision owner and keep the authorised resolution linked to the order.
Structure the decision
Identify what is uncertain, who can decide and what remains blocked.
Review what can be reused
Repeated approved resolutions may become operational guidance after owner review. A past decision does not automatically change a rule or authorise the next order.
Proposed relationship between research-stage workflows; reusable guidance and system execution require implementation and validation.
SUITABLE STARTING POINT
One workflow. A boundary you can test.
Start with one recurring order format or channel, an accountable order team and approved reference data. Distributors, wholesalers, manufacturers and B2B suppliers may have suitable workflows.
WHAT TO MEASURE
Make the improvement observable
Agree a representative baseline and acceptance criteria before a pilot. Compare the same scope and include the effort needed to review exceptions.
- Handling time per order
- Manual entries and touches
- Correction and rework rate
- Time to reviewed draft
- Exception review workload
- Cost per accepted outcome
COMMON QUESTIONS
What to know before a pilot
What is AI order processing?
AI order processing interprets incoming order information and turns it into structured data. A governed workflow checks that data against approved rules and routes exceptions for review before material system actions.
Does AgentKaki Order automatically create ERP orders?
Not by default. A research-stage pilot can stop at a validated, human-approved draft. Any system action depends on the agreed integration, permissions and pilot evidence.
Can it work with emailed purchase orders or WhatsApp messages?
Potentially. The first assessment checks actual formats, channel access, data quality, validation rules and exception patterns.
What happens when order information is unclear?
The intended workflow surfaces the missing or conflicting information for review rather than treating a guess as an accepted order.
Research-stage workflow. This page describes a workflow pattern being evaluated by GZP. Actual scope, integrations, controls and measurable outcomes depend on the customer workflow and pilot evidence.
START WITH THE WORK
Discuss your order workflowBring one recurring order workflow.
Prepare a short summary of input, validation, exceptions, current system and a measurable baseline.