An AI workflow can help a distributor turn an incoming request into a structured draft. The useful starting point is an intake process that preserves evidence and sends uncertain cases to a reviewer.
Disclosure: Prepared by Verix Technology using AI tooling. The examples below are synthetic design examples, not customer results or a claim that Verix has deployed this exact distributor workflow.
Use one approved catalog and one output: a draft request record. Keep prices, substitutions, delivery promises, external messages and ERP writes outside the initial automation. “Please quote two P-100 units for next week” describes a request; it does not establish stock, compatibility or a delivery commitment.
Store the original item wording alongside any proposed SKU and quantity. An exact catalog identifier can be checked deterministically. A model-proposed match should have evidence from an approved source. A familiar-looking name is not proof that two parts are interchangeable.
A workflow can extract the quantity correctly and still fail by letting an ambiguous request bypass review. Define expected fields and the expected route for every test example before comparing models.
This small evaluator uses Python's standard library. It makes no network or AI calls:
def evaluate(cases):
if not cases:
return {"cases": 0, "field_exact_match_rate": None,
"route_accuracy": None, "unsafe_auto_routes": 0}
field_ok = route_ok = unsafe = 0
for case in cases:
expected, predicted = case["expected"], case["predicted"]
field_ok += all(predicted.get(k) == expected.get(k)
for k in ("sku", "quantity"))
route_ok += predicted.get("route") == expected.get("route")
unsafe += (expected.get("route") == "review"
and predicted.get("route") != "review")
n = len(cases)
return {"cases": n, "field_exact_match_rate": field_ok / n,
"route_accuracy": route_ok / n,
"unsafe_auto_routes": unsafe}
For example:
cases = [{
"expected": {"sku": None, "quantity": 3, "route": "review"},
"predicted": {"sku": "P-100", "quantity": 3, "route": "draft"}
}]
print(evaluate(cases))
The invented request could be “Need three mystery pumps.” Guessing a SKU fails field matching; routing it to draft also fails the review boundary. This example is deliberately wrong. Its score is not a measurement of any model.
The checker assumes valid case objects and the routes draft and review. Add schema validation and reject unknown routes before using it in a real pipeline. It checks only SKU, quantity and routing; it does not validate price, units, authorization, attachments or delivery dates. A perfect score on a tiny synthetic set would not establish production accuracy.
Include unknown parts, conflicting quantities, obsolete identifiers, unreadable attachments and requests containing instructions to skip approval. Treat request text as untrusted input, not authority to change the workflow. Keep a separate evaluation set that has not been used to tune the system.
Use data the business has permission to process. Do not upload customer emails or catalog documents to a public notebook or demo. Synthetic examples are useful for explaining logic; representative, lawfully usable examples are needed to evaluate business performance.
Start with read-only catalog permissions and draft-only outputs. Agree retention, access and deletion responsibilities. Avoid putting complete emails in logs. Add a review queue for provider timeouts, missing credentials and invalid output, then test rollback and handover.
Ask an implementation partner to show the input, expected output, failure route and acceptance criteria for one workflow. Compare a custom build with an existing product or ordinary rules-based automation. The simplest workable option may use little generative AI.
Verix Technology helps scope AI workflows, integrations and evaluation. Company information: verixtechnology.com. No production result, accuracy or saving is promised by this tutorial.
The Python example is available under the MIT license, copyright 2026 Verix Technology.