signalCommerce : CatalogOps

Decision assurance for your catalog

Test whether AI shopping agents answer your buyers’ questions correctly from your catalog — compatibility, required parts, bundles, availability — before a catalog change ships.

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Release gate

illustrative

example catalog · frozen suite · after a feed change

Verdict

Ineligible · 1 wrong “yes” on a critical case

Graded cases

✓
Does this base fit my kettle?expects recommend
✕
Will the kettle work on a 120 V supply?expects refuse

Agent said yes: the voltage limit was dropped from the feed

✓
Do I need anything else to install it?expects required part
✓
Is it in stock for delivery to my market?expects refuse
Routed to: feed teamdeterministic grading
The problem

A catalog change can turn a right answer into a wrong “yes”

AI shopping agents answer “will this fit?”, “what else do I need?” and “can I buy it here?” from your product data and documentation. When a feed, mapping or normalization change drops a qualifier, the agent answers confidently — and wrongly.

✕No validation error
✕No failed build

Just a buyer who was told yes when the answer was no.

How it works

From your catalog’s own evidence to a pass or fail release gate — with a person approving what counts as truth.

Step 1

Snapshot the catalog

Import a catalog export into an immutable snapshot. Every later step reads that snapshot, never a moving target.

Step 2

Mine decision contracts

Candidate rules are mined from your own evidence — product data and manuals, each citing its source. A person approves every one.

Step 3

Freeze scenario suites

Approved rules become frozen suites of buyer questions with the decision each must get: recommend, ask or refuse.

Step 4

Answer like an agent

A reference agent (an OpenAI model) answers the suite against your catalog, or captured responses from real agent surfaces are replayed.

Step 5

Grade deterministically

Answers are graded by rule code, never by a model, so the same inputs give the same verdict before and after a change.

Step 6

Localize, route and gate

Each failure is traced to the stage that caused it and routed to its owner. A release gate decides whether the change may ship.

The questions buyers actually ask

CatalogOps checks the decisions an AI agent makes on your buyer’s behalf, not just whether a field is filled in.

Compatibility and conditions

Does it fit, and under which conditions — model, revision, voltage, firmware.

Required parts and quantities

What else the buyer must buy, and how many, so a kit is never sold incomplete.

Bundles

What is included, what is sold separately, and which alternatives are interchangeable.

Offer applicability

Availability, market, locale and price for the offer the buyer can actually purchase.

Multi-turn dialogues

Answers that must stay right as the conversation adds details turn by turn.

Ask or refuse

When evidence is missing or contradictory, the right answer is a question or a refusal — never a guess.

Built to be trusted

Assurance only counts if the verdict itself can be relied on.

People approve truth

Nothing a model infers becomes catalog truth on its own. Mined rules are candidates until a person approves them.

A wrong “yes” is the worst failure

Telling a buyer something is compatible when it is not is graded as the highest-severity failure.

Deterministic and reproducible

Rule-based grading over frozen suites means before and after runs compare like for like.

Provenance on every answer

Every graded answer records which evidence, model and configuration produced it.

Untrusted text stays data

Manuals and captured responses are treated as untrusted input, fenced away from instructions.

Gates your pipeline can read

A CI pipeline reads a release gate’s verdict with a read-only, revocable token and stops a change that is not certified.

Where it fits

  • Runs as the CatalogOps module of the signalCommerce platform
  • Works from your catalog exports and documents — it does not edit your catalog
  • Failures routed to the owning team, with an incident package for each failed run
  • Pairs with Argus (storefront readiness) and Aegis (endpoint simulation)

Know your catalog gives the right answer.

See CatalogOps run a frozen suite against a catalog and gate a change — request a demo and we will walk you through it.

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