Paz for ecommerce teams
AI ecommerce workflow automation
Paz is an AI ecommerce workflow automation platform for recurring checks and evidence-led investigations. Routines save what to check, the relevant scope, and when to run. Overview brings your personal briefing together with shared follow-ups and connected signals, while Inbox holds questions and decisions that need attention.
Ecommerce automation use cases
AI visibility & discovery
Use Visibility watch to investigate changes across tracked queries, engines, and competitors. Read the supporting citations and product facts before choosing a response.
Output: A finding with the observed change, relevant evidence, and a proposed next step.
Website traffic
Website traffic check uses connected Google Analytics data to investigate session changes and the acquisition channels and landing pages involved. Organic traffic investigation, in Organic search, examines connected Search Console evidence instead.
Output: Qualified traffic findings with reporting limits and a next check, not an inferred revenue loss.
Products & catalog
Catalog health & AI readiness follows catalog syncs to surface product-data issues and drafts for review. Merchant Center hygiene, in Google Merchant Center, investigates channel issues against catalog evidence.
Output: Product-data findings or reviewable improvements through the relevant supported workflow.
Evidence → context → permitted action
Example: a product description correction
An illustrative workflow, not a customer result or a live product screenshot.
A product-data check finds that a priority bottle has no capacity detail in its channel description. The catalog and product page agree on the missing fact. The investigation supports a factual correction; it does not authorize a Routine to publish it.
- Trigger
- A description-quality check for the priority collection. A custom Routine can investigate it on a schedule; catalog health checks follow catalog syncs.
- Affected product
- TRAIL-BOTTLE-750 · Trail bottle
- Inputs and context
- Catalog and product page: capacity 750 ml; material stainless steel. Business context: prioritize this collection and retain existing approved claims.
- Current description
- “Trail bottle for everyday adventures.”
- Proposed description
- “750 ml stainless steel trail bottle for everyday use.”
- Unknown
- Insulation duration is not supplied. No performance claim is added. A missing business fact can become a question; an answer is context, not independent source verification.
- Allowed change
- A Google Merchant Center description update for this product only, through the supported field mapping, draft approval, and destination workflow. Saving the Routine is not approval to publish.
- Output
- The finding identifies source facts, the current value, and a proposed correction. Supported draft review and publishing are separate steps. Conflicting facts keep the affected change out of scope.
Evaluate the result in stages: the proposed value is accurate; the approved update is submitted; the destination accepts it. Submission alone does not establish acceptance, issue clearance, or a sales effect.
Shared context helps Paz choose the next useful check
Product data describes what you sell. Business context adds current priorities, campaign information, positioning, and constraints. A focus collection can deserve attention before a phase-out range with the same gap. Context explains relevance, not the cause of a traffic change.
Shared follow-ups retain investigation progress, evidence, questions, and next checks. Paz uses relevant company context and can reuse an answer from a related investigation when its account, catalog, and reporting period fit. A question or decision can appear in Inbox; the investigation continues on the shared follow-up.
Your personal briefing in Overview summarizes changes on a daily or weekly schedule. Its read state and schedule are personal: marking a briefing read does not resolve shared follow-ups or change a Routine schedule.
How recurring workflows run: Monitor, Optimize, Act
01
Monitor
Use Routines for saved recurring checks. Their instructions and schedules define the check; connected signals have their own source coverage and refresh state.
02
Optimize
Investigate material changes with current business context. Supported findings can lead to shared follow-ups that retain evidence and next checks, while Inbox surfaces questions and decisions.
03
Act
Answer a relevant question, review a proposed improvement, or use a supported action. An investigation does not itself grant write access; product changes follow their required approval and destination workflow.
Measure finding quality, source freshness, and destination results separately from commercial outcomes. An AI-shopping omission or a decline in sessions is an investigation signal, not proof of lost sales. Track accepted corrections against a baseline without treating every completed update as a revenue gain.
Connected signals show both evidence and freshness
Overview brings together supported connected signals, including Google Merchant Center, Google Search Console, and Google Analytics. Check the reporting period, coverage, and freshness alongside the metric. When a refresh fails, Paz can retain the last successful results with their original timestamp and a refresh warning; old results are not a new clean check.
Choose a Routine for its sources and purpose. Competitors covers checks such as Competitor movement and Competitor news; Advertising and Operations include checks tied to existing ad and distribution workflows. A library category is not permission to run every possible action in that area.
A custom Routine can file findings, propose queries to track, and remember context, but its agent cannot change products on its own. Product improvements use the separate supported draft-review and publishing workflows. Required approvals, destination permissions, and field mappings still apply.
Where Paz fits in your commerce stack
A good fit
Brands and retailers with recurring visibility checks, traffic investigations, catalog-quality questions, or product-data improvements that depend on business priorities and source evidence.
Alongside your systems of record
Use your existing commerce systems for checkout, payments, orders, and fulfillment. Paz focuses these workflows on ecommerce intelligence and supported product improvements, not every operation in the business.
See your ecommerce workflows in context
Explore a Paz Routine, the evidence behind a finding, and how a shared follow-up develops. See where questions, draft review, and supported actions fit without treating them as the same step.
Frequently asked questions
What is AI ecommerce workflow automation?
AI ecommerce workflow automation uses AI to interpret commerce data and business context within repeatable checks, investigations, and permitted actions. Paz Routines save recurring checks; Overview combines personal briefings, shared follow-ups, and connected signals. Product updates use supported approval and publishing workflows.
How do Routines, Overview, and Inbox differ?
Routines define saved recurring checks. Overview shows your personal briefing alongside shared follow-ups and connected signals. Inbox holds questions and decisions needing attention. Reading a briefing is not the same as resolving a shared investigation or approving an update.
Which ecommerce Routine is a useful starting point?
Choose a check with usable evidence and a clear output. Visibility watch uses tracked AI-query evidence; Organic traffic investigation needs Search Console; Website traffic check needs Google Analytics. Match the scope and schedule to the question and respect the reporting limits.
Can a Routine publish product changes on its own?
A custom Routine can investigate, file findings, propose tracking queries, and remember context; its agent cannot change products on its own. Product changes use supported workflows with the required approvals and destination permissions. A useful investigation does not need an external update.