Resources

Checklists and templates for governed commerce

Practical tooling for commerce teams and agencies — the same material we use in demos and pilots. Request any asset as an editable document, or work through it right here on the page.

The five assets

Commerce AI Safety Checklist

Twenty controls to review before letting AI touch your commerce operations. Use it to pressure-test any vendor — including us — on approvals, audit trails, data handling and rollback.

Request this asset

Catalogue Chaos Audit Template

Score your catalogue health across channels, fitment data, media and SEO fields. Work through it channel by channel to find where drift, duplicates and gaps are actually costing you sales.

Request this asset

App-Stack Consolidation Worksheet

Inventory your commerce apps, their cost, overlap and consolidation candidates. It gives you the numbers to decide what to keep, what to cut and what one platform should replace.

Request this asset

AI Search Readiness Checklist

Is your product data ready for semantic search and RAG merchandising? This checklist walks the data fields, structure and governance that AI-driven discovery depends on.

Request this asset

Agency Margin Calculator

Model build, retainer and referral margin on governed automation services. Compare the per-client economics of implementing, operating and referring commerce automation.

Request this asset

What is inside each asset

Commerce AI Safety Checklist

Approval gates: list every action class the AI may take and mark which require human sign-off before execution — price changes, listing publishes, refunds, customer emails.
Audit trail: confirm every AI action is logged with actor, timestamp, input evidence and outcome, and that the logs are queryable without engineering help.
Evidence grounding: require answers and recommendations to cite live catalogue or order data, and test how the system behaves when the evidence is missing.
Blast radius: check what one misconfigured workflow can touch — one channel, one tenant or the whole account — and whether a dry-run mode exists.
Rollback: verify that any automated change (price, content, stock) can be reversed in bulk with a known procedure, not per-item manual fixes.
Data boundaries: confirm tenant isolation, where data is processed and stored, and whether any of your data is used for model training.
Cost guardrails: set spend caps and alerts on AI usage so a traffic surge cannot produce an unbounded bill.
Human override: confirm operators can pause, edit or halt any agent mid-workflow, and that the emergency stop is itself logged.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

Catalogue Chaos Audit Template

Channel coverage: for each SKU, record which channels list it and where it is missing, suppressed or duplicated.
Title quality: sample listings per category and score title consistency, length and whether key attributes such as brand, size or fitment appear in the title.
Media completeness: measure the share of SKUs carrying the required image count, zoom imagery and video where the category demands it.
Fitment and compatibility data: for fitment-dependent categories, check whether vehicle, model or compatibility attributes are present and machine-readable.
Attribute fill rate: calculate the fill percentage for the attributes your search and filters depend on, category by category.
SEO fields: audit meta titles, meta descriptions and canonical URLs for duplication, truncation and missing values.
Price and stock drift: compare price and availability per channel against the source of truth and record the variance.
Scoring: apply a 0–5 health score per section so you can re-run the audit later and measure improvement over time.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

App-Stack Consolidation Worksheet

Full inventory: list every subscription touching commerce operations — search, recommendations, reviews, sync, QA, support — with owner, monthly cost and contract end date.
Overlap map: mark which tools hold a copy of your product data and which jobs two or more tools are doing in parallel.
True cost line: add per-app licence cost, usage overages and the internal hours spent reconciling data between tools.
Consolidation candidates: flag tools whose job a commerce operating system covers — catalogue sync, search, recommendations, content QA, support drafting.
Switching cost: for each candidate, note data export options, integration dependencies and migration risk before you cut anything.
Savings projection: total the removable licences and reclaimed hours, then sanity-check the number against a realistic rollout timeline.
Decision log: record keep, cut or replace for each tool with the reasoning, so the decision survives staff turnover.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

AI Search Readiness Checklist

Attribute completeness: audit fill rates for the attributes shoppers filter by and ask about — semantic search is only as good as your data.
Description quality: check whether descriptions answer real shopper questions (fit, compatibility, materials, care) in plain language, not marketing copy alone.
Synonym and terminology coverage: capture how customers actually describe your products versus your internal taxonomy, including abbreviations and regional terms.
Fitment and compatibility structure: confirm compatibility knowledge is structured and machine-readable rather than buried in prose.
Media and alt text: verify images and alt text exist for every SKU, because retrieval-augmented answers reference product evidence.
Index freshness: define how often catalogue data flows to the search index and how quickly price and stock changes appear in results.
Failure behaviour: test what search returns for unanswerable queries — a graceful ‘we don’t stock that’ beats an invented answer.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

Agency Margin Calculator

Build revenue: model implementation fees per client — discovery, catalogue onboarding, workflow configuration and governance setup.
Recurring retainer: estimate the monthly operating retainer for running and tuning automation on a client’s behalf.
Referral margin: apply referral revenue percentages (BlueGreen’s agency program pays 10–20% on referred subscriptions) to your modelled client plans.
Client count scenarios: model margin at 5, 10 and 25 client stores to see where the curve bends.
Cost to serve: subtract your delivery hours, licence passthrough and support load for each client.
Churn sensitivity: test how the margin holds if a share of clients pause or leave each year.
Concentration risk: check how dependent total margin is on your largest client before you commit to headcount.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

Explore related

ROI Calculator

Estimate annual savings from consolidation and automation

Case Studies

How BlueGreen deployments work in practice

Glossary

Twenty-five plain-English definitions for commerce AI terms

Want a hand working through any of these?

Book a 25-minute demo and we will walk the checklist against your real catalogue, channels and app stack — no obligation.

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The five assets

Commerce AI Safety Checklist

Twenty controls to review before letting AI touch your commerce operations. Use it to pressure-test any vendor — including us — on approvals, audit trails, data handling and rollback.

Request this asset

Catalogue Chaos Audit Template

Score your catalogue health across channels, fitment data, media and SEO fields. Work through it channel by channel to find where drift, duplicates and gaps are actually costing you sales.

Request this asset

App-Stack Consolidation Worksheet

Inventory your commerce apps, their cost, overlap and consolidation candidates. It gives you the numbers to decide what to keep, what to cut and what one platform should replace.

Request this asset

AI Search Readiness Checklist

Is your product data ready for semantic search and RAG merchandising? This checklist walks the data fields, structure and governance that AI-driven discovery depends on.

Request this asset

Agency Margin Calculator

Model build, retainer and referral margin on governed automation services. Compare the per-client economics of implementing, operating and referring commerce automation.

Request this asset

What is inside each asset

Commerce AI Safety Checklist

Approval gates: list every action class the AI may take and mark which require human sign-off before execution — price changes, listing publishes, refunds, customer emails.
Audit trail: confirm every AI action is logged with actor, timestamp, input evidence and outcome, and that the logs are queryable without engineering help.
Evidence grounding: require answers and recommendations to cite live catalogue or order data, and test how the system behaves when the evidence is missing.
Blast radius: check what one misconfigured workflow can touch — one channel, one tenant or the whole account — and whether a dry-run mode exists.
Rollback: verify that any automated change (price, content, stock) can be reversed in bulk with a known procedure, not per-item manual fixes.
Data boundaries: confirm tenant isolation, where data is processed and stored, and whether any of your data is used for model training.
Cost guardrails: set spend caps and alerts on AI usage so a traffic surge cannot produce an unbounded bill.
Human override: confirm operators can pause, edit or halt any agent mid-workflow, and that the emergency stop is itself logged.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

Catalogue Chaos Audit Template

Channel coverage: for each SKU, record which channels list it and where it is missing, suppressed or duplicated.
Title quality: sample listings per category and score title consistency, length and whether key attributes such as brand, size or fitment appear in the title.
Media completeness: measure the share of SKUs carrying the required image count, zoom imagery and video where the category demands it.
Fitment and compatibility data: for fitment-dependent categories, check whether vehicle, model or compatibility attributes are present and machine-readable.
Attribute fill rate: calculate the fill percentage for the attributes your search and filters depend on, category by category.
SEO fields: audit meta titles, meta descriptions and canonical URLs for duplication, truncation and missing values.
Price and stock drift: compare price and availability per channel against the source of truth and record the variance.
Scoring: apply a 0–5 health score per section so you can re-run the audit later and measure improvement over time.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

App-Stack Consolidation Worksheet

Full inventory: list every subscription touching commerce operations — search, recommendations, reviews, sync, QA, support — with owner, monthly cost and contract end date.
Overlap map: mark which tools hold a copy of your product data and which jobs two or more tools are doing in parallel.
True cost line: add per-app licence cost, usage overages and the internal hours spent reconciling data between tools.
Consolidation candidates: flag tools whose job a commerce operating system covers — catalogue sync, search, recommendations, content QA, support drafting.
Switching cost: for each candidate, note data export options, integration dependencies and migration risk before you cut anything.
Savings projection: total the removable licences and reclaimed hours, then sanity-check the number against a realistic rollout timeline.
Decision log: record keep, cut or replace for each tool with the reasoning, so the decision survives staff turnover.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

AI Search Readiness Checklist

Attribute completeness: audit fill rates for the attributes shoppers filter by and ask about — semantic search is only as good as your data.
Description quality: check whether descriptions answer real shopper questions (fit, compatibility, materials, care) in plain language, not marketing copy alone.
Synonym and terminology coverage: capture how customers actually describe your products versus your internal taxonomy, including abbreviations and regional terms.
Fitment and compatibility structure: confirm compatibility knowledge is structured and machine-readable rather than buried in prose.
Media and alt text: verify images and alt text exist for every SKU, because retrieval-augmented answers reference product evidence.
Index freshness: define how often catalogue data flows to the search index and how quickly price and stock changes appear in results.
Failure behaviour: test what search returns for unanswerable queries — a graceful ‘we don’t stock that’ beats an invented answer.

Sent as an editable document when you request it — no spam, handled under our privacy policy.

Agency Margin Calculator

Build revenue: model implementation fees per client — discovery, catalogue onboarding, workflow configuration and governance setup.
Recurring retainer: estimate the monthly operating retainer for running and tuning automation on a client’s behalf.
Referral margin: apply referral revenue percentages (BlueGreen’s agency program pays 10–20% on referred subscriptions) to your modelled client plans.
Client count scenarios: model margin at 5, 10 and 25 client stores to see where the curve bends.
Cost to serve: subtract your delivery hours, licence passthrough and support load for each client.
Churn sensitivity: test how the margin holds if a share of clients pause or leave each year.
Concentration risk: check how dependent total margin is on your largest client before you commit to headcount.

Sent as an editable document when you request it — no spam, handled under our privacy policy.