AI spans storefront and back office
Digital teams, marketers, merchants, service organizations, and engineers adopt different tools and providers.
Retail + ecommerce
AI is already inside the commerce stack. Can you connect it to the teams using it?
Proxon gives digital, merchandising, marketing, customer care, data, security, and finance teams one shared record of observed and registered AI systems, attributable adoption where identity is available, registered workflows, and available spend context.
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The control gap
AI adoption is moving faster than the commerce operating model.
AI enters retail and ecommerce through customer service, content, merchandising, software, analytics, and corporate tools. Each entry point creates another record to reconcile.
Ownership follows the customer journey
Product, brand, service, data, security, procurement, and finance each own different decisions around the same AI-assisted work.
Spend fragments across channels
Model APIs, SaaS features, licenses, and experiments create cost records that do not automatically carry team or workflow context.
The management layer
One operating view of observed and registered AI
Proxon brings the records behind observed AI adoption, registered systems and workflows, and captured or provider-reported spend into one product. Each function can use the view its responsibilities require.
AI tools, models, agents, and connected services
Bring observed activity and registered systems into a shared inventory, with providers and data sources recorded alongside them when that context exists.
Attributable usage across the organization
Where identity is available, compare observed adoption across people, teams, and departments without turning presence into a performance judgment.
AI spend with organizational context
Review captured and provider-reported spend by provider, model, department, team, and registered workflow when that context is available.
Registered use cases and outcome signals
Keep registered workflows and their curated or reported outcome signals alongside the systems and teams involved in the work.
Shared commerce model
A common AI record across customer and operating teams
Give digital, merchandising, service, data, security, and finance leaders the view their work requires while keeping the underlying evidence connected.
Digital product and ecommerce
Understand which observed and registered AI systems support digital experiences and product work.
Merchandising and marketing
Review attributable adoption and registered workflows across planning, content, research, and campaign teams.
Customer care and operations
Keep agent-assistance, knowledge, quality, and operating workflows in the same management record.
Data, security, finance, and procurement
Bring observed activity and available spend context into vendor, security, and budget reviews.
Where AI enters commerce
One commerce stack, many AI-assisted workflows
Proxon keeps observed systems, attributable adoption, registered workflows, and available spend context together across digital and corporate retail work.
Merchandising and content
Product copy, catalog support, research, and assortment preparation
Customer care
Agent assistance, knowledge retrieval, summarization, and quality support
Marketing operations
Campaign research, briefing, drafting, analysis, and reporting preparation
Demand and inventory planning
Forecast review, exception summaries, scenario preparation, and decision materials
Digital product and engineering
Code assistants, testing, incident analysis, and product research
Corporate operations
Finance, procurement, HR, legal, communications, and internal support
Proxon manages the layer around this work: observed and registered AI systems, attributable adoption where identity exists, registered workflows, and spend where context is available. Commerce owners remain responsible for pricing, inventory, merchandising, customer, and operating decisions.
The operating view
Answer the questions behind every commerce review
Connect observed adoption, available spend context, and registered workflow signals without treating activity as proof of sales, margin, service quality, or customer impact.
Adoption
See where AI is becoming part of commerce work
Where identity exists, compare observed adoption across people, teams, and departments so enablement can focus on uneven uptake.
Cost intelligence
Give digital leaders and finance shared spend context
Review captured and provider-reported spend by provider, model, team, and registered workflow when those dimensions are available.
Workflow outcomes
Review reported commerce signals beside registered work
Keep curated or reported outcome signals connected to adoption and cost while business owners retain responsibility for commerce decisions.
Bring the commerce stack into view
Bring one AI operating record to the next digital, service, or budget review.
See observed and registered systems, attributable adoption, registered workflows, and available spend context across retail and ecommerce teams.
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