Solutions · AI Commerce
Shoppers increasingly ask AI assistants and AI-enabled search what to buy, and retailers such as Amazon and Walmart apply their own algorithms to decide what appears. Artiminds helps brands assess how their products show up in these environments, benchmark against competitors, and improve the product information, content, and data foundations that influence representation. We focus on what brands can control: accuracy, completeness, structure, and consistency of product information across every channel. We do not promise placement or guaranteed recommendations on any third-party platform.
What we do
Be understood by the algorithms that now shape shopping.
We help CPG and consumer brands understand and improve how their products are discovered, represented, and recommended across AI-powered shopping experiences.
AI Discoverability Assessment
Structured testing of how products and brands are surfaced and described across AI assistants, AI search, and retailer algorithms for the questions shoppers actually ask.
Competitive Visibility Benchmarking
Side-by-side analysis of how your portfolio is represented relative to category competitors, by product, attribute, and shopper intent.
Product Information Accuracy & Structure
Auditing and improving the product data, attributes, and structured content that AI systems and retailer algorithms interpret, including consistency across PIM, retailer, and brand sources.
Digital Shelf Analytics
Monitoring search rank, content quality, availability, ratings, and pricing signals across major retailers, connected to sales and share data.
Commerce Data & Measurement
Data pipelines and dashboards that unify digital shelf, syndicated, and e-commerce performance so brand and category teams can act on one picture.
ChatGPT
AI assistant
Gemini
AI assistant
Amazon
Marketplace
Walmart
Retail platform
AI Search
Answer engines
Digital Retail
Retailer sites & apps
Social Commerce
Shoppable feeds
AI visibility dashboard
Lumen Botanical · Sparkling Water
AI Product Discoverability
0%
How often the product appears for relevant category prompts
Recommendation Frequency
0%
Share of relevant recommendations that include the brand
Competitive Visibility
0%
Visibility relative to the top competing brands
Product Information Accuracy
0%
Correctness of attributes, claims, and availability
Digital Shelf Performance
0%
Search rank, content quality, and ratings signals
Growth Opportunities
0
Prioritized improvement areas identified
Simulated metrics for a fictional brand. Artiminds measures and improves visibility signals; it does not guarantee recommendations or placement on any third-party platform.
Outcomes
What changes for the business
- A clear, evidence-based picture of how AI systems currently describe and surface your products
- Prioritized improvements to product information and content that address real gaps
- Benchmarks that track representation over time and against competitors
- Brand, e-commerce, and data teams aligned on a single commerce measurement framework
- Product data foundations that serve both traditional digital shelf and AI-driven discovery
How we engage
- 01
Baseline
We define the shopper questions, categories, and platforms that matter, then capture how your products and competitors are represented today.
- 02
Diagnose
We trace visibility gaps back to root causes in product data, content, availability, and retailer execution.
- 03
Improve
We work with brand, e-commerce, and data teams to fix product information, enrich content, and strengthen the data foundations behind it.
- 04
Measure
Ongoing monitoring tracks representation, discoverability, and digital shelf performance so improvements are sustained.
Technologies
Platforms we work with
Named for identification only. Recommendations are driven by architecture fit, not alliances.
- Python
- Databricks
- Snowflake
- Power BI
- Salsify
- Akeneo
- Amazon Marketing Cloud
- Google BigQuery
- Azure OpenAI Service
FAQ
Common questions
Can you guarantee that AI assistants will recommend our products?+
No, and no credible firm can. AI assistants, AI search, and retailer algorithms are controlled by third parties and change frequently. What we can do is make sure the information those systems rely on is accurate, complete, well structured, and consistent, and measure how representation changes over time. Brands that invest in those fundamentals put themselves in the best position.
How is this different from traditional digital shelf analytics?+
Digital shelf analytics focuses on retailer search rank, content scores, and availability. AI commerce intelligence extends that view to how conversational and generative systems describe, compare, and suggest products. The two are closely related, because the same product information often feeds both, which is why we address them together.
What do we need to get started?+
A defined category scope, access to your product information sources, and a point of contact across brand, e-commerce, and data. We handle the testing framework, benchmarking, and analysis, and we come back with a prioritized set of actions rather than a generic report.
Consultation
Start with a conversation. We will listen, challenge assumptions, and recommend the fastest path to measurable progress.