Azoma

Category: AI Search/Research

Optimising for shopping agents, not just chatbots.

Azoma works on what it calls agentic commerce optimisation, and positions itself deliberately against the general GEO tools. The difference is what it tracks. Alongside ChatGPT, Gemini, Claude and Meta AI it covers retailer shopping agents, Amazon Rufus, Walmart Sparky and Alexa for Shopping, which is where a consumer brand actually loses or wins a sale. Metrics are framed around revenue, conversion and SKU-level discoverability rather than share of voice. It builds digital twin prompt sets from marketplace data, product questions and keyword research to model how shoppers really ask, and covers the emerging agentic commerce, agentic merchant and universal commerce protocols as well as technical GEO and ChatGPT Apps. London-based with around forty staff, it raised an eleven million dollar Series A in October 2026 and lists L'Oreal, Unilever, Mars, Beiersdorf and Reckitt as customers.

Our verdict

The sharpest positioning in this category, because while everyone else measures whether ChatGPT mentions you, Azoma measures whether Amazon Rufus recommends your product, and for a consumer brand that is the question that moves revenue. Working at SKU level rather than brand level is the right granularity for anyone with a catalogue. The credibility is also real in a way that most tools in this part of the directory are not: a Series A this month, around forty staff, a London base and five named global FMCG customers rather than an anonymous logo wall. That it is British is worth noting for our readers. The limitation is simply fit. It is enterprise and ecommerce only, entirely sales-gated with no pricing, trial or self-serve route, and useless to anyone who does not sell physical products through large retailers. Also worth knowing their pricing and contact pages currently return errors, so go through the demo form.

Best for: Consumer brands selling through Amazon, Walmart and other large retailers who need to be found by shopping agents rather than only by chatbots.

Pros

  • Tracks retailer shopping agents like Amazon Rufus and Walmart Sparky, not just chatbots
  • Works at SKU level, the right granularity for a real catalogue
  • Metrics framed around revenue and conversion rather than share of voice
  • Named global FMCG customers including L'Oreal, Unilever and Mars
  • London-based with a fresh Series A and around forty staff
  • Covers the emerging agentic commerce protocols properly

Cons

  • Enterprise and ecommerce only, no use to a services or B2B brand
  • Entirely sales-gated with no pricing, trial or self-serve route
  • Their pricing and contact pages currently return errors, so use the demo form
  • No MCP server
  • Narrow by design, it will not replace a general AI visibility tool

Pricing: Pricing on application, demo only (Enterprise)

Rating: 4/5