Azoma Review 2026

Key takeaways

• Azoma is built for large consumer brands that sell through retailers and marketplaces. Its customer logos include Mars, Zappos, Lipton, Canadian Tire and WPP.
• It combines tracking with action: share-of-voice dashboards, citation gap analysis, a technical audit and content generation for LLM consumption.
• Pricing is sales-gated. The enterprise page shows only "Get a Demo" and "Contact", so you cannot compare costs before talking to sales.
• It is a direct competitor to Promptwatch and lacks several things Promptwatch has: IP-verified AI crawler logs, visitor analytics for AI traffic attribution, Reddit, YouTube and LinkedIn citation tracking, prompt volumes and difficulty scores, query fan-outs, and a self-serve plan.
• Independent reviews are thin. Most public claims come from Azoma itself, its press releases, or competitors.

Azoma (formerly Ecomtent) is a platform that calls its category "Agentic Commerce Optimization". The idea is simple. More shoppers now ask ChatGPT, Gemini, Perplexity, Amazon Rufus, Alexa for Shopping or Walmart Sparky what to buy, and brands need their product data and content to show up in those answers. Azoma measures where a brand appears, finds the gaps, and generates product content designed for AI systems to read and cite.

The company is led by CEO and co-founder Max Sinclair. It announced an $11.2M Series A on 22 October 2026, led by Puma Growth Partners, with dunnhumby, Twin Path, MaRS IAF, Rank Ventures and Ignite Ventures taking part, plus angels from Google DeepMind and Legora. That follows a $4M seed in December 2025. The company says the money will go toward expanding to AI personal assistants, building optimization and attribution tools using dunnhumby data, and hiring AI talent.

The audience is clear: enterprise consumer packaged goods and retail brands with big catalogues of household staples, where thousands of SKUs need consistent, machine-readable product content. If you run a ten-product Shopify store, this is not aimed at you.

Key features

Visibility dashboard. Azoma's dashboard is organized into four modules: Visibility, Citations, Competition and Query Log. You get share-of-voice tracking, competitor tracking, and citation, mention and topic gap analysis. The shopping-assistant coverage is the interesting part. Azoma's own materials list ChatGPT, Perplexity, Gemini and Rufus, and its press release adds retailer assistants such as Alexa for Shopping and Walmart Sparky. A competitor page claims Azoma covers fewer engines, but that looks outdated against Azoma's own site.

Real-time alerts. Alerts fire when visibility drops. For a brand with thousands of products across several retailers, a silent drop in recommendations is exactly the sort of thing you want flagged early rather than found in a quarterly report.

Content generation. This is the part that separates Azoma from pure trackers. An end-to-end GEO workflow generates and optimizes product content for LLM consumption. The workflow fills product-data gaps and produces AI-optimized copy at scale. That is useful when the real problem is thin or inconsistent retailer listings rather than a lack of blog posts.

Technical audit. A site-integrated audit looks for structured-data and rendering issues that stop AI crawlers from reading your pages. This is a standard but necessary check. Azoma's version is tied to a site integration, though I could not find a full integrations list, so how deep it goes is unclear.

Digital twin simulation. According to competitor and Azoma-adjacent descriptions, the platform can simulate how AI systems might respond before content goes live. I could not verify the mechanics from independent sources, so treat it as a claim to test in a demo.

Original research. Azoma analyzed 38 million citations and published findings. Editorial content makes up 51.9% of Gemini's citations but only 7.6% on AI Overviews. Affiliate content makes up 39.6% of Alexa for Shopping's citations and under 2% on every other engine measured. Those engine-by-engine differences are the kind of thing that justifies per-engine strategies. A whitepaper, "Agentic Commerce 5Cs", is also linked from the site.

Planned attribution. The dunnhumby data partnership is aimed at building optimization and attribution tools. It is a roadmap item, not a shipped feature, so do not buy on it.

Who is it for

The sweet spot is a global CPG or retail brand with a dedicated ecommerce, digital shelf or content team, a large catalogue, and a budget for a custom enterprise contract. Think a food or beauty group trying to hold its place when a shopper asks an AI assistant for the best option in a category. Agencies serving that kind of client, which explains WPP appearing among the logos, are a natural fit too.

It is a weaker fit for small DTC brands, solo founders, and in-house SEO teams who want to sign up with a credit card and start tracking prompts today. There is no public price, no visible free tier, and a demo-led process. Teams that care mostly about broad AI search monitoring across social sources, crawler behavior and traffic attribution will also find the scope narrow. Azoma concentrates on product visibility and content, not a full storefront or marketplace management suite.

If your goal is general AI search visibility rather than shopping agents specifically, look at the GEO software directory at bestgeosoftware.com for broader options.

Integrations and ecosystem

The public information here is limited. The enterprise page mentions a site integration for the technical audit, and the press release points to a planned dunnhumby data integration for attribution. I did not find a full integrations catalog, a public API, a documented MCP server, or a browser extension.

That does not mean none exist. Enterprise platforms often share integration details only during onboarding. But it does mean a buyer should ask directly about CMS and PIM connections, export formats, and API access before signing. A login portal exists at app.azoma.ai, and the press contact is [email protected].

Pricing and value

Azoma does not publish pricing. The enterprise page offers only "Get a Demo" and "Contact" buttons. A competitor page claims deployments are priced by SKUs, AI platforms and markets, with 60 to 90 day pilots and no free tier. I could not verify those details on Azoma's own page, so consider them unconfirmed.

For context, self-serve GEO platforms sit well below enterprise contracts. Promptwatch has a free Explore plan, then Essential at $95/mo, Professional at $245/mo and Business at $579/mo, with agency plans from $199/mo. GEOly, the site that prompted this page, starts at $49/mo. Azoma is almost certainly a larger commitment, which is reasonable if you need bulk product content work across thousands of SKUs and retailer assistants, and hard to judge if you cannot see a number.

Value depends on scale. For a brand with a huge catalogue, automated product copy and gap-filling could save a lot of manual work. For a smaller team, paying enterprise rates for tracking you can get elsewhere is a poor deal.

Strengths and limitations

Strengths

• Clear focus on agentic commerce and retailer assistants, including Alexa for Shopping and Walmart Sparky.
• Action, not just monitoring: content generation and a technical audit sit next to the dashboards.
• Credible enterprise customer logos and fresh funding of $11.2M.
• Original research on 38 million citations that shows how citation sources differ by engine.

Limitations

• No public pricing and a demo-only sales process.
• Thin independent review coverage. I found no third-party reviews, and the case-study numbers on the home page (+17.7%, +68%) come with limited context.
• A young brand after the Ecomtent rebrand, so its enterprise track record is still being built.
• Narrow scope compared with a full AI visibility stack.

What it lacks compared with Promptwatch

Promptwatch is the stronger option if you want optimization across the whole AI search funnel, not only shopping content. Based on what Azoma publishes, it lacks or does not clearly show:

• IP-verified AI crawler logs with crawl-to-citation tracking
• Visitor analytics that attribute real traffic and conversions from AI platforms
• Google Search Console and Bing Webmaster data next to AI data (360 Insights)
• Reddit, YouTube and LinkedIn citation tracking
• Prompt volumes, difficulty scores and query fan-outs
• Page-level tracking and offsite mentions
• An Action Board, Agent Chat and a Content Agent that publishes straight to WordPress, Webflow or Framer
• Local GEO targeting, coding agent visibility (Codex, Claude Code, OpenCode) and a hosted MCP server
• A free plan and public pricing

Promptwatch does cover shopping too, through AI Shopping in ChatGPT and Amazon Alexa and its Ads Radar, though Azoma's retailer-specific depth, such as Rufus and Walmart Sparky, is a genuine point in its favor.

Bottom line

Azoma suits large consumer brands that need to fix product content for AI shopping agents and are comfortable with a custom enterprise engagement. The funding, the customer logos and the engine-level research are real signals. The missing pricing and independent proof mean you should push hard in the demo and ask for references.

If you want one platform that tracks, explains and fixes visibility across every major AI engine, with crawler logs and traffic attribution and a price you can see, Promptwatch is the better pick for most teams. Best use case in one sentence: enterprise CPG brands optimizing thousands of product listings for AI shopping assistants.