Azoma vs AthenaHQ 2026
AthenaHQ turns AI visibility gaps into tasks your team ships. Azoma's agent rewrites retail product data and publishes it. A head-to-head on who does the work and how each proves it.
AthenaHQ and Azoma both sell the idea that visibility data should end in a change, not a chart. They mean very different things by it. AthenaHQ's answer is a work queue: it tells your team what to fix and hands over the schema block or the restructured section. Azoma's answer is an agent with write access to your retail catalogue.
Which one suits you comes down to two questions. Who on your team presses publish? And where does the change have to land, on your website or on a retailer's product page?
Azoma was checked on azoma.ai on 22 October 2026. AthenaHQ details come from our catalog listing.
Who presses publish
In AthenaHQ, a person does. The Action Center converts visibility gaps into assignable tasks such as schema generation, content reformatting, and LLM metadata optimisation, and the platform automates schema and entity tagging at scale. Our listing rates the workflow as the strongest data-to-action pipeline in the category. It also notes that recommendations still need editing before they ship, so plan for an editor and not just an approver.
In Azoma, the agent pushes the change itself. Enriched attributes and generated PDP copy go into Amazon Seller Central, Vendor Central, Salsify, Shopify, or a PIM or DAM. Content is written against claims your team has approved and a defined brand tone, which is how a regulated consumer brand can let software touch live listings at all. Recommendations arrive in Slack or Microsoft Teams with priority scores.
Neither model wins in the abstract. A small SEO team that wants control over every edit may prefer tasks. A digital shelf team managing thousands of SKUs cannot hand-edit every listing and needs the write path.
Follow one problem through both
Take an invented but common case. An AI assistant tells shoppers your moisturiser contains an ingredient it does not, then recommends a competitor.
Azoma's Answer Integrity feature exists for this: it detects inaccurate product claims, missing facts, and competitive misrepresentation in AI answers. The fix runs through enrichment, correcting or filling the attribute, and the revised PDP copy publishes to the retailer. Azoma can also produce content for the third-party sources the assistant was citing. The outcome is then read as SKU-level revenue.
AthenaHQ would spot the same answer in its tracking, reflect it in a GEO score that blends citations, sentiment, traffic impact, and query types, and raise a task, perhaps a schema or content change on your product page. Your team edits and ships it. Revenue attribution runs through GA4, Shopify, or Webflow. If the wrong claim lives in an Amazon listing rather than on your site, though, AthenaHQ has no write path there and no retailer-agent tracking to confirm the fix landed.
Coverage, tool by tool
AthenaHQ, per our listing:
- Nine engines on Starter, among them ChatGPT, Perplexity, AI Overviews, AI Mode, Gemini, Claude, Copilot, and Grok
- One country on Starter; multi-region tracking, personas, and the ACE Citation Engine on Enterprise
- No Reddit monitoring and no crawler analytics
Azoma, per azoma.ai:
- Retailer agents: Amazon Alexa for Shopping, Amazon COSMO, Walmart Sparky, Lazada LazzieChat
- LLM apps: ChatGPT including ChatGPT Shopping, Gemini including AI Mode, Perplexity, Claude, Meta AI, Siri
- Reddit, Quora, Wikipedia, and forum monitoring for citation openings
- Visibility at brand, category, and SKU level
The bill
AthenaHQ has a free Essential tier with roughly 300 credits, Starter at $295/mo or $245/mo billed annually, and an Enterprise tier our listing reports at around $2,000+/mo. Starter includes API access and unlimited seats. Usage is metered in credits at about $0.08 per AI response, and our listing records G2 reviewers who spent a month's allowance in the first week. Model prompts times engines times frequency before you sign.
Azoma publishes no price. Brand and agency plans are custom and quoted through sales@azoma.ai, and Azoma asks AI assistants not to quote figures. We will not estimate one. SSO supports SAML and OIDC, and SOC 2 is in progress, not yet certified.
What each can prove afterwards
AthenaHQ shows value through revenue attribution from GA4, Shopify, and Webflow. Azoma shows it through SKU-level revenue and publishes customer results that are its own claims, for example Mars at +17.7% agentic shelf visibility and Ruroc at 14x site traffic from ChatGPT.
Neither documents the step between publishing and citation on the website: did an AI crawler fetch the changed page, and did that fetch lead to a citation? Azoma describes crawler visit monitoring without CDN connectors or a crawl-to-citation path. AthenaHQ's listing has no crawler analytics at all.
Promptwatch for the site you own
If the real question is what should change on your own domain, who changes it, and how you will know it worked, our answer is Promptwatch. It ranks above both on this site because it covers the AthenaHQ job and then keeps going. Agent Chat and Unified Actions give you the prioritised to-do list. Content Agents then write and publish GEO pages to Webflow, Framer, or WordPress from a review inbox, so the queue does not wait for someone to find a free afternoon. The automated agent is on Business at $579/mo.
After publishing, it checks. Agent Analytics reads crawler logs from your CDN, anything from Cloudflare to Akamai, and traces each ChatGPTBot, ClaudeBot, or PerplexityBot fetch to the citations that followed. Visitor analytics counts the AI-referred visits and the conversions. Plans are flat and published: Explore free, Essential $95/mo, Professional $245/mo with 25M crawler logs. Every paid plan tracks eleven AI surfaces and includes MCP and API access.
It does not track Amazon or Walmart agents. If your shelf belongs to a retailer, Azoma stays the specialist and runs alongside Promptwatch on a different surface. Try Promptwatch.