Ira Bodnar says Jev cut his SEO agent cost by 90%
A vendor post reports speed and price across nine pipeline steps, and says nothing about whether the audits still hold up.
Ira Bodnar says his agents that audit and fix a client's SEO and GEO used to cost about $250 per run, and that moving to Jev cut that by 90%. He posted a breakdown of where the savings came from, nine steps in the pipeline, eight of them listed at 30x faster and one at 20x.
Agents that audit and fix a client's SEO/GEO used to cost us ~$250
What the nine steps are
The reads are the boring part and apparently the expensive part. Steps one through five cover reads of Search Console and PostHog or Mixpanel data, checks of what ChatGPT searches on Bing, modeling of what users ask Gemini and Claude, scans of who ChatGPT and Claude cite, and analysis of the sources behind those citations. Step six is gap analysis, described as why they get cited and we don't. Step seven is fixes across thousands of pages on large client sites, step eight sorts which page types ChatGPT cites, and step nine, the 20x one, is creating the pages themselves.
That shape matches what Jev is good at. The heavy classification and extraction passes over data you already have are the parts that go fast, and the generative step at the end is the one that improved least.
What the post does not say
Bodnar sells the thing. The agent is available in the Ryze AI app and as an MCP or Claude Connector, and the post ends with a link to try it. The figures are speed and price only. There is no measurement of whether the audits found the same issues, whether the fixes were the same fixes, or how the 90% was computed against the old $250 figure. One operator, one workload, self reported.
For people building on it, treat the 30x as a per step read throughput claim from a vendor, not a benchmark, and note that the step producing new text got the smallest gain. If your pipeline is mostly generation rather than classification over data you already hold, the arithmetic here does not transfer.
The cost pressure behind this is not unique to Bodnar. Vercel reported Jev hit about 13% of AI Gateway teams on day one, which is the same story from the platform side.
Jev was adopted faster than any other model in AI Gateway history.