Pipeline planAI shortlist and off-site authority
Enterprise B2B teams that rank in Google but are missing or outranked in AI vendor recommendations for their category.
Make your company easy for AI buying agents to evaluate
In short
An agent-ready buying audit checks whether AI agents that research and shortlist vendors for buyers can read, compare and correctly describe your offer. PlanConversion tests your site with real agent tasks, then fixes what blocks them: product and service facts locked in PDFs or images, unclear pricing logic, missing specifications, weak structured data, crawler access and the llms.txt summary. Gartner forecasts that AI agents will intermediate $15 trillion in B2B purchases by 2028.
Built for: Industrial, manufacturing, software and services companies whose buyers compare specifications, compliance and commercial terms before talking to sales.
Last updated
Signs you need this
What usually goes wrong
Facts agents cannot read
Specs, certifications and pricing logic sit in PDFs, images or gated pages, so an agent compares you on incomplete data.
Inconsistent product data
The website, datasheets, partner portals and marketplaces disagree, and the agent picks whichever version it finds first.
No idea how agents see you
Nobody has tested what an agent reports back to a buyer when asked to compare your company with two competitors.
Process
How it works
Agent task tests
Run realistic buyer tasks through AI agents and assistants (shortlist, compare, check compliance, estimate cost) and record what they get right and wrong.
Readability audit
Crawler access, page rendering, structured data, product and service facts, specifications, documentation and llms.txt.
Fact layer
One approved, machine-readable set of product facts, specs, certifications and commercial terms published in HTML and schema.
Retest and monitor
Repeat the agent tasks after the fixes and add them to a recurring check as engines and agent tools change.
Deliverables
What you get
- Agent task test report against 2 to 3 competitors
- Technical readability audit (crawl, rendering, schema, llms.txt)
- Approved product and service fact sheet for the site
- Structured data and page templates for specs and terms
- Recurring agent retest plan
Measurement
How success is measured
Agent tasks answered correctly about your offerProduct facts available in HTML and schemaAccuracy of comparisons against competitors
FAQ
Agent-ready buying audit: common questions
More Generative engine optimization programs
What is an AI buying agent?
Software that researches, compares and sometimes negotiates on behalf of a buyer, for example an assistant asked to shortlist three vendors that meet a specification. Forrester predicts that in 2026 one in five B2B sellers will have to respond to buyer agents in quote negotiations.
Do we have to publish our prices?
No. Many enterprise offers are quoted. What agents need is clear pricing logic: what drives the price, minimums, and how to get a quote, so they do not guess or skip you.
Is this the same as technical SEO?
It builds on technical SEO but tests a different reader. A page can rank well and still give an agent incomplete specs or contradictory facts.
Which companies need this first?
Companies with complex products and long specification lists, such as industrial technology, manufacturing, logistics and enterprise software, where buyers already run structured comparisons.
Enterprise needs
Often solved together
AI governance for marketing teams
Enterprise marketing teams in regulated or brand-sensitive industries where legal, IT or procurement must approve AI use before it scales.
Buying-group consensus and deal acceleration
Enterprise sellers with healthy pipeline but deals that stall in late stages, long sales cycles and buying committees of 6 to 10 people.
Insights
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