Be the tool AI recommends
When a developer asks ChatGPT which tool to use, or a buyer asks Perplexity for the best software for a job, the evaluation is over before your demo. Engines assemble those shortlists from review sites, communities, docs and comparisons. We run the program that puts you in the answer — and keeps what it says about you accurate.
Win the evaluation before the demo
AI has compressed the software evaluation: fewer tabs, fewer review-site visits, one synthesized recommendation. For SaaS, the stakes are double — assistants not only pick the shortlist, they describe your features, pricing and limits. Being absent loses the deal; being misquoted loses it later.
Recommendation share
Presence in best-tools-for-X answers across engines, tracked per prompt and trended against the competitors you actually lose deals to.
Accurate features and pricing
Assistants routinely quote outdated plans and killed features. Structured, current pages and corroborating sources keep the answer truthful.
Source coverage
Deliberate presence on G2, Capterra, Reddit, Stack Overflow, YouTube and the listicles engines cite when they assemble SaaS shortlists.
Docs that acquire
Documentation, changelogs and integration pages become an acquisition surface once models can parse and quote them.
Built for SaaS teams losing invisible evaluations
PLG or sales-led, the pattern is the same: more of your category’s evaluations finish inside an AI conversation you currently have no presence in and no measurement of.
ChatGPT recommends competitors for the exact prompts your product should own.
Assistants quote pricing you changed a year ago, or features you killed two releases back.
Your pages rank fine in classic search, but AI answers cite a reviewer’s listicle instead of you.
Reddit threads visibly drive the AI answers in your category — and nobody owns that channel.
Signups mention finding you through ChatGPT, but the channel is unmeasured and unmanaged.
Your alternatives and vs pages are thin or missing, so engines build the comparison without you.
From prompt map to managed recommendation share
Map
Build the prompt landscape across ICPs, jobs-to-be-done, integrations and migration scenarios — the questions that end in a tool choice.
Baseline
Measure recommendation share, cited sources, sentiment and factual accuracy per engine, benchmarked against category rivals.
Architect
Restructure docs, pricing, integrations, changelog and comparison pages into entity-clear, quotable sources of truth.
Earn
Run the source program — review-site profiles and velocity, community presence, and inclusion in the roundups engines retrieve.
Monitor
Track monthly with GEOly, catch accuracy drift and competitor moves early, and reinvest where share is shifting.
The SaaS answer supply chain
Engines assemble software recommendations from a predictable mix of sources. The framework works each layer deliberately — because you cannot fix an answer without fixing what it is built from.
Review layer
G2, Capterra and category review sites — profile completeness, review velocity and category placement engines lean on for shortlist candidates.
Community layer
Reddit, Stack Overflow, Hacker News and Discord — authentic practitioner discussion, consistently among the most-cited sources in AI answers about software.
Editorial layer
Best-of roundups and comparison articles — earning inclusion in the ones engines retrieve, and publishing credible comparisons of your own.
Owned layer
Docs, pricing, integrations, changelog and vs pages structured so models quote current facts instead of improvising from stale ones.
Measurement layer
Per-prompt recommendation share, cited sources, sentiment and accuracy tracked in GEOly — the scoreboard the other four layers answer to.
Where software decisions meet AI answers
ChatGPT recommendations in buyer and developer conversations
Perplexity — disproportionately used for technical evaluation
Google AI Overviews and AI Mode on software queries
Gemini and Copilot inside Workspace and Microsoft 365 contexts
Coding assistants recommending developer tools and APIs
The review sites, Reddit threads and listicles all of them cite
What the program looks like for software companies
Anonymized patterns from software engagements, structured as case-study slots that will carry approved results.
Developer-tool pattern
Docs and integration pages restructured for parsing, paired with community presence, moved the product into assistant answers for its core use-case prompts.
Sales-led SaaS pattern
Comparison and alternatives architecture plus review-site velocity shifted shortlist answers that previously defaulted to the category leader.
Accuracy-drift pattern
Assistants quoted deprecated pricing until structured pricing pages and corroborating third-party sources were re-crawled and the answer corrected.
Common SaaS AI search questions
We are PLG and developers distrust marketing — does this still work?
Yes, because the levers are not ads. Engines cite docs, communities and peer reviews — exactly the sources developers trust. The work is making those sources complete, current and parseable.
Can you fix what AI says wrong about our pricing?
Usually, yes. Engines re-retrieve: structured, dated pricing pages plus corroborating third-party sources correct the answer over re-crawl cycles. We track the drift in GEOly until it closes.
Should we build vs and alternatives pages?
Yes — honest ones. Engines answer comparison prompts regardless; the only question is whether your framing or a third party’s is the source. Credible comparison content is among the highest-leverage SaaS GEO assets.
How do we attribute AI-sourced signups?
Combine assistant referrer data, branded-search and direct-traffic lift against answer-share trends, and a how-did-you-hear field. Together they bound the channel well enough to justify investment.
Is GEO different from AEO or LLMO?
The labels vary; the discipline is the same — engineering how AI systems discover, describe and recommend you. What matters is that the program is measured at the answer level, not renamed SEO reporting.
Ask us what AI recommends in your category today.
The audit shows which tools engines recommend for your prompts, how accurately they describe you, and the fastest path onto the shortlist.