Software buyers ask assistants which tool to use, what the alternatives are, and what it really costs. We make sure your product is in that answer — not just ranking for a keyword nobody clicks anymore.
The SaaS buying journey used to run through review sites, comparison posts, and a shortlist assembled over weeks. A meaningful part of that now happens in a single conversation: a buyer describes their problem, asks what tools solve it, asks how the top two compare, and asks what they cost.
Three or four products get named in that exchange. Everyone else in the category is absent — not ranked lower, absent. And the products that get named aren't necessarily the best ones. They're the ones the model can retrieve, resolve as a credible entity, and find corroborated information about.
SaaS also has a specific structural problem: documentation and pricing frequently live on subdomains or behind rendering that crawlers can't handle, so the most decision-relevant content on the site is invisible exactly when it matters most.
Both matter, but they need different work and most SaaS teams under-invest in the first.
Best for: Almost every SaaS company. This is where deals are decided and where most vendors are weakest.
Best for: Established products with clean fundamentals looking to expand beyond bottom-funnel capture.
Four factors, and the first two are where most software companies quietly fail.
Documentation subdomains, help centres, and app-rendered pages are frequently uncrawlable. For software questions these are often the most decision-relevant content you have.
Models won't invent your pricing, and they won't fill in a demo form. Pricing that isn't structured and accessible means being omitted from every cost-related answer.
G2, Capterra, and similar sources are retrieved heavily for software questions. When their data contradicts your site, models hedge or default to the third-party version.
Comparison pages that are transparently self-serving get used less than ones acknowledging real tradeoffs. Models favour sources that read as informative rather than promotional.
The full stack, weighted toward the specific failures that keep software companies out of buying-decision answers.
Making docs, help content, and API references crawlable and structured, since these carry the specific detail models need for technical software questions.
Structuring pricing and plan limits so they're accessible and marked up, because models omit vendors whose costs they can't determine.
Building credible comparison and alternatives content that acknowledges real tradeoffs, which performs measurably better than promotional framing.
Reconciling G2, Capterra, and directory data with your own site so independent sources corroborate rather than contradict you.
Mapping integration and use-case questions to dedicated content, since 'does it work with X' is one of the most common evaluation prompts.
Citation share tracked against the prompt clusters that actually influence deals, reported alongside trial and pipeline data where attribution allows.
Where the leverage sits changes substantially with company stage.
Models don't know you exist yet. Entity establishment and category-defining content matter far more than competing for saturated head terms.
You're in consideration sets but losing comparisons. Comparison, alternatives, and pricing content is where the return concentrates.
Recognised in the category but under-covered across the long tail of use-case, integration, and vertical questions.
Usually well-known but inconsistently described — multiple products, editions, and regions creating entity confusion.
Weighted toward the bottom-funnel clusters where software deals are actually decided.
We test crawler access across your main site, docs subdomain, and app-rendered pages, then benchmark the comparison, alternatives, and pricing prompt clusters.
We map which competitors own each high-intent cluster, what content of theirs is being cited, and where review platform data contradicts your own.
Docs and pricing made retrievable, comparison content built or restructured, review platform data reconciled, schema deployed.
Monthly citation tracking on the clusters that influence deals, reported alongside trial and pipeline data where attribution allows.
Comparison pages, pricing transparency, and crawlable documentation improve organic rankings, trial conversion, and sales enablement at the same time as AI citation. The overlap here is unusually clean.
The questions clients ask us most before starting. If yours isn't here, ask us directly on a consultation call.
Usually one of three reasons: crawlers can't access your key pages, models can't resolve your product as a distinct entity, or competitors have far more independent corroboration on review platforms and in editorial coverage.
The first is more common than most teams expect, especially where docs live on a subdomain or the app is client-side rendered.
For AI visibility, it helps substantially. Models won't guess pricing and won't complete a demo form, so vendors without accessible pricing get omitted from cost-related answers entirely — and those are high-intent questions.
You don't need full transparency. Even indicative ranges or starting prices with clear plan structure meaningfully improves inclusion versus a bare contact form.
Very. They're retrieved heavily for software questions, and models treat them as independent corroboration. Where their data contradicts your site — outdated pricing, wrong category, stale feature lists — models tend to hedge or favour the third-party version.
Reconciling that data is often one of the fastest wins available in a SaaS engagement.
Yes, and honestly. Comparison pages that acknowledge genuine tradeoffs get cited noticeably more than ones claiming total superiority, because models favour sources reading as informative rather than promotional.
If you don't build them, a competitor or an affiliate site will, and they'll frame the comparison on their terms.
It matters a lot, and it's one of the most common technical gaps we find. Documentation carries exactly the specific, factual detail models need for technical questions, and subdomains are frequently excluded from crawling or blocked separately.
We audit docs crawlability as standard because it's so often the highest-value fix available.
Imperfectly, and we're upfront about that. AI-assisted research often produces direct or branded traffic rather than attributable referrals, so last-click attribution understates it.
We track citation share on the clusters that influence deals and correlate against branded search volume and self-reported source data. It's directional, not precise, and we say so.
Probably not yet, and we'd tell you so. If your category isn't being asked about conversationally and your positioning is still moving, entity work built on a moving target gets wasted.
The cheap exception is making sure crawlers aren't blocked, which costs almost nothing and avoids starting from a hole later.
Retainers typically start around $4,000–$10,000 monthly for focused engagements and run $10,000–$25,000+ for competitive categories or enterprise scope.
Standalone audits run in the low five figures. Scope is driven mainly by site complexity and how contested your comparison territory is. Detail is on our pricing page.
We'll benchmark the comparison, alternatives, and pricing questions your buyers actually ask — and show you exactly who gets named in those answers today.
No commitment · 45 minutes · Immediate value
We’ll run your category’s real questions live on the call and tell you honestly whether this is worth your budget. No pitch deck.