How to Get a B2B Software Product Recommended by AI Assistants

How do assistants choose which software to recommend?

When buyers ask for software, assistants usually search, read a handful of pages and summarise. The sources they rely on most are:

• Review platforms and their category pages, which show ratings, segments and alternatives.
• Comparison and "best of" lists, where inclusion and position matter. Peec's 2026 study of nearly 200,000 AI responses found that being ranked first in frequently cited lists raised mention rates by 13 to 17 percentage points in the software categories studied.
• Analyst and trade coverage, which carries weight for enterprise categories.
• Vendors' own pages, when they answer specific questions about pricing, integrations and use cases.
• Documentation and developer communities, for technical products.

For questions about specific brands, third-party sources dominate. Omniscient Digital's 2026 analysis found reviews, lists, forums and case studies made up 57% of citations for branded questions.

What is the core playbook?

1. Clarify your category and ideal customer in one plain sentence, used everywhere.
2. Publish specifics: pricing, integrations, security certifications, deployment options and limits, in HTML text.
3. Build use-case and segment pages for the problems and buyers you win.
4. Publish fair comparison and alternatives pages against the tools buyers shortlist.
5. Ask every customer for an honest review on the platforms buyers read, keep your review platform categories accurate, and ask reviewers to complete their company size and industry fields, so segment filters show the customers you serve.
6. Get into the lists and coverage that assistants cite for your category.
7. Keep documentation public and current, especially for technical buyers.
8. Allow AI search crawlers and check that key content renders without JavaScript.

Measure with unbranded questions, repeated runs and separate tracking for each engine. Whether money can buy recommendations is covered in whether a vendor can train or pay ChatGPT to recommend a SaaS product.

Checking whether assistants recommend you

How can I tell whether ChatGPT recommends my AI agent to developers looking for a customer support platform?

Ask the questions developers ask, such as "best AI agent platform for customer support with an API" and "[your product] vs [competitor] for developers". Run them several times in ChatGPT with search on. Developers weigh documentation, SDKs, pricing for usage and GitHub activity, so check whether answers mention your docs and whether your API reference is public and readable.

How can I check whether AI assistants recommend my conversational AI platform?

Test category questions ("best conversational AI platform for banks"), capability questions ("conversational AI with voice and live agent handoff") and comparisons. Record whether you are named, how you are described and which sources are cited. Conversational AI is crowded, so answers often default to the best-known platforms unless your use-case pages and reviews name specific industries and integrations.

Shopify app founders checking AI visibility before launching a new inventory tool

Before launch, run the questions merchants ask, such as "best inventory app for Shopify stores with multiple warehouses". Note which apps are named and which sources are cited: often the Shopify App Store, comparison articles and review sites. Plan an app listing with a clear description, early reviews and a public website with use-case pages.

How can I measure whether AI recommends our collaborative work management software against Jira and Trello?

Track category and comparison questions such as "best work management tool for marketing teams" and "[your product] vs Jira for non-technical teams". Measure unbranded mention rate and position against both over repeated runs. Jira and Trello are named in many answers by default, so focus on segment questions where your product has a clear advantage.

Why is my SaaS brand invisible in ChatGPT recommendations?

Invisibility usually has a basic cause. Check that crawlers can reach your site, that assistants can describe what you do, that you appear on review platforms and comparison lists, and that your pages state pricing and use cases. The general check is covered in how to check whether ChatGPT recommends your SaaS or business.

When a well-known competitor is recommended instead

Learning management software vendors losing enterprise buyers to Cornerstone and Docebo in AI search

Cornerstone and Docebo are well established, with many enterprise reviews and analyst coverage, so they dominate generic enterprise questions. Challengers win narrower ones: a specific industry, compliance training, extended-enterprise training or a particular integration. Publish enterprise case studies with company size and results, and make sure reviewers' company size is recorded on your review profiles.

Why does Perplexity recommend BlackLine instead of our financial close software?

BlackLine is a long-established financial close vendor with extensive analyst and review coverage, so it is a default answer. Compete on specifics: company size, ERP integrations, implementation time and pricing transparency. Pages such as "financial close software for mid-market companies on NetSuite" match the narrower questions where challengers are chosen.

Why does Perplexity recommend Applied Epic instead of our insurance agency management software?

Applied Epic has deep coverage in insurance agency circles. Questions that specify agency size, lines of business, carrier integrations or migration give challengers a chance. Publish integration lists, migration guides and case studies from agencies like your target buyers, and get listed in insurance technology directories and trade media.

Why does Google AI Overviews cite Backstage instead of our developer experience software?

Backstage is a widely adopted open-source developer portal originally built at Spotify, so it is cited heavily for developer portal questions. Explain how your product relates to it, as a hosted alternative, an extension or a different approach, and publish honest comparisons covering setup effort, maintenance and features.

Agent observability software for a company losing recommendations to Datadog and New Relic

Datadog and New Relic dominate general observability questions. Agent observability is newer, so specific questions, such as tracing LLM agent calls, evaluating outputs or monitoring token costs, give a challenger room. Publish clear capability pages, integration guides for agent frameworks, and technical posts developers can cite.

Why does Perplexity recommend Zendesk AI instead of our customer service AI agent software?

Zendesk AI benefits from Zendesk's large installed base and review volume. Challengers win questions about specific help desks, channels, languages or resolution rates. Publish results with methods, integration pages for the help desks you support, and comparisons that state honestly where you fit.

Why does Perplexity recommend Better Stack instead of our log management software for small SaaS teams?

For small SaaS teams, answers weigh price, setup time and bundled features such as uptime monitoring. Better Stack is often recommended on those grounds. Publish a small-team pricing page, setup guides and a comparison focused on the features small teams use.

Why does Perplexity recommend Power BI instead of our business intelligence software?

Power BI is often the default because many organisations already use Microsoft 365. Challengers win on specific needs: embedded analytics, particular data sources, ease of use for non-analysts, or pricing at small scale. Publish use-case pages for these and comparisons that acknowledge when Power BI fits.

Why is Perplexity recommending PagerDuty instead of our AIOps software for incident response?

PagerDuty is the established name for incident response. AIOps questions about alert noise reduction, event correlation or automated remediation are more open. Publish specific capability pages, integration guides with monitoring tools, and measured results such as alert reduction with the method explained.

Why does Perplexity recommend CloudHealth instead of our cloud cost management software?

Cloud cost management questions ask about supported clouds, Kubernetes cost allocation, savings achieved and pricing. CloudHealth has long been an established name. Publish pages on each cloud and on Kubernetes, savings case studies with methods, and comparisons with the tools buyers shortlist.

Why does Perplexity recommend Deel instead of our employer of record software?

Deel has very broad coverage in employer of record questions. Compete on specific countries, industries, contractor versus employee needs, or price. Publish country pages with what you offer in each, transparent pricing and customer reviews.

Why does Perplexity recommend Ramp instead of my spend management software?

Ramp is widely covered for spend management. Challengers win on specific needs: company size, accounting integrations, international entities or approval workflows. Publish pages that state these clearly, pricing and comparisons.

Why does Perplexity recommend Chargebee instead of our subscription billing software?

Chargebee is well documented and widely reviewed for subscription billing. Compete on specifics: usage-based pricing models, supported payment gateways, revenue recognition or price. Publish detailed pages for each, with integration guides.

Why does Perplexity recommend OneTrust instead of our privacy management software?

OneTrust is a long-established privacy management vendor with broad coverage. Challengers win on company size, specific regulations, implementation time or price. Publish regulation-specific pages, implementation timelines and customer examples by segment.

Why does Perplexity recommend Clio instead of our legal practice management software?

Clio is widely reviewed and integrated in legal practice management. Challengers win on firm size, practice area, jurisdiction or specific features. Publish practice-area pages, migration guides and customer reviews.

When the category itself works against you

Why are AI assistants recommending horizontal software instead of our industry-specific platform?

Horizontal tools have far more mentions, reviews and list placements. Industry platforms win when the question names the industry and its specific needs. Publish pages on the regulations, workflows and data your industry requires that horizontal tools do not handle, and seek coverage in the industry's trade publications, which assistants cite for niche questions.

B2B SaaS CTO investigating why AI assistants recommend open-source alternatives

Open-source tools are discussed widely by developers, have public repositories and are free, so assistants suggest them for many questions. Compete honestly: explain when a managed product is the better choice, covering support, security, maintenance cost and time to value. Publish total-cost comparisons that include the engineering time open-source tools require.

Why does ChatGPT recommend a different payment gateway than my business?

Payment questions are usually about fit: countries, currencies, fees, payout speed and platform integrations. Answers name the gateway whose public pages state those facts most clearly. Publish supported countries and currencies, fee tables in text, and integration pages for the ecommerce and billing platforms your merchants use.

Why does AI recommend a competitor instead of our warehouse management software?

Warehouse management questions are specific: warehouse size, industry, integrations with ERPs and carriers, and deployment time. Answers name vendors whose pages state these. Publish pages by warehouse type and integration, with implementation timelines, and ask every customer for an honest review on the platforms buyers read.

Why do AI assistants recommend a competitor instead of my reverse ETL platform for Segment integrations?

Reverse ETL answers often name tools with well-known Segment integrations and documentation. Publish a detailed Segment integration page covering what syncs, in which direction and on which plans. Keep docs public, and make sure listings in Segment's own integration catalogue and partner directories are accurate.

Why does Perplexity recommend a competitor instead of our enterprise wiki software?

Enterprise wiki questions often mention scale, permissions, integrations and search. Answers name vendors with strong review profiles and many comparisons. Publish comparison pages, security and permission documentation, and migration guides from the tools buyers are leaving. Ask every customer for an honest review, with company size filled in.

Why is Perplexity recommending customer feedback tools with fewer integrations than ours?

Integration counts matter only if assistants can see them. Publish an integrations directory in HTML, one page per major integration, stating what each does. Answers may favour competitors with fewer integrations because their integration pages are clearer, or because they appear in more review lists.

Why does Perplexity recommend another audit management platform instead of ours?

Audit management buyers ask about frameworks supported, such as SOX, ISO or internal audit standards, and integrations with GRC tools. Publish framework-specific pages, customer examples by industry, and comparisons. Check your listings in review platforms' audit management categories.

Why does Perplexity recommend another SaaS management platform instead of ours?

SaaS management answers weigh discovery methods, integration counts, license optimisation and pricing. Publish these clearly, with the number of integrations and how discovery works. Seek placement in the comparison lists that Perplexity cites for this category.

Why does Perplexity recommend competitors instead of our release orchestration platform?

Release orchestration is a niche where answers lean on a few comparison articles and vendors with clear documentation. Publish capability pages, integration guides for CI/CD tools, and a comparison page. Check whether the articles Perplexity cites include you.

Why is nobody recommending our roadmapping software in ChatGPT answers?

Roadmapping software is often named alongside project management tools. Answers favour tools with clear category positioning and many reviews. Make sure your review profiles sit in the roadmapping category, publish comparisons with the tools buyers consider, and describe specific roadmapping use cases.

How do I get my browser extension recommended by ChatGPT?

Browser extension questions are answered from the Chrome Web Store, comparison articles and extensions' own sites. Keep your store listing clear, with specific use cases, and gather reviews there. Publish a website with use-case pages and privacy information, since users ask assistants whether extensions are safe.

Which pages to fix first

Which pages should a data observability vendor fix first to improve AI recommendations?

Start with the pages that answer buyers' evaluation questions: supported data sources and warehouses, pricing, and comparison pages against the tools buyers shortlist. Then fix documentation on setup and alerting, which engineers ask about. Pages that only describe "data trust" in general terms give assistants nothing specific to cite.

Why does Google AI Overviews cite competitor pages instead of our revenue intelligence software?

AI Overviews cite pages that rank and answer the question directly. Compare the competitor pages cited with yours: they often have clearer definitions, comparison tables or pricing. Check your pages rank for the sub-questions buyers ask, such as call recording compliance or CRM integrations, and that key text is in HTML.

What should a fraud screening vendor fix when AI cites competitors instead of its product pages?

Fraud screening buyers ask about chargeback rates, approval rates, supported payment platforms and pricing models. Fix product pages so they state these specifically, with published case results where customers allow. Add comparison pages and make sure analyst and review coverage is current.

Which database software pages should we fix first when AI assistants recommend competitors?

Start with pages that answer the deciding questions: performance and scaling characteristics, supported deployment options, pricing and migration from competing databases. Benchmarks with published methods are widely cited. Documentation for common tasks should be public and current.

Why does Gemini recommend another inventory planner for Shopify replenishment questions?

Gemini draws on Google Search, where replenishment questions are answered by app store listings, comparison articles and apps' own pages. Check your app's Shopify App Store listing and your site's replenishment page rank for those searches. Specific forecasting features, supported store sizes and pricing help.

What should we fix first when AI recommends a marketing automation competitor for lead scoring?

Start with a clear page on how lead scoring works in your product: the models used, the data required and the CRM integrations supported. Add a comparison page focused on lead scoring, and case studies with conversion results. Answers often name the competitor whose scoring features are described in the most detail.

How should I plan use-case landing pages so ChatGPT recommends my B2B SaaS for specific buyer problems?

Plan one page per buyer problem that buyers actually ask about, not per keyword variation. Each should name the role and the problem, show the workflow and include an example or result. Prioritise problems where you win deals, and avoid thin variants that risk search spam penalties.

How can a new project management app get recommended by ChatGPT in a crowded market?

In crowded markets, generic questions go to the best-known tools. Pick a narrow segment where your app is clearly better, publish pages for that segment, and ask every customer for an honest review in their own words; as you win that segment, its users will make up more of your reviews. Seek inclusion in lists aimed at that segment, and grow from there.

What do people ask about getting B2B software recommended by AI?

How long does it take for a B2B product to start being recommended?

Search-based answers can change within weeks of new pages, reviews and list placements. Answers from model knowledge change more slowly, with newer model releases.

Should we target the category leader's name in our content?

Yes, through fair comparison and alternatives pages. Buyers ask about the leader, and honest comparisons are cited.

Do analyst reports matter for AI recommendations?

For enterprise categories, often. Assistants cite analyst coverage, and buyers trust it.

What is the most common reason B2B products are missing?

Unclear positioning combined with thin third-party evidence: few reviews, no list placements and vague product pages.