How do B2B SaaS companies get recommended by AI search engines in 2025?
Forget SEO. AI sends qualified leads to companies that publish answer blocks, dominate Reddit, and keep facts consistent. 30-day playbook inside.
Liam Dunne
Growth marketer and B2B demand specialist with expertise in AI search optimisation - I've worked with 50+ firms, scaled some to 8-figure ARR, and managed $400k+/mo budgets.
September 13, 2025
Published: September 5, 2025|Updated: September 13, 2025
6 mins
B2B SaaS companies get recommended in AI search engines through a 4-step approach: conducting baseline assessment to identify gaps, publishing answer-ready content that AI can extract, building third-party authority where models look for validation, and maintaining consistent entity information across the web. Companies implementing this strategy see mentions in ChatGPT, Perplexity, and Google AI Overviews within 30-90 days, with AI-driven traffic converting 23× higher than traditional organic search according to Ahrefs research.
From talking with companies, it seems that everyone's still trying to figure out the playbook. Most are stuck applying SEO tactics to AI search. Here's what we've learned from hundreds of visibility tests.
Key statistics: why AI search optimisation matters now
A huge misconception I keep hearing: "Only consumers use AI assistants." Wrong. Your enterprise buyers are asking ChatGPT for vendor recommendations right now. The data backs it up:
Only 12% overlap between URLs cited by AI and those ranking in Google's top 10 — traditional SEO doesn't guarantee AI visibility based on Ahrefs' AI search overlap study
Another huge misconception I keep hearing is that SEO is exactly the same as AI search optimisation. This is wrong, and here's why:
The fundamental difference between SEO and AI search optimisation (Answer Engine Optimisation or AEO) is the objective. SEO aims for rankings and clicks, while AI search optimisation aims for citations and recommendations. It's possible and quite common for companies that have historically dominated the SERP to lose Share of Voice (SOV) to smaller competitors in AI Search.
Different retrieval methods
AI assistants don't crawl the web like Google. Instead, they fan out dozens of targeted queries based on user prompts, skim snippets from multiple sources, synthesise answers from trusted citations, and personalise recommendations based on session context and location. It's a completely different game and most SEO tools haven't caught up yet.
Microsoft Copilot prefers corporate sources like Forbes and Gartner
The 4-step playbook for B2B SaaS
Step 1: Baseline assessment (days 1-7)
Map your current AI visibility
Before creating content, understand where you stand:
Map 150-300 prompts that your buyers actually use
Test current visibility across ChatGPT, Perplexity, Gemini, and Copilot
Identify top 30 gaps where competitors appear but you don't
This assessment reveals which content to prioritise. Most B2B companies discover they're invisible for 80% of buyer queries while competitors dominate. Focus your efforts on the highest-impact gaps first.
Step 2: Build answer-ready content (days 8-45)
What to publish first
Create pages that directly answer buyer questions:
Comparison pages: [Your Product] vs [Competitor] (yes, mention them by name)
Alternatives pages: Best [Competitor] Alternatives for [Use Case]
Best-for pages: Best [Category] for [Industry/Stack/Budget]
Integration pages: How [Your Product] Integrates with [Popular Tool]
Use-case pages: [Category] for [Specific Scenario]
FAQ hubs: Common questions with direct answers (keep them under 80 words)
Optimal content structure
Best [Category] for [Use Case] in 2025
Quick answer: [Product A] for enterprises needing [X], [Product B] for startups wanting [Y], [Product C] for teams on a budget.
Product
Best for
Starting price
Key limitation
[A]
Enterprise
$2,000/mo
Complex setup
[B]
Startups
$99/mo
Limited seats
[Your Product Name]
[Your Product] is a [category] that [core value prop].
AI trusts external sources more than your own site. Focus on these venues:
Reddit discussions in relevant subreddits
G2/Capterra reviews with specific use cases
YouTube demos showing actual workflows
Technical forums where developers compare tools
Industry-specific communities and Slack groups
How to earn authentic mentions
Monitor brand mentions using tools like Brand24 or Mention
Respond to comparisons with factual corrections (not sales pitches)
Create comparison content that affiliates and reviewers can reference
Enable customer advocacy with case study templates and review incentives
The compound effect
Third-party mentions create a flywheel. One authentic Reddit mention leads to more discussions, which leads to more AI citations, which drives more organic mentions.
Step 4: Maintain entity consistency (ongoing)
Why consistency matters
AI models validate facts across multiple sources. Conflicting information reduces citation confidence. When your pricing differs between your site and G2, or your feature list varies across directories, AI models often skip citing you entirely.
Entity checklist
Product name (exact match everywhere)
Pricing (current and consistent)
Feature list (same terminology)
Company description (single version)
Integration partners (complete list)
Customer count/logos (updated quarterly)
Where to update
Your website: Homepage, about, pricing pages
Review platforms: G2, Capterra, TrustRadius
Data sources: Crunchbase, PitchBook, Wikipedia (if applicable)
Social profiles: LinkedIn company page, Twitter/X bio
Don't create separate "AI-optimised" pages. Instead, restructure existing content to be more extractable. AI models can detect and often ignore content created solely for them.
2. Neglecting Reddit and forums
Many B2B companies ignore community platforms, but Perplexity's citation patterns show Reddit appears in 40% of B2B software recommendations. Authentic participation matters more than promotional content.
3. Inconsistent entity data
When your pricing on G2 doesn't match your website, AI models flag the discrepancy and often exclude you from recommendations entirely. Audit and align all entity data quarterly.
4. Ignoring technical documentation
Marketing pages rarely get cited. Technical documentation, integration guides, and API references receive 3× more AI citations because they contain specific, factual information that AI models can confidently reference.
5. Over-optimizing for one model
Each AI model has different biases. ChatGPT loves Wikipedia-style content, Perplexity prefers Reddit discussions, and Google AI mixes everything. Optimise for all models, not just one.
Measuring AI search impact
Direct metrics
AI citation monitoring: Use manual searches or specialised tools to track mentions
Mention rate: Percentage of relevant queries where your brand appears
Citation rate: How often AI cites your content as the source
Share of voice: Your citations compared to competitors
Branded search volume: AI mentions drive 20-40% increases in brand searches
Direct traffic spikes: Unexplained direct traffic often indicates AI referrals
Demo request quality: Track "heard about you from ChatGPT" mentions in forms
AI-driven traffic doesn't appear in traditional analytics. Most arrives as direct traffic or branded search. Focus on correlation between AI optimisation efforts and quality lead increases rather than perfect attribution.
Implementation timeline
Days 1-7: Assessment
Map 150-300 buyer prompts
Test visibility across all AI platforms
Identify competitive gaps
Prioritise content opportunities
Days 8-20: Foundation
Create 5 comparison or alternative pages
Add structured data markup to all product mentions
Update timestamps across all pages
Build answer blocks for existing content
Days 21-30: Authority building
Identify and join 5 relevant Reddit communities
Update all directory profiles (G2, Capterra, Crunchbase)
Create demo videos showing actual use cases
Participate authentically in community discussions
Days 31-45: Optimisation
Align entity data across all platforms
Add FAQ sections to high-traffic pages
Set up citation monitoring
Track branded search and direct traffic changes
How Discovered Labs helps
We've mapped what each AI model prioritises, built technology to track citations across all platforms, and developed the playbook that consistently generates AI recommendations within 30-90 days. We ship daily researched, high-quality content that's engineered for AI citation.
What we do differently:
Track your AI citations across ChatGPT, Perplexity, Claude, and Google AI
Identify specific content gaps preventing recommendations
Manage the complete optimisation process from content to entity alignment
Report on actual AI visibility metrics, not traditional SEO vanity metrics
We audit your current AI visibility and show exactly what needs fixing. No generic strategies or lengthy discovery processes.
Most companies see initial citations within 30-90 days. Companies with strong technical documentation and clear entity definitions typically see results faster. The key is maintaining consistency across all platforms.
Which AI platform should we optimise for first?
Optimise for all platforms simultaneously since the core requirements overlap. However, if you must prioritise: technical buyers use ChatGPT and Perplexity, enterprise buyers rely on Microsoft Copilot, and SMB buyers default to Google AI Overviews.
Do we need to rewrite all our content?
No. Focus on creating 10-15 answer-ready pages first: comparison pages, alternative pages, and technical documentation. Add answer blocks to existing high-traffic pages rather than complete rewrites.
Can we track ROI from AI optimisation?
Direct attribution remains challenging since AI-driven traffic appears as direct traffic or branded search. Track proxy metrics: branded search increases (typically 20-40%), demo request quality, and mentions of AI sources in sales calls.
Should we create AI-specific content?
No. AI models can detect and often ignore content created solely for them. Instead, restructure existing content to be more extractable: clear answer blocks, structured data, and factual information.
