When Buyers Ask AI: “What's the Best Tool for...?”
Developers and technical buyers increasingly turn to AI for software recommendations. When they ask about your category, is your product being mentioned—or are competitors capturing the consideration?
Developer asking:
“What are the best alternatives to [Your Product Category] in 2025?”
AI Response:
“Here are some popular alternatives to consider:
- • [Competitor A] - Great for enterprise teams...
- • [Competitor B] - Best for startups...
- • [Competitor C] - Strong API integrations...
Is your product in that list?
Weekly ChatGPT users asking for recommendations
Source: OpenAI
B2B buyer journey before contacting sales
Source: Gartner/Forrester
Amazon revenue from AI recommendations
Source: Amazon
The New Battleground for Tech Mindshare
In a crowded SaaS market, being discovered is half the battle. AI is becoming a key discovery channel—and most tech companies aren't monitoring it.
Developer-Led Discovery
Developers routinely ask AI for tool recommendations. Being absent from these recommendations means missing a significant discovery channel.
The Comparison Problem
AI constantly compares products in your category. If your unique value props aren't understood, you're described generically—or not at all.
Integration Perception
AI shapes perception of your product's ecosystem—integrations, APIs, compatibility. Gaps in this knowledge affect technical decision-making.
Market Position at Risk
While you invest in marketing, competitors may be better represented in AI responses—gaining mindshare without traditional ad spend.
A Technical Buyer's Journey
A VP of Engineering asks their AI assistant: “We're evaluating observability platforms. What should we consider beyond Datadog?”
The AI responds with a thoughtful comparison—mentioning pricing structures, integration capabilities, and use cases. Three alternatives are highlighted with specific strengths for each.
Your platform isn't mentioned.The VP proceeds to evaluate those three alternatives. By the time your sales team hears about the opportunity—if they ever do—your competitors are already deep in proof-of-concept discussions.
What Refractia Reveals for Tech Companies
Actionable insights for technology brands and SaaS companies
Product Trait Analysis
Discover how AI describes your product's features, use cases, and differentiators. See if AI understands what makes you different—or if you're perceived as "just another X."
Example: AI might describe you as "similar to [competitor]" rather than highlighting your unique advantages.
Category Share of Voice
See how often AI mentions your product versus competitors when asked about your category. Track your share of AI recommendations over time.
Example: In "best project management tools" queries, track whether you appear in top 3 or top 10.
Technical Perception
Understand how AI perceives your technical capabilities—scalability, security, API quality, integrations. These factors heavily influence B2B decisions.
Example: See if AI accurately describes your REST API capabilities and integration ecosystem.
Competitive Positioning
Monitor how AI positions you against direct competitors. Identify where competitors are perceived stronger—and where you have unrecognized advantages.
Example: Discover that AI recommends competitors for "enterprise use" even though you have enterprise features.
Technology-Specific AI Perception Risks
Outdated feature information from old documentation
Buyers think you lack capabilities you've had for years
Being categorized incorrectly (e.g., "for small teams" when you serve enterprise)
Enterprise buyers exclude you during initial research
Technical debt or past performance issues still referenced
Historical problems shape current perception
Competitors mentioned more frequently in category recommendations
Market position erodes in AI-influenced discovery
Integration capabilities understated or missing
Technical buyers assume ecosystem limitations
Winning in AI-Driven Discovery
Tech Companies Can:
- Monitor AI perception across technical queries
- Ensure documentation shapes AI understanding
- Track competitive positioning in category queries
- Identify content gaps affecting AI perception
- Measure impact of launches on AI recommendations
Strategic Questions:
- When developers ask about your category, are you mentioned?
- Does AI understand your technical differentiators?
- How do you compare to competitors in AI responses?
- Are outdated descriptions limiting your opportunities?
- Can you track how perception changes after updates?
Frequently Asked Questions
Why does AI perception matter for tech companies?
Developers and technical buyers frequently ask AI for tool recommendations. Being mentioned—or not—in these responses can significantly impact lead generation and market positioning. This is especially critical in crowded SaaS categories.
How does AI evaluate software products?
AI models synthesize information from documentation, reviews, GitHub activity, developer forums, and tech publications to form perceptions about software products and their capabilities.
Can SaaS companies improve their AI recommendations?
Yes. By understanding how AI currently perceives your product and optimizing your content strategy—documentation, developer advocacy, technical content—you can improve how AI describes and recommends your software.
How often should tech companies monitor AI perception?
Given the rapid pace of technology and frequent product updates, monthly monitoring is recommended. After major releases or announcements, more frequent tracking helps measure impact on AI perception.
See How AI Perceives Your Tech Brand
Get a comprehensive analysis of your product's AI perception. Understand competitive positioning, identify gaps, and discover opportunities to improve AI recommendations.