We Asked ChatGPT About 50 Indian SaaS Companies. Here's What We Found.
Kre8on Team
Kre8on Engineering
We ran an experiment last week. We took 50 of the most prominent B2B SaaS companies in India—from fast-growing Series A startups to established unicorns—and asked ChatGPT, Claude, and Perplexity if they knew who they were. We didn't ask "What is [Company]?" We asked the questions buyers ask: "What's the best Indian CRM for manufacturing?" or "Which HR SaaS integrates best with local payroll?"
The results were brutal. Over 60% of these companies were entirely invisible to AI assistants when it mattered most. Their target buyers are using Perplexity to build vendor shortlists, and these companies aren't even making the pre-qualification cut. But the companies that did appear had one very specific structural advantage in common.
The Experiment: What We Tested
We selected 50 Indian SaaS companies across 8 core categories:
- CRM & Sales Tech
- HR & Payroll
- FinTech Infrastructure
- EdTech Solutions
- Developer Tools
- eCommerce Enablement
- Marketing Tech
- Productivity SaaS
For each company, we didn't just search their name. We ran 10 high-intent buyer queries per platform (ChatGPT, Claude, Perplexity), mimicking how a VP of Engineering or Head of Sales searches for vendors today.
We measured three things:
- Mention Rate: Did they appear in the top 3 recommendations?
- Context: Were they framed as budget options, enterprise leaders, or just "also available"?
- Displacement: Did the AI hallucinate a global competitor (like Salesforce or Workday) when a local option was explicitly requested?
The Results: AI Visibility is a Winner-Take-All Game
Here's a representative slice of the data from our test. The scores represent the percentage of relevant queries where the brand was correctly cited and recommended.
| Company Type | Category | ChatGPT Score | Claude Score | Perplexity Score | Overall |
|---|---|---|---|---|---|
| Series C DevOps Platform | DevTools | 82/100 | 90/100 | 88/100 | 86/100 |
| Series B API Startup | FinTech | 75/100 | 80/100 | 85/100 | 80/100 |
| Bootstrapped HR SaaS | HR Tech | 65/100 | 70/100 | 72/100 | 69/100 |
| Enterprise Commerce API | eCommerce | 45/100 | 30/100 | 55/100 | 43/100 |
| Seed-Stage Marketing CRM | Marketing | 12/100 | 15/100 | 20/100 | 15/100 |
| Series A Analytics Tool | Data | 8/100 | 0/100 | 15/100 | 7/100 |
| Late-Stage EdTech LMS | EdTech | 0/100 | 5/100 | 10/100 | 5/100 |
The distribution was alarming but predictable:
- ●60% scored below 20/100. They are functionally non-existent in AI search.
- ●25% scored between 20-50. They appear occasionally, usually appended to a list lead by US competitors.
- ●15% scored above 50. These companies dominate AI mindshare and capture the baseline traffic.
Why did the top 15% win? It wasn't domain authority or backlink profiles.
The 3 Patterns of AI-Visible Brands
Pattern 1: Native Data Access (The MCP Advantage)
Companies that exposed their core data through structured APIs or, increasingly, Model Context Protocol (MCP) servers, appeared 4x more often. Claude and ChatGPT don't just want to read your marketing site; they want to query your live documentation, pricing, and capabilities. The Series C DevOps Platform in our test exposed a rudimentary schema that allowed Claude to definitively state its integration capabilities.
Pattern 2: High-Density, FAQ-Style Content
Long-form, narrative blog posts died with the AI era. The bootstrapped HR SaaS that scored 69/100 didn't have a massive marketing budget. What they had were hundreds of dense, aggressively structured pages answering highly specific questions ("How does [Feature] compare to [Competitor] for Indian labor laws?"). AI models ingest Q&A formats with near-perfect retention.
Pattern 3: Embedded Entity Relationships
Startups frequently cited alongside legacy competitors in authoritative technical publications (like GitHub discussions, StackOverflow, or dense technical tear-downs on standard engineering blogs) established strong "entity relationships." When the AI thinks of "Stripe alternative India," it immediately retrieves the FinTech startup because of semantic proximity in its training data, not because of SEO keywords.
What Makes a Brand AI-Visible?
The shift from SEO to AI visibility requires understanding three core concepts. This isn't theoretical; this is how search works in 2026.
Generative Engine Optimization (GEO)
The practice of formatting your brand's footprint so LLMs find you credible. Move away from keywords toward semantic facts, including `llms.txt` files.
Answer Engine Optimization (AEO)
Perplexity gives answers, not links. AEO means structuring your site data (JSON-LD) so engines can extract your exact pricing and features without parsing fluff.
Model Context Protocol (MCP)
The endgame. Let AI agents natively connect to your product in real-time, bypassing outdated training data altogether with Anthropic's open standard.
How to Check Your Own Score
Are you in the 15% that dominates, or the 60% that's invisible?
Don't guess. We built a tool to show you exactly how ChatGPT, Claude, and Perplexity perceive your brand today. A good score (50+) means you are successfully capturing zero-click AI search traffic. A poor score (<20) means every time a potential buyer asks an AI for a recommendation, your competitor gets the lead.
Conclusion: The Compounding Gap
The visibility gap between optimized and unoptimized brands is compounding much faster than the SEO gap ever did. In standard SEO, you could buy ads to bridge the gap while you built domain authority. In AI search, there are no ads to buy your way into a Claude response. You are either the authoritative answer, or you don't exist.
The Indian SaaS companies that optimize their architecture for AI visibility today will secure a 12-18 month head start on their competitors. The rest will wonder why their inbound pipeline suddenly dried up.