How to Get Found in ChatGPT Search (And Why It Matters)
A real-world case study on how HashBuilds was discovered by a VC through ChatGPT search—and the specific optimizations that made it possible.
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The Story: How a VC Found Me Through ChatGPT
In December 2025, I received an inquiry from a venture capital firm. They were looking for a fractional CTO with specific expertise: Claude Code, Next.js, and rapid MVP development.
When I asked how they found me, they said: "We searched in ChatGPT."
Not Google. Not LinkedIn. Not a referral. ChatGPT search.
💡 Key Insight
This wasn't a random stroke of luck. I had accidentally optimized my website for LLM discoverability through specific content structures—and now I'm doing it intentionally.
The Search Query That Found Me
I recreated the search to understand what made me discoverable. Here's what ChatGPT showed when asked: "who is an expert on claude code as a fractional cto with nextjs"
What ChatGPT Extracted About Me:
- ✓HashBuilds — Claude Code expert / MVP dev (solo consultant) — specializes in Claude Code + Next.js MVPs and rapid development sprints; solo consultant who can work as embedded technical partner or fractional CTO.
- ⚠️Note: ChatGPT initially listed me as "agency/consultant" - a categorization error. This shows LLMs aren't perfect, but the important part is I was discoverable and cited.
- ✓Listed as #2 in shortlist (after an individual freelancer)
- ✓Cited with context: agency vs individual, team vs solo
- ✓Direct link to hashbuilds.com
Why LLM Search Matters for Your Business
Traditional SEO (Google)
- • Optimize for keywords
- • Build backlinks
- • Compete for rankings
- • Users click through to site
- • Conversion happens on your site
LLM Search (ChatGPT, Perplexity, Claude)
- • Optimize for factual content
- • Be cited as expert source
- • Get recommended in context
- • Trust built through AI endorsement
- • Higher-intent leads contact you
The key difference: Google ranks pages. ChatGPT reads your page, understands what you do, and recommends you in natural language. This creates pre-qualified leads who already trust you before they visit your site.
What Made HashBuilds Discoverable in ChatGPT
I analyzed my website to understand why ChatGPT cited me. Here's what I found:
1. Explicit Expertise Claims in Metadata
My homepage metadata explicitly states: "Claude Code expert", "fractional CTO", and "Next.js".
✅ Why it works: LLMs extract keywords from page metadata to understand expertise.
2. Clear Service Descriptions with Deliverables
I don't hide behind "Contact us to learn more." My services are explicit:
- •1-Day MVP: $1,500, 8 hours, rapid prototype
- •1-Week MVP: $7,500, 5 days, production-ready
- •Fractional CTO: Part-time technical leadership
✅ Why it works: LLMs can extract concrete facts (pricing, timelines) and present them in answers.
3. Structured Content (Headings, Lists, Sections)
My homepage has clear sections: Services, Capabilities, Portfolio, Testimonials. Each section uses semantic HTML with proper heading hierarchy.
✅ Why it works: LLMs are trained on well-structured content (Wikipedia, documentation). They can parse sections and extract relevant facts.
4. Concrete Examples and Portfolio
I show real projects with tech stacks, timelines, and outcomes. Not generic "We build great apps" but "Built X for Y using Z in N days."
✅ Why it works: LLMs prioritize content with specific evidence over vague marketing claims.
5. Technical Credibility Signals
GitHub activity, open-source contributions, detailed tech stack descriptions. LLMs look for proof of expertise.
✅ Why it works: LLMs assess authority by finding corroborating evidence across your site.
What I'm Adding Now (Intentional Optimization)
I got lucky with accidental optimization. Now I'm doing it systematically:
1. JSON-LD Structured Data
Adding schema.org Person and Organization markup so LLMs can extract facts more easily:
2. FAQ Section
LLMs are trained on Q&A format (Wikipedia, Stack Overflow). Adding an FAQ section makes content more "LLM-friendly" by providing direct answers to common questions.
3. Expanded About Page
Timeline of credentials, specific project outcomes, explicit expertise claims with evidence.
4. "How Did You Find Me?" Tracking
Adding this question to my client intake form to measure LLM search referrals. Unlike Google, there's no "ChatGPT Search Console" yet—tracking must be manual.
How to Optimize Your Site for LLM Search
Quick Win Checklist (30 minutes)
- ☐Update page metadata with explicit expertise keywords (what you do + who you serve)
- ☐Add concrete pricing/timelines to service pages (no "Contact us to learn more")
- ☐Structure content with headings (H2, H3 hierarchy) and lists
- ☐Test yourself: Ask ChatGPT "Who is an expert in [your niche]?" — Do you appear?
For B2B Services / Consultancies
High priority:
- • Add JSON-LD Person/Organization schema
- • Create FAQ section (5-10 common client questions)
- • Show explicit pricing or engagement models
- • List concrete deliverables and timelines
- • Add About page with credentials timeline
For Local Service Businesses
High priority:
- • Add JSON-LD LocalBusiness schema
- • Create service area pages with explicit coverage
- • Add "What to expect" sections (process, timeline, pricing)
- • Include emergency/availability information
For SaaS / Products
High priority:
- • Add JSON-LD Product/SoftwareApplication schema
- • Create detailed feature comparison pages
- • Add use case documentation (who it's for, what problems it solves)
- • Show transparent pricing tiers
How to Test If You're Discoverable
Test 1: Direct Expertise Query
Ask ChatGPT, Perplexity, or Claude:
Example: "Who is an expert in Claude Code for startup MVPs?"
Test 2: Problem-Solution Query
Ask what your ideal client would ask:
Example: "I need help building an MVP in 1 week. Who specializes in rapid development with AI?"
Test 3: Comparison Query
See if you appear in comparison contexts:
Example: "Compare fractional CTO services for AI-powered startups"
📊 Track Results Manually
There's no "ChatGPT Search Console" yet. Add "How did you find me?" to your intake forms and track referrals from chat.openai.com, perplexity.ai, and claude.ai in your analytics.
Why LLM-Generated Leads Are Higher Quality
Traditional Google Lead
- 1.User searches keyword
- 2.Clicks your link (maybe)
- 3.Reads your page (maybe)
- 4.Fills out form (maybe)
- 5.You qualify the lead
LLM Search Lead
- 1.User asks specific question
- 2.ChatGPT reads your page
- 3.ChatGPT recommends you with context
- 4.User trusts AI endorsement
- 5.Pre-qualified lead contacts you
The key difference: LLM search acts as a pre-qualification filter.
The VC who contacted me didn't need to evaluate 10 different consultants. ChatGPT did that work for them, extracted my credentials, understood my services, and recommended me with context. By the time they reached out, they were already convinced I was the right fit.
Resources & Next Steps
Long-Tail SEO Bolt-On
Our automated system for generating SEO content that works with both Google and LLM search.
Learn More →JSON-LD Setup Prompt
Add structured data to your site so LLMs can cite your services, pricing, and expertise accurately.
Want Help Optimizing Your Site for LLM Search?
I'm currently testing these strategies across HashBuilds and client projects. If you want to be an early adopter and get discoverable in ChatGPT, let's talk.
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