Orbit LabsHow Investor Signals went from 2% to 24% AI visibility in under four months
When founders ask AI engines where to get their pitch reviewed by active investors, Investor Signals now shows up. In under four months it climbed from roughly 2% to 24% visibility across six engines — rising from the floor of a six-brand tracked set to #3, overtaking three established players — while growing share of voice to 6% and holding positive sentiment.
+22 pts
#3 of 6, +3 spots
+2 pts, positive
"We had the investors and the product. What we didn't have was a presence the moment a founder opened ChatGPT and asked where to get their deck reviewed — we just weren't in those answers. Within a quarter that flipped. Founders started arriving already knowing who we were, and Orbit Labs is the reason we now show up where that search begins."
Investor Signals had built a high-trust offer — real 1:1 access to active investors — but as founders shifted to asking AI engines where to get their pitch reviewed, it was almost absent from those answers, sitting near the floor of its category at roughly 2% visibility. Orbit Labs ran a Three Gates diagnosis that pointed to retrieval as the bottleneck, built a buyer-intent prompt universe from real founder language, then executed an answer-first content and Citation Supply Chain program across owned, controlled and independent third-party sources — measured weekly against a locked baseline. Across the engagement, AI visibility rose from ~2% to 24%, share of voice from under 1% to 6%, it climbed from the floor of a six-brand tracked set to #3 (overtaking three established players), sentiment held positive at 72, and a founder pipeline filled underneath.
Invisible in the answers founders had started to trust first
For most of its growth, Investor Signals ran on reputation and warm intros — the way trusted, founder-facing services do. But a widening slice of founders no longer starts there. They open ChatGPT, Perplexity or Gemini and ask how to get real investor feedback on their deck, or which pitch-review service to use. In those answers, Investor Signals was almost absent: visibility across tracked founder prompts sat at roughly 2%, near the floor of its category, while a handful of names owned the conversation.
The gap wasn't only being found. It was being understood as a premium platform with active investors rather than a generic deck-feedback tool, and being recommended when a founder asked for options. If the engines can't do all three, a company is filtered out before the founder ever knows it exists — three distinct gates, each failing for a different reason. The job was to find which gate was losing the answer, and fix that one first.
A diagnostic-to-execution engine, measured against a locked baseline
Two frameworks anchor the work — the Three Gates of AI Visibility to locate exactly where a brand loses the answer, and the Citation Supply Chain to win it back. Operationally, that runs in five steps.
Find the gate that's losing the answer
We baseline against a fixed, ICP-injected conversational prompt set — not a score that drifts week to week — and map which founder prompts surface the brand, which competitors appear instead, and which sources the engines pull from. A technical pass checks crawlability, rendering and extractability. For Investor Signals the diagnosis pointed cleanly to Retrieval — the brand was rarely fetched into answers at all.
Build the map from real founder language
The prompt set is built from how founders actually talk when raising — their real questions, plus SEO keywords used as intent proxies — converted into conversational, buyer-intent prompts across the journey: awareness, consideration, decision. Branded "vs" prompts are deliberately excluded because they skew visibility scores. A gap analysis then prioritizes by commercial value and sequences the work.
Answer-first content, engineered to be cited
Every piece opens answer-first — the first two or three sentences directly answer the founder's prompt, then expand with evidence — with citation triggers built in (hard stats, clean declarative claims, named expertise), an FAQ section, and comparison / alternative pages where founders weigh options. Structured data (FAQPage, Article, Organization) is generated and the piece is published straight into the CMS.
The Citation Supply Chain
Recognition and recommendation are earned through citations, in order of how much an engine trusts the source: Owned → Controlled External (consistent listings and profiles so engines read one coherent entity) → Independent Third-Party (the hardest and most valuable — listicles, editorial, and the founder communities engines lean on). Community work is run with discipline: a single real, assigned voice answering genuinely, held to a ~9:1 contribution-to-self-reference ratio and kept entity-safe — never spam that could damage how AI understands the brand.
Visibility tracked, tied toward pipeline
Everything is tracked against the locked baseline from Step 1 — at the level of each piece, each topic, and the brand overall — so every lift is attributable, not anecdotal. The measurement model and the honest limits of attribution are detailed next.
AI visibility is the leading indicator. We're honest about the rest.
We treat AI visibility as the early signal and report it across three layers, from most controllable to most commercial:
Visibility signals
Mention rate, citation rate, share of voice, sentiment and average position on the topics the brand wants to own — tracked weekly against a locked baseline.
Traffic & clicks
AI-referred and organic traffic via Search Console and GA4, split branded vs non-branded, to separate genuinely new demand from people who already knew the name.
Self-reported attribution
The most reliable read: when a founder tells the team they found Investor Signals through an AI assistant. That's the signal we trust most.
AI search makes attribution harder than classic SEO. A founder often gets their answer inside ChatGPT or Claude without ever clicking, then arrives weeks later through a branded or direct search — so last-click reporting quietly credits "direct" for work AI did. We're upfront about that, which is why we lean on self-reported attribution and why every commitment we make is scoped to controllable output, never to a guaranteed outcome. Cadence: a weekly async update and a recurring review against the locked baseline.
"What I cared about was that they never overpromised. No guaranteed rankings — just clear work and weekly numbers I could check myself. For a brand built on founder trust, that mattered."
From the floor of the category to cited in one in four founder answers
Over the engagement, Investor Signals' visibility rose from roughly 2% to 24% — climbing from the floor of a six-brand tracked set to #3, overtaking three established players, and appearing in about one in four of the founder prompts it tracks — while share of voice grew from under 1% to 6% and sentiment held positive at 72.
Beneath the AI lift: organic search compounded and the pipeline filled
The same answer-first work compounded in classic search. Over the measurement window, Search Console climbed steadily on both clicks and impressions — and in the business itself, the founder pipeline filled alongside the program, with the platform's first signed clients now live as testimonials on its site.
The 68-deal pipeline is a leading indicator — founder deals in the CRM and self-reported AI discovery — reported alongside the program rather than as attributed revenue. Conversion is real and verifiable: the platform's first signed clients have testimonials live on investorsignals.co. We don't publish a revenue figure here, and every commitment Orbit Labs makes is scoped to controllable output — never a guaranteed ranking or revenue outcome.
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The first step is a 20–30 minute scoping conversation. We'll review your category, your competitors, and the prompts your buyers are using, then recommend the right scope.
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