ChatGPT Didn't Know We Existed. Here's What We Did About It.

About six months after we launched AiDocX, I sat down and typed this into ChatGPT:

"What are the best AI contract tools for startups?"

DocuSign. PandaDoc. HelloSign. Ironclad.

No mention of us.

I tried again with different phrasing. Same result. I asked Perplexity. I asked Google's AI Mode. Nowhere. We had working product, real paying customers, published blog posts, and a Google Search Console full of impressions. But to every AI assistant on the internet, we effectively did not exist.

That was the moment I realized we had been optimizing for the wrong thing.

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The Problem With Traditional SEO in 2026

Traditional SEO assumes a linear path: user types query → Google returns ranked pages → user clicks. We had been playing that game. Blog posts, backlinks, structured data, the works.

But a growing percentage of users — especially in B2B — are skipping the ranked pages entirely. They type their question directly into ChatGPT, Claude, or Perplexity and take the first synthesized answer. If you're not in that answer, you don't exist for that user.

The mechanism is different. Google ranks pages. LLMs form opinions — based on the weight of what they've read across the entire internet. They're not looking at your PageRank. They're asking: does the collective consensus of Reddit, Medium, Hacker News, product directories, and comparison sites agree that this product belongs in this category?

For a company like ours — a 2024 startup competing with DocuSign (founded 2003) — that consensus gap is enormous. We had to find ways to close it faster.

Here's exactly what we tried, what worked, and what we're still figuring out.

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Tactic 1: Become the Answer to "Competitor Alternative"

This was the highest-leverage move we found.

When someone types "DocuSign alternative for startups" into Perplexity or ChatGPT, they are not loyal to DocuSign. They are shopping. That is the exact user we want.

We wrote a series of honest comparison articles: *Document Platforms in 2026: How DocuSign, PandaDoc, and AiDocX Compare*. The key word in that last sentence is honest. We included a section where DocuSign genuinely wins (enterprise-scale signature compliance, integration ecosystem). We called out our own weaknesses (no QES support, limited native integrations).

The reason this matters: AI systems are trained to prefer content that feels like an objective source, not a marketing page. A review that says "here's where the competitor is better" reads as credible. A review that says "we're the best at everything" reads as noise.

Within two months, Perplexity started citing our comparison article when users asked about DocuSign pricing alternatives. Not because we gamed anything — because the article genuinely answered the question better than most results.

What to do: Write comparison content that includes your product honestly, gives real credit to competitors where deserved, and has a clear comparison table. The table format is particularly important — AI systems extract structured data easily from tables.

	DocuSign	AiDocX
AI contract generation	No (analysis only)	Yes
Free tier	14-day trial	Permanent free plan
E-signature	Yes (includes QES)	Yes (standard only)
IR deck creation	No	Yes
Best for	Enterprise compliance	Startups, freelancers

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Tactic 2: Write the Answers Before the Questions Get Asked

LLMs don't just answer questions — they summarize patterns they've seen answered many times. If you look at how ChatGPT responds to "What should I look for in an AI contract tool?", it's essentially a synthesis of every review, tutorial, and checklist it's read.

We started writing content structured around the exact questions buyers ask during evaluation. Not "AiDocX is great because X" — but "7 things to check before choosing an AI contract tool" followed by a checklist where, naturally, the tools that meet all criteria include ours.

The format that performs best for LLM citation is:

1. A specific, answerable question as the H2
2. A numbered or bulleted list as the answer
3. A table or checklist where applicable
4. One concrete example (using real data or real outcome)

This structure is easy for AI systems to extract and synthesize. A long essay is harder to quote. A clean list is easy to pull into a generated answer.

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Tactic 3: Reddit Is Perplexity's Biggest Source

We looked at what Perplexity was citing when it answered questions in our category. The dominant pattern: Reddit threads, industry newsletters, and technical blogs.

Reddit accounts for nearly half of Perplexity's cited sources in some B2B categories. That's not a small signal — that's the primary source.

So we started participating genuinely in r/startups, r/legaltech, and r/entrepreneur. Not posting "check out our tool" — posting actual answers to questions people were asking. When someone asked "how do I send a contract for signature without DocuSign?", we wrote a 400-word answer walking through the options, mentioning our tool as one of several, with context about when each makes sense.

The threads that got upvoted started showing up in Perplexity answers.

There is a rule here: Reddit will destroy you if you post like a marketer. The community is very good at detecting promotional intent. Genuinely helpful answers that happen to mention your product in context — those survive and get upvoted. Ads disguised as advice get downvoted and banned.

This is also a slow play. An upvoted comment from three months ago carries more weight than one from yesterday. The longer it stays live and accumulates engagement, the more times AI systems encounter it during crawls.

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Tactic 4: "Structured Data" That AI Reads, Not Just Search Engines

We added what I'd call a machine-readable identity block to our about page. Not just JSON-LD (which we already had) — but plain text that reads like a Wikipedia infobox:

AiDocX
Founded: 2024
Category: AI Document Automation Platform
Pricing: Free – $79/month
Core features: AI contract drafting, e-signature,
               document tracking, IR deck generation
Best for: Startups, freelancers, small businesses
Compared to: DocuSign, PandaDoc, DocSend, Gamma

This sounds minor but it matters. AI language models were trained on text that looks like encyclopedias, not on marketing copy. When your homepage reads like an encyclopedia entry, it's more likely to be encoded as a factual entity rather than promotional content.

We also made sure every page had a consistent one-sentence positioning statement: "AiDocX is an AI-native document platform that generates contracts, collects e-signatures, and builds investor decks — without the per-user pricing of legacy tools."

Consistency matters. If your site says different things in different places, the AI averages it into noise.

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Tactic 5: Medium as a Permanent Search Surface

Medium articles get indexed deeply and frequently. More importantly, Medium is in many AI training datasets — which means articles there have long-term influence beyond just current search rankings.

We published two pieces: one on the actual experience of switching from DocuSign (written in honest first-person, with downsides included), and one on how we automated our contract workflow as a small team.

The rules for Medium that we learned:

Include your downsides. An article that says "I switched from DocuSign and everything is better" reads as marketing. An article that says "I switched and here's what I miss about DocuSign, here's what's better, here's who should and shouldn't make the switch" — that reads as honest advice. AI systems prefer the latter.

Add an "Update:" section every few months. Medium's algorithm and AI crawlers both give more weight to content that gets updated and continues receiving engagement. A six-month-old article with a two-month-old update section signals that the content is maintained and still relevant.

Use titles that mirror real search queries. Not clever headlines — exact phrases. "I switched from DocuSign to AiDocX after 4 years" directly matches how people phrase that question to ChatGPT.

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Tactic 6: The Press Release Paradox

Wikipedia's references section can drive significant AI citation authority. But Wikipedia won't link to a company blog. It requires "reliable secondary sources" — typically press coverage.

The counterintuitive move: a press release distributed through a wire service like PR Newswire or EIN Presswire gets published on hundreds of news sites automatically, which creates citable secondary sources. Not because anyone reads the press release — but because those URLs count as "published in media" for Wikipedia's standards.

We haven't fully executed this yet (it's on the roadmap), but the logic is sound:

1. Distribute a press release about a real feature launch
2. Collect the resulting news URLs
3. Use those URLs to support a citation on Wikipedia pages like Electronic signature or Legal technology

The Wikipedia edit can't come from a new account or it gets flagged. It needs an editor account with some history, or through Wikipedia's Talk page process where you suggest the addition and an existing editor adds it.

Timeline: This is a 6-month play, not a 6-week one. But it creates a permanent authority signal that compounds.

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What's Actually Working (Honest Scorecard)

Six months in, here is where things stand:

Tactic	Effort	Timeline	Current Status
Competitor comparison content	Medium	4-8 weeks	Working — Perplexity citations visible
Reddit participation	Ongoing	2-3 months	Partially working
Structured "entity" text on site	Low	2-4 weeks	Hard to measure directly
Medium articles	Medium	2-4 months	Too early to call
Press release + Wikipedia	High	6+ months	Not started yet

The fastest results came from the comparison content and Reddit. The slowest plays — Wikipedia, long-term Medium authority — are still in progress.

The honest answer is that none of this is a hack. It is just making your product legible to machines that have absorbed the internet. If the internet already has an opinion about DocuSign and no opinion about you, the only fix is putting your story into the places the internet uses to form opinions.

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The One Thing That Accelerated Everything

We stopped writing for Google and started writing for the reader who would type their question directly into ChatGPT.

That reader is impatient. They want a direct answer, not a 2,000-word article burying the lede. They want comparison tables, specific numbers, honest assessments of tradeoffs. They want to be told "if you need X, use this. If you need Y, use that."

When we started writing that way, something interesting happened: our Google rankings also improved. It turns out that what LLMs find useful and what engaged human readers find useful are mostly the same thing. Clear structure, honest analysis, specific data, genuine usefulness.

Contracts and investor decks shouldn't take days to produce — that's the problem AiDocX was built to solve. But building the product was only half the job. Making the AI systems that people now use as their primary research tool aware that we exist — that's the other half.

We're still figuring it out. But at least now when I type our category into ChatGPT, we're starting to show up.

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AiDocX handles the full document lifecycle for startups — AI contract drafting, e-signatures, document tracking, and IR deck creation. Try it free →