If Google can't crawl your app, and ChatGPT can't cite your docs, you're invisible in exactly the searches your buyers run. We fix the technical foundation underneath your growth.
The Problem
Your JS-heavy app and docs site barely crawl
AI companies build on React, Next.js, and component frameworks that render client-side. Google can render JavaScript, but slowly and inconsistently, and your most valuable pages are usually behind logins or built as single-page apps that never expose clean HTML. Your documentation, which is your richest source of long-tail intent, often ships as a docs framework that loads content dynamically and never gets fully indexed. The result is that the pages most likely to win developer and buyer searches are the ones the crawler sees as blank.
You rename features faster than search can keep up
AI categories move every quarter. You launch a feature, name it, then rename it when the market settles on different language. Each rename orphans the old URL, breaks inbound links, and resets whatever ranking authority that page had earned. Most AI companies have a graveyard of 404s and redirect chains from feature renames and rebrands, and search engines treat that churn as instability. You are constantly starting over on terms you should already own.
Answer engines don't cite you, which costs you twice
More of your buyers now ask ChatGPT, Perplexity, and Google's AI overviews instead of clicking ten blue links. If those systems don't pull and cite your content, you lose the click and you lose the credibility of being the named source. For an AI company this stings doubly: when a buyer asks an AI which vendor to trust and a competitor gets named instead of you, your own category's technology is recommending against you.
Speed and structure problems quietly suppress everything
Heavy model demos, unoptimized images, and bloated client bundles tank your Core Web Vitals. Missing or malformed structured data means search engines and answer engines can't reliably understand what your pages are. These aren't dramatic failures, so nobody flags them, but they sit underneath every page and cap how high anything can rank no matter how good the content is.
How We Help
Technical SEO for an AI company is not the same job as technical SEO for a brochure site. The hard problems are crawlability of JavaScript-heavy app and docs surfaces, an architecture that survives constant feature renames, and getting cited by the answer engines your buyers increasingly trust. We start by crawling your site the way Google actually does, rendering JavaScript, so we see the gap between what your team thinks is indexed and what search engines can really read.
The first fix is almost always rendering and crawl access. We get your key pages into a state search engines can read reliably, whether that means server-side rendering, static generation for your marketing and docs surfaces, or prerendering specific routes. We treat your documentation as a first-class SEO surface, not an afterthought, because docs capture the exact long-tail intent that developer and technical-buyer searches run on.
Next we build a site architecture that can absorb the way AI companies actually operate. You will rename features. You will reposition. So we design a URL and information structure where renames are a managed redirect, not a cliff, and where category authority accrues to durable hub pages instead of evaporating every time the product team picks a new name. Stable structure is what lets you compound ranking authority across a fast-moving category.
Then we go after answer-engine citation directly. Getting cited by an LLM or AI overview is its own discipline, and for an AI company it is not optional. We make your content extractable: clean semantic HTML, clear question-and-answer structure, structured data that machines parse without ambiguity, and authoritative source content that answer engines prefer to quote. This is the work that gets you named when a buyer asks an AI who to trust.
Underneath all of it we fix the boring, load-bearing things: Core Web Vitals, image and bundle weight, structured data, internal linking, and the redirect and canonical hygiene that keeps your authority from leaking. None of this is glamorous. All of it caps your ceiling if it's broken.
The Winston Francois difference is that we are operators, not a checklist agency. We don't hand your engineers a 200-row audit and disappear. We work alongside your team, prioritize the handful of fixes that actually move organic and citation visibility, and ship them with you. We tell you plainly when a finding doesn't matter, because a list of 200 issues where 190 are noise is how agencies bill hours, not how you grow.
We sequence the work so the highest-impact fixes, usually rendering access and the rename-proof architecture, land first, and we measure against organic visibility and answer-engine citation rather than a vanity audit score.
What we deliver
- Rendered-crawl audit – shows the real gap between what your team thinks is indexed and what search engines can read on your JS app and docs
- Rendering and indexation fixes – SSR, static generation, or prerendering for the marketing and documentation surfaces that matter
- Rename-proof site architecture – URL structure, hub pages, and redirect strategy that survive constant feature and category renaming
- Answer-engine citation setup – semantic structure, structured data, and extractable content that gets you cited by LLMs and AI overviews
- Core Web Vitals and structured data remediation – the performance and markup foundation that lifts every page's ceiling
Most AI companies treat technical SEO as a one-time audit, but the real failure is structural: a JS app search engines can't read, an architecture that breaks every time you rename a feature, and content answer engines won't cite. Fix the foundation and ranking compounds. Ignore it and great content sits invisible.
Our Methodology
We open with a rendered crawl, hitting your site the way Google's crawler actually does so we can compare what's indexable against what your team assumes is live. That almost always exposes app and docs pages that render blank to search engines. We map your URL history next, surfacing the 404s and redirect chains left behind by feature renames and rebrands, because that churn is usually quietly bleeding your authority.
From there we prioritize ruthlessly. We rank fixes by impact on organic visibility and answer-engine citation, not by how many rows we can put in a spreadsheet. Rendering access and a rename-proof architecture typically come first because they unblock everything downstream. Then we work through structured data, Core Web Vitals, internal linking, and citation-readiness in the order that compounds fastest.
Unlike agencies that deliver a static audit and bill for the diagnosis, we ship the fixes with your engineering team and measure against real outcomes. We stay close to your product roadmap so the next feature rename is a planned redirect instead of another reset, which is what keeps the gains from eroding in a category that never stops moving.
How We Work
The first few weeks are diagnosis and the highest-impact fixes. We run the rendered crawl, audit indexation across your app and docs, map the rename-and-redirect damage, and put your real technical foundation on the table. Then we sequence the work so rendering access and architecture land before cosmetic items.
We work embedded with your engineering and marketing teams rather than throwing a report over the wall. Technical SEO fixes live in your codebase and your CDN config, so we pair with your developers, write the tickets in your language, and stay in the loop through implementation instead of disappearing after the audit.
We keep tight feedback loops on what's actually getting indexed and cited. As pages become crawlable and structured data lands, we watch coverage, organic visibility, and whether answer engines start naming you, then redirect effort toward whatever is still capped. Engagements usually run a focused initial sprint to fix the foundation, followed by ongoing work as you ship new features and surfaces.
You keep final authority over what merges and ships. We handle the diagnosis, prioritization, implementation support, and measurement; your team owns code review and release. That split keeps velocity high without compromising your engineering standards.
- Improved crawlability and indexation of app and docs pages
- Reduced authority loss from feature-rename redirects
- Increased citation by AI answer engines
- Stronger Core Web Vitals and structured-data coverage
If your ai / machine learning company needs technical seo leadership, we should talk.
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Frequently asked questions
How much does technical SEO for an AI company cost?
Most technical SEO engagements for AI and ML companies run $10,000-$30,000 per month, depending on how large and complex your app and docs surfaces are and how much rendering and architecture work the foundation needs. An initial fix-the-foundation sprint sits at the higher end because rendering access and a rename-proof architecture are heavier lifts.
How long before we see results from technical SEO?
Indexation improvements show up fastest. Once previously-blank pages render and get crawled, you can see new coverage within a few weeks.
How does the work integrate with our engineering team?
Technical SEO fixes live in your codebase and infrastructure, so we work embedded with your engineers rather than handing off a report. We write tickets in your stack's language, pair on rendering and routing decisions, and stay in the loop through implementation and code review.
What makes Winston Francois different from a traditional SEO agency?
Most SEO agencies deliver a 200-row audit, bill for the diagnosis, and disappear before anything ships. We are operators who prioritize the handful of fixes that actually move organic and citation visibility, then implement them alongside your team.
How do you measure ROI from technical SEO?
We measure against outcomes that connect to pipeline, not vanity audit scores. The leading indicators are indexation coverage of your app and docs pages, organic visibility on your category and feature terms, and whether AI answer engines begin citing you by name.
Who is the right fit for this technical SEO service?
The best fit is a Series A through Growth AI or ML company, roughly $5M-$100M ARR, with a JS-heavy product and a real documentation surface, that is investing in organic and content but seeing it underperform. Companies that rename features often, have a backlog of redirects and 404s, or notice that answer engines cite competitors instead of them get the most value. We are not the right fit for a static brochure site with no app or docs to crawl, or for a team that wants a one-time audit handed off with no intention of shipping the fixes. The first step is a rendered crawl to see what's actually indexable.
