SAN FRANCISCO | PALO ALTO | MOUNTAIN VIEW | SAN JOSE | OAKLAND
Nowhere on earth has a higher concentration of buyers who open ChatGPT before they open Google. If your category gets shortlisted inside an AI answer and your company is not named, you lose the deal before the evaluation starts. We do the technical and content work that gets you into both the rankings and the answers.
IN SHORT
SEO TYT provides SEO services to San Francisco and Bay Area companies, with a focus on B2B SaaS, developer tools, fintech and AI companies, alongside local Bay Area businesses. We handle technical SEO, content strategy, and visibility inside AI answers from ChatGPT, Gemini and Perplexity. Our team works from Karachi, twelve hours ahead of Pacific time.
THE LOCAL PICTURE
National figures, then why this market is ahead of all of them.
of US adults under 50 use ChatGPT, against 28% of those over 50 (Pew Research, June 2026)
Those are national numbers. The Bay Area is at the far end of every one of them. Under fifties use ChatGPT at roughly twice the rate of over fifties nationally, and this is a market where the working population is overwhelmingly under fifty, technically literate and employed at companies that encourage AI tool use. Thirty eight percent of employed Americans already use chatbots for work tasks. In a San Francisco software company the real figure is far higher.
For a B2B company selling here, that reshapes the top of the funnel completely. The buyer who used to search for best tools in a category, open six tabs and compare, now asks an assistant and gets three recommendations with a short justification for each. Your chance to be discovered during the comparison has been removed, because the comparison already happened without you.
There is a second Bay Area pattern worth naming. Sixty eight percent of searches now end with no click at all, and technical audiences are especially likely to get what they need from a summary. Traffic to B2B content sites has fallen across the board while the underlying demand has not changed. A lot of Bay Area marketing teams are reading that traffic decline as a content quality problem when it is a distribution problem.
The Bay Area buying process for software has been content led for a decade, but the content that decides it is increasingly not yours. Comparison posts, listicles, community threads and review sites feed both Google and the language models, and those sources decide which three names come back when a buyer asks for recommendations.
Most Series A and B companies we look at have the same profile: they rank well for their own brand name, they have a blog that produces steady output and almost no non brand traffic, their documentation outranks their marketing site, and they are absent from every best tools list in their category. None of that is a writing problem. It is a structural one.
The local market has its own version of this. San Francisco small businesses face some of the highest costs in the country and a customer base that researches everything, which makes organic visibility disproportionately valuable when paid acquisition is this expensive.
COVERAGE
San Francisco proper plus the wider Bay, which behaves as one talent and customer market and several distinct local ones.
San FranciscoSoMaFinancial DistrictMission DistrictMarinaPalo AltoMountain ViewSunnyvaleSan JoseSanta ClaraMenlo ParkRedwood CityOaklandBerkeleyFremontSan Mateo
For a software company selling nationally or globally, the physical location barely matters and we treat the work accordingly. For a Bay Area business serving local customers, it matters a great deal and we handle it as local search.
SOUND FAMILIAR?
These are the patterns we see repeatedly in this market.
The most consequential problem on this list and the least measured. There is no analytics event for being left out of a recommendation, so companies discover it only when they think to check, usually after a quarter of soft pipeline.
WHAT WE DO
Baseline where you currently appear across the main platforms, then do the structural work that gets you cited: clear factual positioning, schema, comparison content, and presence on the third party sources the models draw from.
Extremely common at Series A and B. Brand search converts well so the dashboards look acceptable, but every one of those visitors already knew you existed. There is no acquisition happening, only capture.
WHAT WE DO
Build for the problem the buyer searches before they know your name, which usually means comparison, alternative and use case content rather than more thought leadership.
A very 2025 problem. Volume without depth, no internal structure, no differentiated information, and frequently cannibalising each other across near identical topics. The library looks substantial and performs like nothing.
WHAT WE DO
Audit what exists, consolidate the overlapping pieces, cut the ones with no purpose, and rebuild the remainder around genuine topic clusters with something in them a model would want to quote.
Typical of developer tools. Documentation earns links and answers real questions while the marketing site talks about outcomes in abstract language nobody searches for.
WHAT WE DO
Treat that as an asset rather than a problem: connect the docs into the site structure deliberately, and rebuild the marketing pages around the specific problems the docs imply people are trying to solve.
Bay Area agency pricing reflects Bay Area salaries. For a company that has raised a seed or Series A and is watching runway, it is often simply out of reach, so nothing gets done at all.
WHAT WE DO
Price against the work rather than the market, and run rolling monthly so a company under budget pressure is not locked into a commitment it regrets.
Those lists drive a large share of category discovery and they now feed the language models as well, so absence from them costs you twice. Most are maintained by people who can be contacted.
WHAT WE DO
Identify the lists that actually matter in your category, work out what gets a product added, and pursue them systematically rather than hoping.
WHAT WE WORK ON
◉
Crawlability, indexation, Core Web Vitals, JavaScript rendering and the issues that specifically affect React and Next.js marketing sites, which is most of them here.
◉
Comparison, alternative, use case and problem led content aimed at buyers who do not yet know your name, mapped so pages do not compete with each other.
◉
Baselining where you appear across ChatGPT, Gemini, Perplexity and AI Overviews, then the schema, positioning and third party presence work that gets you named. Tracked monthly.
◉
For businesses serving local customers rather than a global market: Google Business Profile, proximity signals and review systems.
For the full detail on our technical, content and AI visibility work, see our SEO services page. This page covers what changes in San Francisco specifically.
AI VISIBILITY
In most markets AI visibility is a sensible thing to start on. In this one it is already deciding deals. Your buyers are the earliest adopters of these tools anywhere, they are using them at work with their employers' encouragement, and a large share of category research now happens inside a conversation rather than on a results page.
The mechanism is worth understanding because it changes the work. Language models do not rank pages, they assemble answers from sources they consider reliable, weighted heavily towards third party corroboration. What gets a product named is being described consistently across the web, in clear factual language, on sources the model already trusts, with unambiguous information about what the product does and who it is for.
That means a meaningful part of this work happens off your own site: review platforms, comparison sites, community discussion, category lists and documentation. It also means vague positioning is actively harmful, because a model cannot recommend a product it cannot categorise.
We baseline your presence across each platform using the questions your buyers actually ask, then report monthly on whether you appear, which competitors do, and which sources are being cited. In a market this AI literate, that report tends to be the thing leadership actually reads.
BAY AREA INDUSTRIES
The economics and buying behaviour differ sharply between these.
Long cycles, committee decisions, and discovery that now happens largely through comparison content and AI recommendations.
Technical audiences who distrust marketing language, where documentation quality and community presence outrank conventional content.
The most crowded positioning space in the market, where being clearly categorisable matters more than being clever.
Regulated, trust sensitive, and heavily dependent on credibility signals that both Google and the models weigh.
Recruiting, legal and consulting firms serving the tech sector, competing on credentials and referral adjacency.
Restaurants, clinics, home services and studios facing high costs and customers who research everything before booking.
HOW WE WORK
Karachi is twelve to thirteen hours ahead of Pacific time. That is close to a full inversion, which sounds like a problem and in practice is the cleanest handoff of any offshore arrangement. We work through your night, so what you raise at the end of Monday is generally done when you open your laptop on Tuesday.
Calls run at nine in the morning Pacific, which is nine or ten at night here. We hold that slot deliberately rather than asking clients to take early calls, and most Bay Area clients settle into one call a month with asynchronous communication in between, which suits how software teams already work.
On cost, Bay Area agency pricing reflects Bay Area salaries. A seed or Series A company watching runway frequently cannot justify fifteen thousand a month, and ends up doing nothing at all. Our pricing reflects Pakistani salaries, and the work is identical because all of it happens in the same tools.
The limitation is that we are not in the room. We cannot sit in your Monday standup or walk to your office, and we will not register a San Francisco address for a Google Business Profile at a location we do not occupy.
Rolling monthly terms, invoicing in US dollars, and every account we work in created under your own email so you own it outright.
OUR PROCESS
1
Technical audit including JavaScript rendering, content inventory, non brand ranking assessment, competitor analysis, and a recorded baseline of where you appear across the AI platforms.
2
Technical issues cleared, schema implemented, overlapping content consolidated or cut, positioning made unambiguous for both crawlers and models.
3
Comparison and use case content published, third party source work under way, and the internal structure that turns individual pages into topic authority.
4
Monthly reporting on non brand traffic, pipeline attribution where your stack allows it, and AI citation tracking across every platform.
Four things, roughly in order of impact. Be unambiguously categorisable, which means describing what the product is in plain language rather than in positioning abstractions. Have schema and structured data that state it explicitly. Be present on the third party sources these models draw from, which means review platforms, category lists, comparison sites and community discussion. And have depth on your own site that a model can quote without editing.
What does not work is anything sold as submitting your site to ChatGPT, because no such mechanism exists. If a vendor offers it, that tells you what you need to know about the rest of their offering.
It can, and it is the most common technical issue we find in this market. Client side rendered content may be crawled inconsistently, hydration errors can hide content entirely, and route handling frequently produces indexation problems that nobody notices for months.
Next.js with server side rendering or static generation mostly solves it if configured correctly, but correctly is doing a lot of work in that sentence. The audit checks what Google actually renders against what a user sees, which is usually where the surprises are.
It depends on your sales motion. If buying happens through outbound and founder networks, spend the money there first. If buyers in your category research independently before they ever speak to a vendor, which is most software categories, then starting early matters because authority compounds and cannot be bought quickly later.
For an early stage company we would usually recommend a narrow, cheap scope: get the technical foundation right, make the positioning machine readable, and build a small number of genuinely useful pages. That is a fraction of a full retainer and it is the part that compounds.
It is an asset that is usually being wasted. Documentation earns links and answers concrete questions, which is exactly what both search engines and language models reward. The marketing site talks about outcomes in language nobody searches for.
The fix is connecting them deliberately rather than suppressing the docs: internal linking that passes authority in both directions, and marketing pages rebuilt around the specific problems the documentation shows people are trying to solve.
Audit it honestly. Most large AI generated libraries contain three categories: pages with a real purpose that need substantial editing, near duplicates competing with each other, and pages that should never have existed. The last group is usually the largest.
Consolidate the duplicates, cut the rest, and rebuild the survivors with information a model would actually want to cite: specifics, data, opinions, things that are not available in every other article on the topic. Volume is not the problem, interchangeability is.
A fixed set of prompts reflecting the questions your buyers ask, run across ChatGPT, Gemini, Perplexity and AI Overviews every month, with the results recorded: whether you were named, which competitors were, what sources were cited. The same prompts each time so the comparison is valid.
Alongside that, referral traffic from AI platforms in your analytics, and branded search volume, which tends to rise when people encounter your name in an answer before searching for you. None of the three is precise on its own. Read together they are honest.
Yes, and it is worth doing deliberately because those lists now feed the models as well as driving direct traffic. Most are maintained by publications, communities or individuals who can be contacted, and most have criteria, whether stated or implied.
The work is identifying which lists actually influence your category, understanding what gets a product included, and pursuing them properly. It is slow and it is not guaranteed, and any agency promising placements on a timeline is describing something paid rather than earned.
Better than partial overlap does, in our experience. A near total inversion means clean handoffs rather than constant interruption: you write it up at the end of your day, we work on it overnight, it is waiting in the morning.
Calls are at nine in the morning Pacific, which is late evening for us, and we hold that slot so nobody is taking meetings at an unreasonable hour. Most Bay Area clients run one call a month and everything else asynchronously, which is how their own teams already operate.
We will run the questions your buyers ask through ChatGPT, Gemini and Perplexity, check your technical health and non brand rankings, and show you which competitors are appearing instead. You get the findings whether or not you work with us.
