A b2b saas seo agency is a search partner that specialises in how software actually gets bought: long sales cycles, a buying committee instead of a single purchaser, and a narrow set of high-intent, bottom-of-funnel queries that convert far better than broad traffic ever will. It pairs technical SEO engineering with a documented content strategy built around the product, and, since 2026, with the work of getting your pages cited inside AI answers rather than only ranked in blue links.
That last part is why this job has changed. Ranking on Google and being quoted by ChatGPT, Perplexity or a Google AI Overview are now two different outcomes with different mechanics, and plenty of agencies still chase only the first. What follows is the playbook itself, including the parts that are commercially inconvenient for us to write down. It is close to the approach we run on our own site and for clients through XOVO's SEO service, set out so you can hold any agency, including this one, to it.
What is a content strategy
A content strategy is the set of decisions that determine what you publish, who it is for, why it deserves to exist when something already ranks for the same query, where it will be distributed, and how you will know whether it worked. It is a decision document. Everything downstream, including the calendar, the briefs and the word counts, is the output of those decisions rather than a substitute for them.
The reason the question "what is a content strategy" gets asked so often, and answered so badly, is that most of the published answers describe artefacts instead of decisions. They tell you a strategy contains personas, a keyword list, a calendar and a style guide. Those are deliverables. You can hold all four in a shared drive and still have no strategy, because none of them tells you what you will refuse to publish or what evidence you can bring that nobody else has.
A working content strategy answers five questions in writing, and the answers constrain each other.
The first is who the content is for, stated specifically enough to exclude people. "B2B marketers" is not an answer. "The security reviewer who has to sign off on a vendor before procurement will release budget" is an answer, because it tells a writer which objections the page has to clear and which it can ignore.
The second is what you can say that the competition cannot. This is the supply question, and it is the one most teams skip. Content quality is downstream of evidence access. If your organisation holds product telemetry, support ticket patterns, implementation logs, pricing data or the scar tissue of having shipped the thing forty times, that is your supply. If it holds none of those, your strategy has to include a plan for generating evidence, not just a plan for publishing words.
The third is which surfaces you are publishing for. A page written to rank on Google, a passage written to be quoted by an AI assistant, a LinkedIn post and a sales-enablement one-pager have different shapes. Deciding you are writing for two surfaces and not five is a strategic act, because it changes structure, length and how often you update.
The fourth is who reviews and who owns. In technical categories the reviewer is the constraint, not the writer. A strategy that commits to eight posts a month when the one engineer qualified to check them has four hours a month is not a strategy, it is a queue that will stall in week three.
The fifth is the measurement contract, agreed before anything is published. Which numbers count, at which stage, and over what period. Agreeing this in advance is the only defence against the quarterly conversation where someone asks why traffic is flat on a programme that was never built to move traffic.
A content calendar is not a content strategy
This distinction is worth making sharply because the conflation is so common. A calendar tells you that a post about invoice matching ships on the fourteenth. It does not tell you why that post exists, what it has that the three ranking pages lack, which internal pages it will link to, who will read it, or what happens to it in six months when the product changes.
The same applies to a keyword list. A spreadsheet of 400 terms with volume and difficulty columns is an input. Until somebody decides which 40 of them you will actually compete for, and accepts that the other 360 are being abandoned on purpose, no strategic decision has been made. The abandonment is the strategy. Choosing everything is the same as choosing nothing, and it produces sites with two hundred thin pages and no authority on any subject.
Brand voice guidelines sit in the same bucket. Useful, necessary, not a strategy. Voice determines how a page sounds once you have decided it should exist.
What has to be decided before anything gets written
In practice there is a short sequence that has to close before the first brief goes out, and running it takes days rather than weeks.
Decide the topic territory you intend to own, narrowly. Not "AI", not "automation", but something like "three-way matching and purchase-order exception handling for mid-market finance teams". A territory you can describe in one sentence and defend with evidence beats a category you can only describe with adjectives.
Decide the evidence you will spend. List, concretely, what your organisation can publish that a competitor cannot copy within a week. Aggregated product data, a teardown of a process you run, a benchmark from your own logs, a genuinely opinionated position on a contested method. If the list is empty, fix that first.
Decide the update policy. Every page gets an owner and a review interval at the moment it is commissioned, not after it decays. This one decision does more for long-term organic performance than most publishing-volume increases, and it costs nothing at commissioning time.
Decide distribution per page, before writing. A page with no distribution plan beyond "we will publish it and see" is a lottery ticket. Search is a distribution channel, but it is slow and it is not guaranteed, so each piece should have at least one non-search route to its first hundred readers.
Decide the kill criteria. What has to be true at 90 days and at 180 days for the page to stay as it is, get rewritten or get consolidated into another page. Sites that never consolidate accumulate cannibalised near-duplicates that compete with each other for the same query.
That is the whole of what a content strategy is: five decisions, written down, revisited quarterly. Everything else is production. Our content strategy and branding practice runs exactly this sequence before a single brief is written, because the alternative is paying writer rates to discover the decisions later.
Why B2B SaaS SEO is a different job from transactional SEO
Transactional search is a short loop. Somebody wants running shoes in a size, they search, they compare on price and delivery, they buy, and the whole thing can close in eleven minutes. The page's job is to be found and to reduce friction. Volume correlates with revenue reasonably well, because a large fraction of the people searching are capable of buying today.
A considered B2B software purchase breaks almost every assumption in that model. Nobody signs a forty thousand pound annual contract off a single blog visit. A finance lead, a security reviewer, an end user and a VP each touch the decision over weeks or months, and each one searches for something different along the way. The end user searches for the workflow. The security reviewer searches for your compliance posture and your data residency. The finance lead searches for pricing structure and for what happens at renewal. None of them are searching the same term, and the page that satisfies one of them will actively annoy another.
This has four consequences that change the work.
Volume stops being the right target. The queries that matter are usually low-volume and high-intent: "[competitor] alternative", "SOC 2 compliant [category] tool", "how to automate three-way invoice matching". A generalist agency chasing 50,000-volume head terms will report traffic that looks impressive in a slide deck and books no demos. The agencies that get results for software optimise for the searches a buyer runs when they already have a problem and a budget, even when those searches happen a few hundred times a month.
Attribution stops working the way the dashboard assumes. A long sales cycle means a page published in March might influence a deal that closes in September, so last-click attribution buries SEO's real contribution. Any serious search partner for software ties work back to a proper baseline in Google Search Console and GA4, then follows assisted conversions and pipeline influence rather than sessions alone.
Trust carries more weight than it does in transactional categories. A buyer evaluating software that will touch their revenue or their customer data reads the author line, checks whether the company has actually done the thing it is writing about, and notices immediately when a page reads as generic. Google's own guidance leans the same way, rewarding demonstrated first-hand experience. The content that earns both rankings and demos tends to be written or reviewed by someone who has shipped the product, not handed to a freelancer who has never opened it.
The conversion event moves. A lot of software now runs a self-serve or product-led motion where a free signup is the conversion and sales enters later. That pushes weight onto content that intercepts a specific problem, because the person searching is often a practitioner solving something at eleven at night, not a committee in a meeting. An article that walks through exactly how to do the thing your product does can earn the signup on the spot. The same logic governs the product surface itself, which is why we treat the marketing site and the application as one system in our B2B website development playbook.
Does higher search volume mean a better keyword for B2B software?
No, and for considered purchases the relationship frequently inverts. A 50,000-volume category term attracts students, job seekers, competitors doing research and consultants writing their own posts. A 260-volume query naming a specific integration and a specific failure mode attracts people who are mid-evaluation and have a budget line open.
The honest complication is that you cannot build a business on 260-volume terms alone, because there are not enough of them and they saturate. Head terms still do work: they build topical authority, they get you into the consideration set early, and they are what an AI assistant pulls from when somebody asks a broad definitional question. The decision is about sequencing. Buy or build the high-intent terms first, because they pay for the programme, then expand upwards into the volume once the money-making pages are ranking.
Content strategy, content marketing services or a content marketing agency: which do you need
Someone who has just worked out what is a content strategy usually discovers that the next question is harder: who should do it. These three phrases get used interchangeably by people selling them, which is why buyers end up paying for the wrong one. They are distinct purchases with distinct failure modes.
A content strategy engagement buys you decisions. It is short, senior and ends with a document: the territory, the evidence plan, the query map against the buying committee, the internal link architecture, the update policy and the measurement contract. Nobody writes an article. You come out knowing what to make and, more usefully, what not to make.
Content marketing services buy you execution against decisions that already exist. This is briefing, writing, editing, technical review coordination, publishing, schema, internal linking and reporting. Content marketing services are the right purchase when you or someone else has already made the five decisions and the bottleneck is throughput. They are the wrong purchase when you have not, because a services team will cheerfully produce forty competent pages that collectively do nothing, and it will not be their fault.
A content marketing agency retainer is the bundled version: strategy and execution under one accountable party, usually monthly, usually with a distribution and promotion component attached. A content marketing agency is worth the premium when the strategy needs to change in response to what the execution learns, which in software is most of the time. It is poor value when your requirements are stable and you mainly need volume, because you pay strategy rates for production work.
There is a fourth option that gets underrated. Hiring one strong in-house content lead and buying production capacity around them often beats both a pure services vendor and a full content marketing agency, particularly for technical products where evidence access is the constraint. An in-house lead can get a thirty-minute conversation with an engineer. An external agency, however good, usually cannot.
| What you are buying | You should buy it when | Who supplies the evidence | What you own afterwards | How it fails |
|---|---|---|---|---|
| Content strategy engagement | You are publishing but cannot say why each page exists | You do, in structured interviews | A decision document, query map and update policy | It sits in a drive because nobody owns execution |
| Content marketing services | The decisions are made and throughput is the bottleneck | You do, via briefs you supply | Published pages, schema and briefs in your own systems | Competent pages that collectively move nothing |
| Content marketing agency retainer | Strategy must adapt monthly to what execution learns | Shared, with the agency extracting it from your team | Pages plus the strategy, if the contract says so | You pay strategy rates for production volume |
| In-house lead plus bought capacity | Evidence lives with engineers who will not talk to outsiders | Your own staff, directly | Everything, plus institutional knowledge | Single point of failure when that person leaves |
The practical test for which one you need is a single question: can you write, in two sentences and without hedging, what your next ten pages have that the ranking pages do not? If yes, buy content marketing services and get moving. If no, buying execution will not help, and a content marketing agency that starts by selling you a volume commitment rather than that answer is selling you the wrong thing.
One caveat on ownership, because it costs people real money. Some agencies keep the schema, the reporting dashboards and the keyword research inside accounts they control, so leaving means losing your own history. Ask where every artefact lives before you sign, and get it in the contract. Our SEO engagements are deliberately built the other way, so that switching providers never costs you the work already done. The same clause decides an outbound engagement: a cold email agency that registers your sending domains inside its own account can hand you a dead programme on the day you leave.
The playbook, in the order it actually works
The order matters more than the list. Doing content before the technical layer is fixed is how teams pour money into pages that never get crawled cleanly.
Fix the technical foundation before writing a word
SEO is an engineering problem before it is a marketing one. We crawl the full site with Screaming Frog, reconcile what we find against Search Console coverage, and fix the technical layer first: render-blocking resources, Core Web Vitals, canonical and redirect logic, and interconnected JSON-LD schema graphs.
Software marketing sites carry specific traps. A JavaScript-heavy site can hide its content from crawlers that do not render JS. A blog stranded on a separate subdomain gives its authority away instead of feeding the product pages. Faceted integration or template pages spin up thousands of near-duplicate URLs that consume crawl budget and cannibalise each other. These get triaged before content, because a strong article on a page Google struggles to crawl is wasted work. Where we can get them we also pull server log files, since they show which URLs Googlebot and the AI crawlers actually spend budget on, which is rarely the pages you would hope. If the underlying build is the problem rather than the configuration, that is a different project, and our custom website development guide covers what rebuilding for crawlability actually involves.
Build topical clusters around jobs, not stray keywords
Once the foundation holds, content is modelled as pillar-and-cluster topics rather than a list of disconnected posts. Each cluster maps to a real job the buying committee is trying to do, and every supporting article links up to its pillar so relevance flows between them.
We brief each page against the pages already ranking, using Surfer SEO and Clearscope to set entity coverage and structure from live SERP data rather than a generic template. Internal links are planned into the brief before writing starts, not sprinkled on afterwards. This is where the abandonment decision from the content strategy earns its keep: a cluster of twelve deeply interlinked pages on one job outperforms sixty scattered posts across six jobs, and it is cheaper to maintain.
Let the product carry the content
The content that converts for software is rarely a generic listicle. It is the comparison page, the alternatives page, the integration page, the use-case page and the occasional free tool that solves a slice of the problem at no cost. These sit at the bottom of the funnel where intent is highest, and they are hard for a content mill to fake because they require actually understanding the product.
A payments company that publishes a real teardown of how chargeback disputes work will beat a thin listicle, because it can only be written by people who see the problem daily. Product-led pages are where a focused team pulls ahead of a generalist, and they are usually the shortest path from ranking to revenue. They are also the pages that need the update policy most, because they go stale the moment the product ships a release.
Earn authority instead of only publishing content
Content ranks faster when the domain behind it has earned trust, and for software that trust rarely comes from cheap link building. It comes from being referenced: original data pulled from your own product usage, a genuinely useful free tool, a founder who says something specific and falsifiable in a trade publication.
A benchmark built from anonymised data you already hold tends to earn more links than a year of guest posts, because other writers cite numbers they cannot get anywhere else. We track referring domains and anchor health in Ahrefs and Semrush, but the work that moves them is digital PR and material worth citing. This is slower than the technical fixes, and it is where a lot of agencies quietly do nothing, so ask exactly how an agency plans to earn links before you sign.
The 2026 layer: generative engine optimisation
A growing share of B2B research now starts inside an AI assistant, and those systems answer by quoting sources rather than handing over ten blue links. Generative engine optimisation is the practice of making your pages easy for those systems to parse, trust and quote. It is a genuinely different skill set from classic ranking, and our guide to generative engine optimisation goes deeper on the implementation detail than there is room for here.
The mechanics are concrete. Crawlers including GPTBot, ClaudeBot and PerplexityBot read raw server-rendered HTML and do not execute JavaScript; Gemini is the exception, because it borrows Googlebot's renderer. Content that only appears after a client-side render is invisible to most of them, however good it is. Interlinked JSON-LD schema served in the initial HTML gives those crawlers the entity structure they use to work out who you are and what you are authoritative about. Key passages get rewritten answer-first into self-contained chunks of roughly eighty to a hundred and fifty words, because that shape gets pulled into AI answers far more often than a snippet fragment or a rambling section does. A plain-text llms.txt file listing your key pages and claims is a cheap, low-risk addition on top.
There is a trade-off worth stating plainly. AI Overviews and assistant answers frequently resolve a query without the user clicking anything, so a page can influence a buyer without ever earning a session in your analytics. The practical response is to weight effort towards queries where the buyer still has to leave the answer and evaluate a real product, and to make sure your brand name and your specific product claims travel with any citation, so the mention does work even when the click never happens.
No agency can guarantee a place in a specific AI answer, and any that promises one is selling something. Carry that reflex into every channel where an outcome sits with someone else's systems; the deliverability questions to ask a cold email agency are built around exactly that gap. What honest practice looks like is shipping the mechanics that correlate with citation and then tracking brand mentions across the assistants over time.
Classic blue-link SEO compared with generative engine optimisation
| Dimension | Classic blue-link SEO | Generative engine optimisation |
|---|---|---|
| Goal | Rank in the top organic results | Get quoted inside the AI answer |
| Who reads the page | Googlebot, which renders JavaScript | GPTBot, ClaudeBot, PerplexityBot, which read raw HTML only |
| What wins | Backlinks, keyword coverage, click-through rate | Answer-first passages, schema, citable evidence |
| Ideal passage shape | Snippet-length fragments | Self-contained answers of 80 to 150 words |
| Primary metric | Rankings, impressions, clicks | Brand mentions and citations in AI answers |
| Effect of freshness | Helpful | Strongly correlated with being cited |
| Breadth strategy | Broad coverage compounds | Concentration outperforms breadth |
The point is not to choose. A single well-structured page can rank on Google and be quoted by Perplexity from the same HTML, which is exactly the target.
What our own AI-surface data says about concentration
This section is first-party XOVO data from our own Google Search Console property, and we are publishing it because we have not seen anyone else publish the equivalent.
Over the 111 days to 5 September 2026, our site recorded 1,947 AI-surface impressions across 55 pages. Roughly 85% of those impressions were concentrated in four URLs, which returned 692, 641, 200 and 118 impressions respectively. The remaining 51 pages shared what was left. Separately, our case study page shows the highest ratio of AI-surface impressions to total web impressions on the site, at 38%.
Two things follow from that distribution, and both contradict advice that is still being sold.
The first is that AI citation is not a breadth game. Fifty-one of our fifty-five appearing pages produced a rounding error between them. Publishing more pages did not spread citation around; it produced a long tail that barely registered. The four URLs that carried it are the ones with the deepest single-subject coverage and the clearest answer-first structure. If citation scaled with page count, the distribution would look far flatter than 85% in four URLs.
The second is that page type matters more than page volume. Our case study page has modest web impressions and yet is the single most AI-weighted page on the site at a 38% ratio. That page is specific, evidence-dense and reports an outcome with numbers and a method attached. It is exactly the shape an assistant can quote without hedging, and it earns disproportionate AI attention as a result.
There are limits to what we can claim from this, and we would rather state them than overreach. It is one site, one property, 111 days, and 1,947 impressions is a small absolute number. AI-surface impression reporting in Search Console is also still a young metric, so we treat the direction as informative and the precision as provisional. We are not claiming this generalises to every site. We are claiming it is real evidence, from a real property, that the concentration strategy is worth testing before the breadth strategy.
What we changed because of it: we stopped commissioning thin supporting posts to pad clusters, and we redirected that budget into deepening the pages that already appear in AI answers and into building more evidence-dense pages of the type that earns the highest ratio. The tactic that follows from this data is unglamorous. Write fewer pages. Make the ones you write carry original evidence, structured so a machine can lift a paragraph from them without losing the meaning.
What we would not do, even if you asked
Several tactics still get sold in this category and we decline them. Writing these down costs us work occasionally, which is roughly the point.
We do not build programmatic page sets with no unique value. Spinning up two thousand URLs from a template and a database of city names or job titles is trivially easy and it reliably produces a site that is mostly indexable filler. It sometimes works for a quarter. Then a core update arrives, or the crawl budget analysis shows Googlebot spending its time on templated pages instead of the pages that convert, and the clean-up costs more than the exercise earned. Programmatic pages are defensible when each one carries data that only exists at that URL. They are not defensible when the only thing changing between them is a noun.
We do not write comparison pages against competitors we have not used. Comparison and alternatives pages convert well, which is why so many of them are written by someone who read the competitor's pricing page for ten minutes and invented the rest. That produces claims we cannot substantiate, occasionally produces legal exposure, and is transparently obvious to any reader who has actually used the other product. If we write a comparison, someone on the team has run both. If nobody has, we write the use-case page instead and let the reader compare.
We do not accept volume targets set without a distribution plan. "Twelve posts a month" is not a goal, it is a cost. Before we agree to a publishing cadence we ask how the first hundred readers of each piece will arrive, given that organic will not deliver them for months. If the only answer is search, the cadence is wrong and the budget belongs in fewer, better pages.
We do not report vanity traffic as if it were performance. If sessions rose because a post about a general topic caught a news cycle, we say so and we say it is not pipeline. This makes some monthly reports less pleasant, and it is the only version of reporting that lets you make a good decision about next quarter's budget.
We also do not promise rankings on a date, or citation in a named AI assistant. Both depend on systems we do not control.
Measurement: what to track at each funnel stage
The measurement problem in this category is structural. The highest-intent queries are the lowest-volume ones, which means the pages that produce revenue produce numbers too small to look impressive on a chart, and the pages that produce impressive charts often produce nothing. Any measurement approach that does not confront that inversion directly will push you towards the wrong work.
Start by fixing the baseline. A surprising share of new engagements begin with an organic channel that is misconfigured in GA4, commonly with paid traffic misattributed as organic. Until that is clean, every comparison downstream is noise.
Then measure differently by stage.
At the top of the funnel, where somebody is learning that the problem has a name, the useful signals are impressions and average position on the target query set, plus scroll depth and returning visitors. Conversion rate at this stage is close to meaningless and should not be a target. The honest purpose of these pages is to be in the consideration set and to feed the AI surfaces, not to book demos.
In the middle, where somebody is comparing approaches, look at pages per session on the cluster, movement from a cluster page to a product or pricing page, and email or content-asset capture. This is the stage where internal linking does most of its work, and where a cluster that is well-linked internally visibly outperforms a set of orphan posts.
At the bottom, on comparison, alternatives, integration and pricing-adjacent pages, measure demo requests, trial signups and the assisted-conversion path. Expect low traffic and high rates. A page with 180 visits a month and an eight per cent demo rate is worth more than one with 18,000 visits and no conversion path, and your reporting should make that obvious rather than burying it in a sessions column.
Across all three, add two lagging measures that most reporting skips. First, pipeline influence: which deals in the CRM touched which pages, at any point, not just last click. Second, AI-surface presence: impressions from AI surfaces where Search Console reports them, plus manual or tracked checks on whether your brand and claims appear in assistant answers for your priority questions. The second is imperfect and worth doing anyway, because it is currently the only visibility into a channel that is taking share from the one everyone measures.
How long before B2B SaaS SEO shows results?
Longer than anyone selling it wants to admit. Rankings on target queries typically start moving in three to six months, and pipeline attribution usually lags by another two quarters because the sales cycle is what it is.
Two things stretch that timeline specifically for software. The sales cycle delays the payoff signal, and technical debt on JavaScript-heavy sites often has to be cleared before content ranks at all. The fastest movement almost always comes from bottom-of-funnel product pages, because intent is high and competition on those exact queries is frequently weaker than on head terms. Scope the technical baseline first so the slow part starts on day one rather than in month three.
How much should a B2B SaaS company spend on content?
The wrong way to answer is a percentage of revenue, because it tells you nothing about whether the work will land. The right way is to work backwards from evidence supply and review capacity. Count how many pages a month your subject-matter experts can genuinely review without the queue stalling, multiply by the fully-loaded cost of producing a page at the depth your category requires, and add distribution. That number is your floor. If it is higher than your budget, publish fewer pages rather than shallower ones, because shallow pages in a considered category cost money and return nothing.
What to look for in a b2b saas seo agency
Most of what separates a capable agency from an expensive one is visible before you sign. Ask to see the technical work, not only the published articles. Any team can show you a decent blog. Fewer can show you a Screaming Frog audit reconciled against Search Console, a schema implementation, or a before-and-after on Core Web Vitals. The teams that hold up under scrutiny lead with that engineering evidence, because it is the part that is hard to fake.
Concrete signals worth weighting:
- They ask for Search Console and GA4 access early, because they know they cannot baseline without it, and they check whether your organic channel is even tracked correctly before proposing anything.
- They talk about your buying committee and your bottom-of-funnel queries instead of head-term traffic volume.
- They can explain how they handle AI crawler access and server-rendered HTML, not just Googlebot.
- Their reporting ties specific changes shipped to impressions, position and pipeline, rather than a traffic chart with no causal claim in it.
- They tell you what they would not do, unprompted.
- You own everything at the end: the pages, the schema in your own CMS, the keyword maps in a workspace tied to your login, the tracking in your own properties.
Questions worth asking on the first call
A handful of direct questions separate an agency that has done this from one that has read about it.
Can you show me a technical audit you delivered, with the actual fixes and the ranking movement that followed? How would you handle a site where the content only renders after JavaScript runs? What do you baseline before you start, and how do you confirm our organic tracking is clean? How would you earn links for a product in our specific category, not in general? What would you refuse to publish for us? What exactly do we keep if we stop working with you?
If every answer comes back as "it depends", that is information. Teams that have shipped this work answer with specifics, because they have hit each of these problems on a live site.
How SEO, paid and product should connect
Search does not run in a vacuum, and treating it as a silo is a common way to waste budget. The queries you cannot rank for yet are exactly the ones worth buying in the meantime, which is why organic and paid search and social should share one keyword and intent map rather than compete over it. Paid data also tells you which bottom-of-funnel terms actually convert before you spend six months building content for them, which makes it the cheapest strategy research available.
The product side of the loop matters just as much. The pages that convert best describe workflows the product genuinely handles, which means the content roadmap and the product roadmap need to be in the same conversation. This is particularly acute for early-stage teams still defining scope, where the content strategy and the build plan are effectively one document; our notes on scoping an MVP in 90 days cover that overlap from the engineering side.
On the analysis side, our AI Growth Strategist reallocates budget across channels based on marginal return and surfaces which channel combinations are carrying a given segment. Feeding that channel data back into the content roadmap is what keeps the organic work pointed at revenue rather than at traffic for its own sake.
If you want a plain read on where your site stands across the technical layer, the content strategy and the AI-surface layer, book a free AI audit. We crawl the site, check how AI crawlers actually see it, compare your page distribution against the concentration pattern in our own first-party data, and tell you what is worth fixing first, whether or not you end up working with us.
