Category creation. Evaluator-driven enterprise deals. Pricing under capability uncertainty. A competitive set that resets every model release. The strategic motion for an AI company has its own physics.
Built for Applied AI CEOs at Series A. Applies to founders of foundation model companies, AI infrastructure companies, and vertical AI SaaS from Seed through Series C where commercial motion has outgrown founder-led sales.
Category narrative. Product narrative. ICP architecture by buyer role. Sales motion. 90-day priorities.
Read the scope →SF Marketing Agency partners with AI companies - applied AI, foundation model, AI infrastructure, and vertical AI SaaS - on strategic marketing and go-to-market. The core engagement addresses four commercial constraints specific to AI: category creation in an undefined reference frame, enterprise evaluator-driven sales cycles, pricing under capability uncertainty, and differentiation that survives model-release cycles. Entry is through the $7,500 Positioning and GTM Sprint delivered in 14 business days.
The vertical matters because the buying committee, risk language, proof standard, sales cycle, and trigger event change by category. The strategy has to reflect that reality before channels or creative are chosen.
The page identifies the real decision participants: economic buyer, evaluator, champion, operator, or referral source.
Every market has a different perceived risk: budget waste, operational failure, compliance exposure, partner credibility, or reputation.
The strategy defines which proof the buyer needs before action: numbers, process, clinical depth, technical capability, or commercial outcomes.
The page routes into the right first engagement instead of forcing a generic service conversation.
The strategic methodology holds across AI sub-categories. The specific commercial tactics adapt to whether you are creating a category, selling into an existing one, or rebuilding an incumbent category with AI-first architecture.
Companies solving specific workflow problems with AI as the core mechanism. The category creation problem is sharpest here.
Companies building or fine-tuning base models for specific deployments. The commercial challenge is differentiation that survives the next release cycle.
Tooling, MLOps, observability, evaluation, safety, guardrails. Selling to a technically sophisticated buyer who evaluates critically.
Incumbent SaaS categories being rebuilt with AI as the core differentiator. Competing against entrenched non-AI players with a better mousetrap.
Every AI company we partner with is contending with some combination of these four. Ignoring them is the most common strategic error we see in this vertical.
The category you are selling into did not have a shelf label 18 months ago. Your buyer is forced to evaluate your product against a mental model borrowed from an adjacent incumbent category. This breaks feature-based positioning and demands category-narrative work first.
Security review, compliance review, hallucination review, and model-behavior review happen before feature evaluation. Traditional SaaS marketing addresses the champion. AI marketing has to address the silent evaluator stack that can veto the deal at month four of a six-month cycle.
The model your customer signed a 12-month contract against will be obsolete in 4 months. Annual pricing that made sense in SaaS breaks in AI. The commercial architecture has to account for capability drift - usage-indexed, outcome-indexed, or shortened contract cycles.
Your capability advantage last quarter may be commodity this quarter. Durable differentiation cannot live in the model layer alone. It has to live in workflow integration, data proprietary loops, and customer-adjacent assets that compound independently of model progress.
The first principle in AI company marketing is sequencing. Most GTM problems we encounter are not about what the company is saying. They are about the order in which the company is saying it.
Product-first messaging fails because the buyer has no category frame to place the product in. The product sounds like an expensive version of the adjacent incumbent. Category-first messaging works because it repositions the buyer before the buyer evaluates the product.
Every AI GTM problem we have been hired to solve was, underneath, a sequencing problem. The company had all the right assets in the wrong order.
The second principle is evaluator-first marketing. In enterprise AI, the deal dies quietly in security, compliance, and IT review - usually after the champion has fallen in love with the product. A GTM that surfaces security and compliance artifacts as top-of-funnel assets, not end-of-funnel documents, kills the wrong deals faster and compounds the right ones.
The third principle is moat architecture. We do not build marketing that celebrates current capability, because current capability evaporates. We build marketing that celebrates workflow depth, data loops, customer compounding, and ecosystem position - things that survive the next release cycle.
The product legitimately worked and the champion always loved it. Deals stalled at month four of a six-month enterprise cycle. CAC payback had blown past 24 months. The GTM rebuild front-loaded the evaluation stack - SOC 2, model card, data handling architecture - as top-of-funnel assets.
The next quarter, deals still died in security. But they died at week six, not month four. The pipeline compounded because the team stopped burning six months on non-closing deals. CAC payback dropped 47% in 90 days with no change to product or ICP.
The company was building AI for contract workflow, but every sales call devolved into a comparison with document-automation incumbents. The buyer priced the product as if it were a better version of an incumbent, which capped willingness-to-pay at incumbent ceilings.
The Positioning Sprint rebuilt the category narrative. The buyer was guided into a new frame (contract intelligence, not document automation) through sequenced content, pricing architecture, and sales motion. Qualified pipeline doubled. ACV increased 40% as buyers stopped comparing to incumbents.
The company had AI-first architecture in a category dominated by 15-year-old SaaS players. Sales cycles were exhausting because the buyer had to unlearn the incumbent framework before evaluating the new product. Every deal was a greenfield education.
The GTM rebuild positioned the product not as a replacement but as a parallel layer - initially coexisting with the incumbent, gradually displacing it. This shortened deal cycles by removing the rip-and-replace cognitive load. Enterprise cycles compressed 38%.
The core gate for AI companies. Category narrative, product narrative, ICP architecture by buyer role, sales motion definition, and 90-day execution priorities.
Read the scope →For AI companies where the problem is broader than positioning - full four-axis strategy covering positioning, packaging, GTM, and demand generation.
Read the scope →Post-sprint partnership for AI companies that want ongoing strategic oversight as the model, market, and competitive landscape continue to shift.
Read the scope →Note on brand building depth: For AI companies whose core challenge is brand identity rather than full commercial motion, brand engagements are handled directly by Stan Consulting LLC. The Brand Archive is the research reference within the network. It contains source-cited case studies on branding decisions and their consequences, useful when reviewing how comparable category-creation moves have played out.
Applied AI and vertical AI companies from Seed to Series C with $500K-$20M ARR. Foundation model companies and AI infrastructure companies at any stage where the commercial motion has outgrown founder-led sales. Not yet-validated research spinouts or pre-product-market-fit exploration.
Four sub-categories. Applied AI: companies solving specific workflow problems with AI (legal, medical, sales, finance verticals). Foundation model companies: those building or fine-tuning base models. AI infrastructure: tooling, MLOps, observability, evaluation. Vertical AI SaaS: incumbent SaaS categories being rebuilt with AI as the core differentiator.
Three recurring patterns. Positioning collapses under an undefined category (the buyer has no reference frame). Enterprise deals stall in security and compliance review despite strong practitioner signal. Pricing erodes because model capability improves faster than contract length. The engagement addresses the underlying category creation work, then the mechanics follow.
The Positioning and GTM Sprint at $7,500, delivered in 14 business days. The sprint produces category narrative, product narrative, ICP architecture by buyer role, sales motion definition, and 90-day execution priorities. From there, three paths: your team executes in-house, scoped execution project ($10K-$75K), or Quarterly Strategy Partnership ($4,500/mo).
The firm works with AI companies across foundation models, applied AI, and AI infrastructure in the US, Canada, and Europe. Representative cases include a Series A AI infrastructure company where CAC payback was reduced 47% in 90 days, an applied AI company in legal tech whose pipeline doubled through evaluator-first marketing, and a vertical AI SaaS company whose category narrative accelerated enterprise deal cycles by 38%.
Standard for applied AI. The work identifies the mental model your buyer uses (usually an adjacent incumbent category they borrow from), positions against that reference frame, then migrates the buyer to the new category over time. Sequenced category creation, not a single positioning statement. Most AI GTM errors come from trying to invent a category in the first quarter rather than migrating into one over twelve months.
If you want a senior strategist to review your commercial direction before committing to a full engagement, the founder at stantscherenkow.com works directly with a small number of operators.
Positioning & GTM Sprint for AI companies. $7,500 flat. 14 business days. Category narrative, product narrative, buyer-role ICP architecture, 90-day execution sequencing.