GTM Agency for AI Companies

Where AI GTM breaks

What we build for AI companies

AI GTM FAQs

What makes AI company GTM different from other B2B GTM?

AI GTM is defined by a category-education problem that does not exist in mature software markets. Your buyer is usually being asked to approve a product whose category did not exist 18 months ago, whose ROI cannot be benchmarked against industry norms, and whose procurement path runs through legal, security, and a newly-formed AI governance committee. That changes everything upstream: messaging has to teach before it sells, outbound has to qualify for innovation appetite rather than pain severity, and content has to build category language that your buyer can use internally to justify the purchase. We build AI GTM systems assuming the market is still forming and the buyer is still deciding whether the category is real, not just whether your product is the best option in it.

How do you explain novel AI products to buyers who have never seen them before?

By anchoring every explanation in a job the buyer already recognises and quantifies. We never lead with model architecture, training data, or capability demos. We lead with the workflow the buyer already owns — claims triage, contract review, tier-1 support, code review — and show where the AI changes the cost, speed, or quality of that workflow. Every message, landing page, and sales deck we build for AI companies follows the same spine: what the buyer does today, what breaks about it, what the AI changes, what the buyer has to trust for it to work. Category education and product pitch collapse into a single story told in the buyer's language, not the vendor's.

What is a realistic AI startup GTM strategy at seed and Series A?

Design partners first, repeatable motion second, scale third. At seed, your GTM job is to find five to ten design partners who will co-build the product with you in exchange for heavy discounts and deep access. That is founder-led sales, not outsourced SDR work. At Series A, once you have early product-market fit signal, the goal shifts to finding the narrowest buyer segment where the product already sells without heavy customisation, then building one repeatable outbound motion and one compounding content motion against that segment. We help Series A AI companies through our fractional VP of sales and outbound sales agency services rather than dropping SDRs on an unproven motion.

How do you sell AI to enterprise buyers who have procurement and governance blockers?

By treating procurement as a GTM workstream, not an obstacle. Enterprise AI buyers now run every vendor through a gauntlet of data-handling review, model evaluation, bias and red-team testing, and an AI governance committee review. That can add three to six months to a deal. We help AI companies pre-stage that review: a trust centre that documents data use, model provenance, and evaluation methodology; a security package that answers the 80 percent of questions most enterprise reviews ask; reference architectures for the deployment patterns enterprise IT will actually approve. We also coach AEs to invite procurement early rather than hide from it, which shortens the cycle significantly.

What are the most common AI buyer objections and how do you handle them?

Four objections dominate. First, hallucination and accuracy risk — handled with evaluation data, human-in-the-loop design, and clear accuracy thresholds. Second, data exposure and IP leakage — handled with tenancy options, no-training guarantees, and a trust centre. Third, vendor lock-in to a model provider — handled with model-agnostic architecture or transparent benchmarks. Fourth, ROI uncertainty — handled with pilot programmes that measure against a baseline the buyer already tracks. We build the objection-handling assets (one-pagers, trust pages, ROI calculators, eval reports) as part of the GTM system so AEs are not improvising in the deal.

Should an AI company run outbound or wait for inbound to build?

Both, but weighted by stage. Pre product-market fit, outbound is a discovery tool — you are using it to find the shape of the buyer, not to hit a pipeline number. At early PMF, outbound is the fastest way to validate a segment and book design-partner conversations at volume. Once PMF is real, outbound and inbound run together: outbound captures the accounts where the buyer has not yet started searching, inbound captures the ones who have. Waiting for inbound is a luxury most AI companies cannot afford because the category is still forming and buyers are not yet searching in predictable patterns. We build both motions through our SDR agency and SaaS SEO agency services.

How do you approach SEO and content for AI companies when the keywords barely exist yet?

By building two layers in parallel. The first layer targets existing high-intent keywords adjacent to your category — the workflow, tool, or incumbent your buyer is searching for today. The second layer builds the new category language: long-form explainers, comparison pages, and evaluation guides that define the space you want to own. The second layer is slower but more defensible because it compounds as AI search engines like ChatGPT, Perplexity, and Google AI Overviews cite the source that taught them the category. Our GEO agency work is particularly relevant here — AI buyers are disproportionately likely to start research inside an LLM rather than Google.

How long do AI deals take to close compared to traditional SaaS?

Longer, and trending longer. A mid-market AI deal that would be 30 to 60 days in traditional SaaS is now routinely 90 to 150 days because of AI governance review. Enterprise AI deals frequently exceed 12 months, especially in regulated industries. Pilot-to-production paths add another dimension: many AI deals close as a paid pilot first, then expand to production three to nine months later once accuracy and ROI are proven. We structure GTM engagements around that reality — expect early pilot wins at month 3 to 6 and meaningful production revenue at month 9 to 15.

Do you work with foundation model companies or applied AI startups?

Mostly applied AI. Foundation model GTM is a different game — the buyer is a developer or platform engineer, distribution runs through cloud marketplaces, and the sales motion looks more like infrastructure than applications. Applied AI companies selling into a specific vertical or workflow have a buyer we know how to reach: a line-of-business leader whose job the product changes, backed by an IT or data team who signs off on deployment. Most of our AI engagements are applied AI companies between seed and Series C selling into regulated industries, knowledge work, and operations-heavy categories where the ROI story is legible.

How do you avoid burning budget on AI GTM experiments that do not work?

Small bets, fast reads, clear kill criteria. Every channel we launch for AI clients has a defined test: sample size, expected response rate, qualification threshold, and the point at which we stop and reallocate budget. We run the first outbound sequence into a 500-account test list before scaling to 5,000. We publish the first 10 content pieces in the new category language before committing to 100. We instrument every touchpoint so the CAC read is truthful at four weeks, not six months. In an emerging category where buyer behaviour is still shifting, the team that iterates fastest wins — and iteration only works if the measurement system is trustworthy.