Selling AI is a different marketing job than selling SaaS. Your buyer is technical, your competitive landscape moves in weeks, your pricing is usage-based, and increasingly your most important "search engine" is another AI. Most marketing agencies aren't built for any of this. We are — from foundation-model platforms to applied-AI verticals, we build the marketing motions AI brands actually need.
Developers, ML engineers, and data teams evaluate AI products on documentation depth, runnable code examples, transparent benchmarks, and the quality of the changelog. They actively distrust marketing-language hero sections and quote carousels. Most agencies have no idea how to write for this audience. We do, and we write everything with that buyer in mind.
When developers ask Claude, ChatGPT, or Perplexity which tool to use for a task, the answer favors whoever was well-documented and well-cited on the public web. AI products that haven't invested in AEO are systematically missing from the answer at the exact moment buyers decide. This is one of the largest, least-discussed shifts in B2B distribution happening right now.
When revenue is a function of API calls, tokens, or compute hours, the MRR-and-churn framework stops describing the business. Cohort curves are non-linear, expansion happens silently, and the channels that bring high-value usage-heavy customers are different from the channels that bring trial-only signups. Marketing optimization has to be rebuilt for this reality.
The work that makes ChatGPT, Claude, Perplexity, Gemini, and AI Overviews cite your product when buyers ask category questions. Structured content, factual claim density, schema, and the citation surface that increasingly drives discovery in technical markets.
Documentation that doubles as a top-of-funnel marketing surface. Runnable examples, integration tutorials, benchmark posts, and changelog content that earns developer trust — built on Mintlify, Docusaurus, or custom Next.js MDX.
Paid acquisition designed for technical buyers. Developer-focused creative, content syndication in the right communities, and ABM for enterprise AI sales. Not the generic B2B-SaaS paid playbook applied to AI brands.
GitHub stars, npm downloads, sandbox sign-ups, API-key creation, first-call conversion — instrumented as one unified funnel. Joined to product usage so content investment connects mathematically to API revenue.
Cohort modeling built for usage-based pricing. Predictive expansion. LTV that accounts for non-linear consumption curves. The math the team needs to bid against true value, not against signup events.
Programmatic head-to-head comparisons, integration pages ("X with Y framework"), and benchmark posts engineered for both human readers and AI-search citation. The pages technical buyers actually look at before evaluation.
Within a quarter, the structured-content and citation work compounds into measurable AEO presence — your product appears in AI-assistant answers for category questions, in Perplexity citations, and in AI Overview panels. This is increasingly where evaluation actually starts.
You can finally answer "did this content series produce API customers?" with a number, not a guess. Marketing budget moves toward the content and channels that produce usage-heavy accounts, away from what just produces signups that never activate.
The creative and content pipeline ships weekly, not quarterly. When a competitor releases a new model or a foundation player changes the category, your marketing can respond inside a week — not inside a planning cycle.
We're platform-agnostic by design — we work with the tools your team already runs, and add only what's missing. The shortlist below is the stack we deploy most often for AI / ML engagements.
Docs & developer experience: Mintlify, Docusaurus, Nextra, custom Next.js MDX — built as both docs and SEO/AEO surface.
Product analytics: PostHog, Mixpanel, Amplitude, Segment — instrumented around API events and developer activation.
Billing & metering: Stripe Metered, Orb, Metronome, Lago — joined to cohort modeling for usage-based revenue.
Developer-funnel data: GitHub API, npm download stats, Hugging Face metrics — pulled into the warehouse for unified attribution.
AI search visibility: Custom Perplexity, ChatGPT, Claude, and Gemini citation tracking with structured-data pipelines.
Data warehouse & BI: BigQuery, Snowflake, ClickHouse — for both product event volume and usage-revenue modeling.
Marketing site: Next.js, Framer, Webflow — engineered for technical-audience trust signals and Core Web Vitals.
Every engagement begins with a Discovery Audit — a six-week fixed-scope diagnostic of your current marketing operation. From there, AI / ML clients usually move into one of three paths, depending on where the biggest constraint is.
Most common entry. When the product is invisible inside LLMs and undervalued in search, we rebuild the citation surface, docs architecture, structured-content pipeline, and the AEO measurement layer. 12 to 16 weeks, then continuous content operation.
When acquisition is good but no one can connect content investment to API revenue. We rebuild the unified pipeline from public surfaces (GitHub, docs, social) through activation through paid usage. Warehouse, multi-touch attribution, and developer-cohort modeling.
For AI companies that need a unified marketing, creative, content, AEO, and analytics operation moving at model-release cadence rather than agency cadence. Everything under one architecture, weekly ship velocity.
Discovery Audit looks at your full AI/ML stack — docs, developer funnel, usage analytics, AEO citation surface, and how data flows between them — and returns a clear roadmap. Six weeks, fixed scope, your document to keep regardless of next steps.