/ S_02 · AI Growth Systems

    AI growth systems for teams that need sharper execution and faster learning loops.

    AI is not a tool you bolt onto the org chart. It's a way to shorten the distance between a hypothesis and a shipped test. This engagement builds the agentic workflows, creative pipelines and reporting layer that let a growth team move at that speed without adding headcount.

    Start a conversation →Hiring Brief →
    / Who this is for
    • ▸CMOs and growth leaders who want AI in the workflow, not just the deck
    • ▸Ecommerce, DTC and B2B teams shipping too few creatives per week
    • ▸Founders building AI-native brands from day one
    • ▸Ops teams drowning in reporting, briefs and QA
    / The problem

    Where teams get stuck.

    Most teams are using AI the way they used stock photos in 2014 — as a one-off asset generator. The compounding value shows up when agents run inside the actual growth loop: brief, produce, ship, measure, learn, brief again. That's a systems build, not a subscription.

    / What Heath builds

    The system.

    Four workstreams that make the engagement real. Not a deck — the systems that get built and left in place.

    • 01 /

      Agentic workflow architecture

      Multi-step agents built with Claude, Codex, the OpenAI Agents SDK, MCP and n8n. Human-in-the-loop review points where they matter, autonomous handoffs where they don't. Prompt standards, eval frameworks and logging so the system stays reliable in production.

    • 02 /

      Creative automation pipeline

      AI product photography, AI video concepts, static and short-form ad variants, and landing-page copy generated on a weekly cycle. Every asset is tagged, versioned and tied back to performance data so the model of what works keeps improving.

    • 03 /

      Reporting and lead-quality agents

      Reporting agents pull from GA4, CallRail, HubSpot, Klaviyo and paid platforms into a single scorecard. Lead-quality monitors flag CPL and CPA drift in real time and route corrections to the team before wasted spend compounds.

    • 04 /

      SEO, content and answer-engine systems

      Content briefs, structured data, internal linking and answer-engine visibility built as a repeatable pipeline instead of one-off blog posts.

    / What this fixes

    The symptoms.

    If two or three of these read as a description of the current state, this is the right engagement to have a conversation about.

    • ✕AI stuck at one-off asset generation instead of running inside the growth loop.
    • ✕Creative production capped by human bottlenecks and shoot cycles.
    • ✕Reporting stitched together by hand every Monday morning.
    • ✕Lead-quality drift on paid programs that goes unnoticed until CFO Friday.
    • ✕SEO and content briefs written from scratch every time.
    • ✕A team excited about AI with no operating model to actually deploy it.
    / Operating model

    How this runs.

    • 01 · Discovery

      Two-week executive discovery: goals, metrics, systems audit, adoption plan. Every agent has a defined job and a measurable outcome.

    • 02 · Ninety-day build

      Ninety-day sprint to ship the first three to five agents in production with owners, dashboards and QA baked in.

    • 03 · Enablement

      The team is trained on the system, not dependent on the builder. Documentation, runbooks and eval frameworks stay with the org.

    • 04 · Iteration

      Monthly working sessions to extend the system, retire what isn't earning its keep, and layer in new capability.

    / How the engagement works

    Ninety days, not slides.

    01 /Discovery is a two-week executive sprint: goals, target metrics, systems and data audit, and an adoption plan. Every proposed agent has a defined job, an owner, a measurable outcome and a kill criterion — no science-fair projects.

    02 /The 90-day build ships the first three to five agents into production across creative, reporting and lead-quality — the highest-leverage workflows first. Agents are built on Claude, Codex, the OpenAI Agents SDK, MCP and n8n, with human-in-the-loop review where it matters and prompt evals baked in.

    03 /The final phase is enablement and iteration. The internal team is trained on the system, not dependent on the builder. Runbooks, prompt libraries and eval frameworks stay with the org, and monthly working sessions extend the system and retire what isn't earning its keep.

    / Proof

    Proof to review.

    View full portfolio →
    / Common questions

    Before you reach out.

    Do you replace our marketing team with agents?
    No. Agents replace the low-leverage repetitive work — briefs, first-pass creative, reporting, QA, monitoring — so the team can spend its time on strategy, judgment calls and things AI genuinely can't do. Headcount usually stays flat and output goes up.
    What stack do you build on?
    Whatever is right for the job: Claude and GPT for reasoning, Codex for code-level automation, the OpenAI Agents SDK and MCP for tool orchestration, n8n for workflow glue, and the existing marketing stack (GA4, Klaviyo, HubSpot, Shopify, the ad platforms) as the ground truth data layer.
    How do you keep the agents reliable in production?
    Every agent ships with an eval framework, logging, human review points for anything customer-facing, and a documented rollback plan. Reliability is a design constraint, not a hope.
    / Related services
    / Resources
    / Next step

    If AI needs to live inside the growth loop — not next to it — start here.

    Contact Heath →Hiring Brief →