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Service · AI Automation

AI automation, where it actually compounds.

Most agencies sell AI as a buzzword. We design and operate the systems — lead enrichment, audience modeling, content workflows, brand-voice generation, and reporting agents — that compress weeks of manual work into hours, with senior humans still in the loop where it matters.

Outcomes

What we engineer for.

Targets, not promises — the metrics every engagement is measured against. Real numbers from real clients live in the case studies below.

10×+

Speed on audience research

−60%

Manual reporting time

24/7

Lead enrichment cadence

Weeks

Time to production system

Who it's for

The shape that fits.

AI automation pays back fastest where there's repeatable knowledge work and clean data. The clients who get the most leverage tend to share these traits:

Ideal clients

  • Founder-led businesses where senior people are stuck doing manual work
  • Sales-led companies with high lead volume and inconsistent qualification
  • Marketing teams drowning in reporting requests from leadership

Industries

  • Professional services
  • B2B SaaS
  • Financial services
  • Healthcare & wellness
  • Local & home services

Stages

  • Teams of 10–100 hitting operational ceilings
  • Companies in PE/VC scaling phases
  • Established operators automating before scaling headcount

Methodology

How we run AI Automation.

Every AI automation engagement runs through the same four-stage loop. We don't ship demos — we ship production systems with documentation, monitoring, and a runbook.

  1. 01

    Workflow audit

    We sit with the people doing the work, document the steps, identify what's repeatable, and map where AI adds leverage versus where it adds risk. Not everything should be automated.

  2. 02

    System design

    Each workflow is designed as a graph — inputs, transformations, validation, human-review gates, output. We pick tools (Clay, n8n, Zapier, Make, custom code, the OpenAI / Anthropic APIs) based on the job, not the trend.

  3. 03

    Production build

    Systems are built with monitoring, error handling, and clear ownership. Senior humans review where judgment matters — copy approval, lead qualification, anomaly response — so quality holds at scale.

  4. 04

    Operating handoff

    We document the runbook, train the in-house team, and stay on retainer for the first quarter to tune. The system is yours when we're done — not a black box that breaks the moment we leave.

What's included

The deliverables.

What gets built inside a typical AI automation engagement.

  • Lead enrichment & scoring

    Inbound and outbound leads enriched with firmographic, intent, and persona data — then scored against your ICP using a model trained on your closed-won history.

  • Content & creative pipelines

    Brand-voice generation pipelines for ad copy, email, social, and short-form video scripts. Senior creative reviews every output before it ships.

  • Reporting agents

    Weekly performance digests compiled automatically — pulling from Meta, Google, GA4, your CRM — and written in your team's voice, not template English.

  • Audience modeling

    Customer-call transcripts, support tickets, and review data clustered into audience territories that inform creative, copy, and product positioning.

  • Operations runbook

    Documentation of every workflow, owner, monitoring alert, and escalation path — so the team can run the system without us.

Where AI changes the math

Operational, not theoretical.

Most agencies say they 'use AI.' Here's what that actually looks like in production for our clients.

01

Structured prompting, not chat

Production systems use structured prompts with explicit schemas, validation, and retry logic — not a teammate copying outputs out of ChatGPT. The difference is reliability and audit trail.

02

Human-in-the-loop where it matters

Every system has a defined gate where a senior human reviews. Copy ships through editors. Leads route through a qualifier. Reports are signed off before they leave the building. Speed without judgment is just faster mistakes.

03

Tool-agnostic by design

We build on Clay, n8n, Make, Zapier, custom code, and the major model APIs depending on the workload. The systems aren't locked into a single vendor — so when models or pricing change, the work doesn't break.

Frequently asked

Quick answers.

Is this AI strategy or AI implementation?
Both, but heavily weighted toward implementation. We won't ship a strategy deck without ownership of the systems that come from it. The deliverable is working software — production workflows, documented and monitored — not a roadmap.
Which tools do you build on?
Tool selection is workload-driven. Lead enrichment usually runs on Clay. Workflow orchestration on n8n, Zapier, or Make. Generation pipelines on the OpenAI and Anthropic APIs depending on the use case. We avoid lock-in.
Do you replace our marketing team?
No — we make it harder to replace them. The goal is leverage, not headcount reduction. Senior humans get freed from manual work to do the judgment-heavy parts that matter.
How long does a build take?
A first production system typically ships in 4–8 weeks, depending on data readiness and the workflow's complexity. We size projects against business value, not engineering time.
Who owns the systems we build?
You do. We document everything, train your team, and the runbook lives in your tools. We stay on retainer if you want ongoing optimization, but the system isn't ours to take with us.

Engage

Ready to put AI Automation to work?

Most engagements start with a 30-minute strategy call — no pitch deck. We'll walk through your goals, your numbers, and what the first 90 days should look like.