Shipped to production, not pitched as a demo
AI Marketing & Consulting
AI is useful for the marketing work that currently eats senior time — briefs, scoring, drafting, enrichment — but only when the pipeline around the model is designed properly. I run orchestrated AI content systems in production and bring that pattern to client engagements.
Haiku / Sonnet / Opus
Models in orchestration
4+
Production AI integrations
pgvector
RAG on PostgreSQL
Per-token
Cost tracking built in
How I actually run this
I have been shipping Claude and OpenAI into production systems since 2023. Content Mint runs a multi-brand content pipeline orchestrating Haiku, Sonnet, and Opus with per-token cost tracking. Brand KB does RAG over pgvector with an embeddable SDK. Harness Lab is a self-improving meta-harness that analyzes historical runs and proposes prompt optimizations. The common thread: AI as a governed system, not a chatbot demo.
Most AI engagements I take on start with a workflow audit — what repeats, what depends on judgment, and where a language model can do useful work without generating liability. From there I design implementations that use the smallest viable model for each step, with human review gates where the output matters. I stay away from speculative autonomy and focus on systems that can be shipped, measured, and improved.
What's included
- Workflow audit
- A written pass over the marketing operation identifying what repeats, what depends on judgment, and where a language model can do useful work without generating liability.
- Content pipeline design
- Orchestrated production pipelines with distinct steps — signal ingestion, research, drafting, quality analysis — and human review checkpoints where the output actually matters.
- Model orchestration
- The right model for each step. Haiku for classification, Sonnet for drafting, Opus for judgment — with a per-token cost ledger so spend stays predictable.
- Human-in-the-loop gates
- Explicit checkpoints where a human reviews, approves, or rejects before anything goes live. The team stays in control; AI handles the repeatable parts underneath.
- Governance and review process
- Written guidelines for when the team trusts model output, when it requires review, and how to handle model releases and drift without rewriting the integration.
How it works
Five phases from audit to governance — each producing a concrete artifact, not a slide deck.
- 1AuditMap the current marketing workflows and identify where language-model work will be useful — and where it will not.
- 2DesignDesign an integration for the specific workflow: model selection per step, cost projections, fallback behavior, human checkpoints.
- 3PilotBuild a working prototype against the real workflow. Observability and cost tracking wired in from day one — not a demo.
- 4IntegrateWire the pilot into the existing stack. CMS, CRM, calendar, analytics — AI as a layer on top, not a replacement.
- 5GovernDocument when the team trusts output, when review is required, and how to handle model drift and releases.
Frequently asked questions
- What does AI actually do well inside a marketing team?
- AI is useful for the repeat work that currently eats senior time — drafting briefs, scoring inbound leads, classifying support tickets, assembling first-draft content, summarizing research, rewriting for tone, enriching data. It is not useful for judgment-heavy work that should not be automated away — strategy calls, brand voice decisions, customer positioning. The engagements I take on start by drawing that line explicitly, so the team does not waste effort automating the wrong things.
- How do I avoid AI-generated slop in my content?
- The common failure mode is pointing a single large model at a blank page and shipping whatever it produces. The working pattern is an orchestrated pipeline: signal ingestion, research, structured drafting, a quality analysis pass, and human review before anything goes live. I run this in production on Content Mint — a multi-brand editorial system orchestrating Claude Haiku, Sonnet, and Opus through distinct steps with human checkpoints. The pipeline is the quality control, not the prompt.
- Which models should my team be using?
- The smallest one that gets the job done for each step. Haiku for classification and scoring. Sonnet for drafting and rewriting. Opus for evaluation, judgment, and anything customer-facing. That mapping keeps cost and latency predictable, and it lets you run far more volume than a single-model pipeline would allow. The models change every few months — what does not change is the shape of the workflow.
- Can you set up AI workflows on top of our existing tools?
- Almost always. Most clients already have a CMS, a CRM, a content calendar, and analytics in place. I build integration layers on top of those — Claude or OpenAI inside the existing stack, with observability, cost tracking, and review gates wired in. I avoid platform replacement projects. AI should make the existing marketing machine faster, not force a rebuild.
Pair with
Ready to put AI to work — properly?
Book a 30-minute discovery call and I'll sketch the first workflow audit with you.
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