Service
AI Tooling
AI tooling is useful when it sits on a working lifecycle system. I build focused copilots and generation tools that help teams produce, score, QA, or personalize lifecycle work without pretending the model is the strategy.
01 What this fixes
The pain teams call about.
AI experiments stay in docs and never reach production workflow.
Generated content ignores lifecycle state and brand rules.
Teams want personalization, but the underlying data is unreliable.
Manual QA slows every campaign and test.
02 Deliverables
What gets shipped.
- 01Internal copilots for lifecycle planning, copy drafts, QA, or account research.
- 02Prompt and evaluation harnesses for repeatable output quality.
- 03Workflow integrations with ESP, CRM, or internal tools.
- 04Guardrails, review states, and documentation for operators.
03 Methodology
How I approach this work.
I start with the human workflow, then add AI only where it removes real drag.
Every tool needs evaluation. If quality cannot be checked, it cannot be trusted.
The best AI layer usually looks smaller than the brainstorm, because production tools need boundaries.
Most fit a Standard Build or Platform Build engagement, with a 6-12 weeks build window depending on account complexity and team access. Start with the related tool if you want a quick read on whether the problem is worth scoping.
04 FAQ
Common questions.
Are you an AI consultant?
No. I build lifecycle infrastructure first. AI is one layer when it helps the system ship better work.
What models do you use?
Usually OpenAI or Claude, with evaluation tooling around the task. The model choice matters less than the workflow.
Can this generate email copy?
Yes, if the tool has brand rules, lifecycle context, and review controls.
Can we connect it to our ESP?
Yes, but I prefer review states before anything writes directly to production systems.
Think your lifecycle is leaking?
Book a 30-minute call. One-page scope inside a week if there’s a fit. Clear no if there isn’t.