Keep your AI content system tuned as your business changes.
AI content workflows are not set-and-forget. Prompts drift, ICPs evolve, templates age, costs creep. Ongoing Optimization is a productized monthly retainer that keeps the system aligned with the business it is supposed to serve.
Built for teams running an AI content workflow that needs expert ongoing tuning — whether you built it with ATLAS or somewhere else.
AI content workflows quietly stop working.
The system that shipped three months ago was scoped against three-month-old data. Your business has moved since then. New offers. New ICPs. New objections from sales calls. New competitor moves. New customer language showing up in tickets and reviews.
Without ongoing tuning, the prompts that worked stop matching the buyer. The templates that produced clean output start producing thin output. Costs creep as the model handles traffic that should route to a smaller one. The team quietly stops using the system because it stopped feeling useful.
Ongoing Optimization is the maintenance layer that keeps the workflow aligned with the business — not a generic AI consulting retainer.
What is included every month.
Seven categories of work that keep the system tuned. Specific allocations within them flex with what your stack actually needs in any given month.
New content templates
As your offers and audiences shift, new content types come into scope. New templates get scoped, drafted, and added to the workflow.
Prompt and workflow tuning
Existing prompts, signal extractors, and synthesis steps get tuned against current customer language. Outputs stay sharp instead of drifting toward generic.
Monthly performance review
A short written report each month: what shipped, what worked, what did not, what is next. Includes cost trends, output volume, and quality observations.
Integration updates
When CRMs, review platforms, or content destinations change shape, the integration layer keeps working without you re-coordinating it.
Campaign expansion
New angles surface from new signal. The workflow gets extended to handle them — new sequence variants, new comparison pages, new social formats.
Quality gate improvements
Approval criteria evolve as you learn what is working. Quality checks and human-review gates get tightened or relaxed in response.
Reporting and support
Async support throughout the month for questions, decisions, or requests. Plus the optional 30-minute monthly review call if you want one.
Discovery, baseline, and the first quick wins.
Month one is intentionally substantive. It is not a holding pattern.
Discovery
Audit of your current AI content stack: workflows, prompts, templates, integrations, output samples, cost data. Identify what is drifting and what is working.
Baseline
Capture metrics that matter: cost per asset, time-to-publish, output volume, quality score, customer feedback. Sets the reference point for monthly reviews.
Quick wins
Two or three immediate improvements ship in month one. Usually prompt tuning + one template refresh + one cost optimization. Tangible value before the first invoice clears.
Roadmap
Written plan for months two and three covering planned tuning, expansion, and reporting cadence. Aligned with what you actually need, not a fixed checklist.
Built for teams running an AI content workflow that needs ongoing tuning.
Not a fit if you do not have an AI content workflow yet. Start with the Content Ops Audit instead.
Teams running an AI Content Ops Station
You finished a pilot or full build with ATLAS. You want continuity rather than a one-shot project. Ongoing Optimization keeps your system aligned with the business it is now operating against.
Teams who built their own AI content workflow
Your team built something internally — prompts, templates, automation — but no one owns the ongoing tuning. Engineers move to other priorities. Marketing inherits a system they cannot maintain. Ongoing Optimization is the expert maintenance layer.
Teams coming off a pilot or full build
A pilot ($7,500+) or full build ($15,000+) is the build itself. Ongoing Optimization is what keeps it useful past month one — without re-scoping a new project every time the business shifts.
Single floor. Scoped to your stack and cadence.
One retainer shape. No tiered comparison games. The price reflects effort; the scope flexes with what your workflow actually needs.
Ongoing Optimization
~10–15 hours of focused work per month
Most engagements settle at the floor. Heavier stacks — multiple data sources, multiple workflows, more output volume — scope up from there at the next quarter review.
- All 7 categories of monthly scope above
- Async support throughout the month
- Optional 30-minute monthly review call
- Written monthly performance report
- Month-to-month — no annual commitment
- Cancel with 30 days notice
- Pause for a month if you have a slow stretch
- Scope adjustments reviewed quarterly
- No surprise overages — heavy months even out
Common questions before booking.
Quick answers to the questions that decide whether the retainer is a fit.
Do I need to be an existing ATLAS customer to qualify?
No. Ongoing Optimization works for any team running an AI content workflow that needs expert ongoing tuning. Teams who built their own internal AI content systems are a common fit.
What counts as in-scope work in a month?
Anything that keeps the workflow aligned with the business: prompt tuning, template updates, integration fixes, new content angles, quality gate refinements, reporting. Out-of-scope work — a brand-new pipeline, a new data integration, a major rebuild — is scoped separately as a project.
Can I pause or cancel?
Yes. The retainer is month-to-month. Cancel with 30 days notice. Pausing for a month is also fine if you have a slow stretch.
How do hours and scope adjustments work?
The $2,500/month floor implies roughly 10–15 hours of focused work. If your stack regularly needs more, the retainer scope adjusts at the next quarter boundary. No surprise overages — if a month runs hot, the next month evens out.
What if I do not have an AI content workflow yet?
Start with the Content Ops Audit instead. Ongoing Optimization is the maintenance layer for systems already running. Trying to use it as the build phase will produce poor value for both sides.
Want to keep your AI content system tuned?
The first conversation is a 20-minute call to understand your current setup, your output cadence, and what is drifting. From there I scope the retainer to your actual stack.
Ask About Ongoing Support ($2,500/mo)Ongoing Optimization is delivered as a productized retainer, not a self-serve subscription. The first conversation defines what is in scope each month so neither side gets surprised.