AI automation
built for your business.

It starts with a conversation — you describe the problem, we ask questions, and we figure out together what's worth building.

From there we scope the work, agree on a fixed price, and get started. You work directly with Stephen and his team throughout. We build, you review, we iterate. No lengthy discovery phases, no disappearing for months.

It depends on what you need. For an initial AI strategy engagement — understanding where AI fits in your business and what's worth building — plan on $3,000–$4,000.

For a custom AI application build, workflow automation, integration, or full deployment, budget $15,000 and up depending on scope.

Every project is quoted at a fixed price. No hourly billing, no surprises. Tell us what you're working on and we'll give you a straight number.

It depends on the business, but here are a few examples of where AI naturally intersects with common business types.

Financial advisory firm: AI can ingest client financial data and flag irregularities, reconciliation gaps, and anomalies that would take an analyst hours to catch manually. The advisor reviews a clean summary instead of digging through spreadsheets. What used to take a day takes twenty minutes.

Law firm: Contract review and intake triage is a natural fit. AI reads incoming documents, surfaces key clauses and risk flags, and routes work to the right people — so attorneys spend their time on judgment, not extraction.

Technology company: Most SaaS businesses are sitting on years of support tickets and product feedback they never fully use. AI can surface patterns across that data — what's breaking, what customers actually want, what to prioritize next — so product decisions are grounded in signal rather than gut feel.

The throughline is the same across industries: find where your team is doing repetitive, high-cognitive-load work on information that already exists in your systems — and build AI that handles that layer.

It depends on what you're building. A strategy engagement usually wraps in two to three weeks. Most automation and integration projects run four to eight weeks. Custom AI application builds depend on scope, but six to twelve weeks is a typical range.

We work in short cycles — you see progress early and often, not at the end of a long runway. If something needs to change mid-build, it can.

This is an important question and the answer depends on what we build together. For automation workflows, data moves through the platforms we use (Make, n8n, etc.) but is never stored by Bowles Creative — it stays within your systems and the integrations you authorize. For custom builds, data lives in infrastructure you control.

When AI models are involved (OpenAI, Anthropic), we use their API — not consumer products — which means your data is not used for model training. For regulated industries or particularly sensitive data, we discuss data handling explicitly as part of scoping so there are no surprises.

Honestly, it isn't always. AI works best when there's repetitive, high-volume work that already runs through your existing systems — information that needs to be extracted, routed, summarized, or acted on at a volume that makes doing it manually feel like a tax on your team's time.

It's a poor fit when the core work requires relationship judgment, creative discretion, or when the underlying data simply doesn't exist yet. We'd rather tell you that up front than build something that doesn't move the needle.

If you're not sure, the strategy engagement exists for exactly that — to figure out where it creates real value before committing to a build.

AI systems need attention after launch — models change, outputs drift, and the business problems you're solving evolve. Most clients move into an ongoing service retainer that covers monitoring what we built, tuning it as needed, and adding to it as new needs come up. It keeps things from quietly breaking and keeps the work moving forward. Retainers average $1,000/month.