Aizaz
What you are actually paying for
AI automation pricing is rarely "the model API." It is discovery, integration, error handling, deployment, documentation, and the engineering time to make the workflow survive production — not just a demo.
Clients who compare quotes on "hours to build a chatbot" usually miss the integration and maintenance work that determines whether the project is useful six months later.
Sprint vs open-ended projects
A 14-day AI Systems Sprint scopes one workflow with a fixed outcome. That is how we de-risk a first project: one system, measurable result, clear handoff.
Larger platforms or multi-system automation are quoted in milestones — not open-ended hourly work without a defined workflow.
Review engagement models for MVP builds, retainers, and audits if you are deciding how to structure a longer engagement.
Integration depth moves cost
Connecting HubSpot is different from NetSuite, Shopify, and custom ERP middleware. More systems, failure modes, and compliance constraints mean more engineering time — regardless of which LLM you choose.
Ongoing costs
Factor API usage, hosting, monitoring, and optional retainer support — not just build cost. A workflow that nobody maintains becomes manual work again, with extra software attached.
How we recommend scoping a first project
- Pick one workflow with daily volume (see identifying workflows worth automating)
- Fix scope and outcome before model selection
- Measure hours saved after launch — then decide what to automate next
Conclusion
Get a fixed scope for the first workflow. Measure what changed. Expand with data, not hype.