Ship your first AI-powered automation in 7–14 days.

What AI Automation QuickStart actually means

AI Automation QuickStart is the smallest unit of useful AI automation work I sell. The principle: pick one specific manual task that's costing your team real hours, scope it tight, ship it in 7 to 14 days, hand over working source code and documentation, and support it for 60 days afterward.

It exists because most of the automation requests I receive are exactly this shape — we have this one painful workflow, can you just build it? — and the right answer is to deliver that one thing, not to upsell a six-week strategy phase.

A QuickStart engagement is fixed scope, fixed timeline, and a fixed price floor. The discovery call confirms the scope before any quote, and the quote doesn't move unless the scope explicitly moves.

What kinds of automations fit a QuickStart

A typical AI Automation QuickStart covers a single workflow, end-to-end, from trigger to outcome. Common shapes:

• Data pipelines — extracting from one or more systems, transforming, loading into a destination (databases, spreadsheets, dashboards). Scheduled or event-triggered.
• Web scraping with AI enrichment — turning unstructured web content into structured records with LLM-summarized fields ready for outreach, research, or CRM ingestion.
• AI-augmented document workflows — ingesting documents (invoices, contracts, reports), extracting structured information with an LLM, routing to the right system.
• Notification and reporting agents — automations that watch for conditions across systems and surface them via Slack, WhatsApp, or email at the right moment.
• Form-to-action automations — a form submission triggers a multi-step backend process across several tools.
• Integration glue — making two or three systems talk to each other when no off-the-shelf integration exists.

The constraint that makes QuickStart work is one workflow. Two workflows = two QuickStarts, or it might be an AI Product Build instead.

What "fixed scope, fixed price" actually buys you

Buyers sometimes assume fixed-price engagements are riskier — that the contractor will cut corners to protect margin. In my experience the opposite is true for the right kind of work. Fixed scope means:

1. You know what you're buying before you sign. No "discovery phase that might find more scope." The discovery call surfaces it; the quote reflects it.
2. You're not paying for time, you're paying for an outcome. If I'm faster, that's my problem. If I'm slower, that's also my problem.
3. Scope changes are explicit. If the work grows — a new system to integrate, a new edge case to handle — it's a documented scope-change line item, never a surprise on the invoice.

If your scope is fuzzy and likely to shift weekly, QuickStart isn't the right product — start with an AI & Automation Opportunity Audit, where the deliverable is itself a written, scoped plan.

The 7-to-14-day timeline, day by day

The timeline isn't aspirational marketing. It's a constraint that shapes the engineering. Here's how it actually unfolds:

Day 0 — Discovery Call (20 min). We confirm fit, scope, systems involved, and success criteria. I learn enough to quote within 24 hours.

Day 1 — Quote. A fixed-price quote with scope, timeline, deliverables, and exclusions. You sign or you don't. No proposal-by-committee.

Day 2 to 10 — Build. I work in a shared Slack or WhatsApp channel with you. You see daily progress. Mid-build you get a demo of the half-built system so we catch misalignment early.

Day 11 to 14 — Deploy and train. The automation goes live in your environment. You receive source code, plain-English documentation, a Loom walkthrough, and a handover call.

Day 15 to 75 — 60-day support. Any bugs, edge cases, or LLM API changes during this window are on me. You're not figuring it out alone in the first two months — which is when most post-launch friction actually happens.

A typical QuickStart architecture

To make this concrete, here's what a typical AI-augmented data pipeline QuickStart looks like architecturally: