Why 2-3 Week AI Sprints Beat Traditional Projects (and How I Price Them)
Fixed scope. Fixed timeline. Fixed price. Here's why AI Development Sprints ($5K-$20K) are the best way to build AI products right now - for both clients and builders.
Editor note (February 8, 2026): This post is preserved as a historical snapshot from the earlier sprint framing. Current positioning uses agentic development lanes with public setup medians, required monthly retainers, and a 3-month minimum. See Renaming Sprints to Agentic Development and start with the quote form.
Traditional software projects move like freight trains. Big budgets, long timelines, unclear outcomes. That model is breaking - especially in AI, where the tech changes every week.
That's why I've moved to 2-3 week AI Development Sprints, priced at $5K / $10K / $20K. Fixed scope, fixed timeline, fixed price.
This post explains why the model works, how it de-risks AI for clients, and why my current 10K MRR experiment is the proof of work behind it. I also wrote The AI Development Sprint Playbook if you want the full step-by-step breakdown.
The problem with traditional AI projects
Most AI projects fail for predictable reasons:
- Long timelines mean the tech shifts mid-project
- Vague scopes cause endless revisions
- Clients pay for effort, not outcomes
- The build never reaches production
AI doesn't reward slow execution. It rewards fast cycles and feedback loops.
The sprint model (what it is)
An AI Development Sprint is:
- 2-3 weeks of focused build time
- Fixed scope (clear, bounded outcomes)
- Fixed price ($5K / $10K / $20K)
- Daily updates and review points
- Production-ready deliverables
It's designed to get to "working and testable" fast — not to disappear for months and return with something no one asked for. I deploy on Vercel with Next.js and Convex as the data layer — tools that match the sprint pace.
Why it works for AI specifically
AI products have a unique risk profile:
- Promises are big, but failures are common
- Edge cases show up early
- Models evolve faster than roadmaps
A sprint compresses the risk window. Instead of betting a quarter, you bet two weeks. If it works, you scale. If it fails, you learned fast.
What clients actually buy
Clients aren't buying code. They're buying clarity and momentum.
In a sprint, they get:
- A working prototype
- A validated workflow
- A clear understanding of costs and limitations
- The option to continue or stop
That's worth far more than a six-month promise.
The pricing tiers (and who they fit)
$5K Sprint
- Single workflow automation
- One core feature
- Ideal for small teams testing AI
$10K Sprint
- Multi-step workflow
- Light integrations
- Good for growth-stage teams
$20K Sprint
- Multiple workflows
- Custom integrations
- Best for companies serious about AI adoption
The key is that price aligns with scope - no surprises.
De-risking for both sides
The sprint model protects both client and builder:
- Clients avoid long commitments without proof
- Builders avoid endless scope creep
It's a clean contract: we both know what "done" means.
My proof of work: build in public
I'm currently running a 30-day 10K MRR experiment, building in public. In the first five days I've shipped:
- 3 production-ready apps
- 14+ agents in daily use
- 300+ commits
- $0 revenue (not deployed yet)
That gap between build and revenue is part of the proof: I'm not hiding the hard parts. I'm documenting them. That transparency builds trust. The full pivot story is covered in From Agency to AI Products: The Pivot Story.
The sprint deliverables (what I actually hand over)
Every sprint ends with:
- A working product or workflow
- Documentation (setup + usage)
- Clear next steps (what to build next)
- Optional handoff or ongoing support
It's not just "code." It's a usable asset.
Why this model is growing now
Two things happened this week:
That means the capability curve just got steeper. If you're waiting for AI to "settle," you'll miss the window. Sprints help companies move while the window is open.
The real advantage: speed with accountability
Anyone can promise AI. Few can ship.
The sprint model gives me a constraint that matters: time. It forces focus. It forces decisions. And it forces delivery. I use OpenClaw as my orchestration layer and Anthropic's Claude for reasoning across the sprint.
If you've been burned by long, vague AI projects, try the opposite. Two weeks, a clear scope, and a shipped output. I teach the full sprint process in AI Sprint Mastery.
Final thought
I built my company - Lavon Global Pty Ltd in Melbourne - around one belief: AI should ship faster than traditional software. Sprints are the cleanest expression of that belief. For the real numbers behind what this costs, see AI Agent Cost Breakdown: Real Numbers.
If you want to test the model, I'm open to sprint projects. If you just want to learn from the experiment, I'll keep publishing what I'm seeing in real time.
I cover the full sprint methodology in AI Sprint Mastery. Either way, the takeaway is simple: in AI, speed is a strategy. Follow the builds on GitHub.
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