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Stop Planning. Start Shipping.

How long does it take to build an MVP? Our 6-week MVP process transforms your idea into a production-ready application. Most clients see working prototypes within 2 weeks and full deployment by week 6.
You have an AI automation idea. Maybe it’s been sitting in your backlog for months. Maybe your team keeps talking about it but never starts. Maybe you tried building it but got stuck in complexity. We turn that idea into production reality. Like Snacker.ai - from Lovable prototype to 5,000+ users and paying customers in 3 months.

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The 6-Week MVP Process

What exactly do you deliver in 6 weeks? A complete AI-powered MVP with core features, deployment infrastructure, authentication, documentation, and 30 days of support - everything needed for real users.
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Week 1: Discovery & Architecture

Monday: Kickoff call to understand your vision and constraintsTuesday-Wednesday: Map workflows and identify integration pointsThursday: Present technical architecture for approvalFriday: Finalize specifications and success metrics
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Week 2: Core Development

Build the foundation: - Set up infrastructure and deployment pipeline - Implement core business logic - Create data models and storage layer - Build authentication and security
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Week 3: Intelligence Layer

Add the AI magic: - Integrate LLM capabilities (GPT-4, Claude, etc.) - Implement prompt engineering and optimization - Build error handling and fallback systems - Create structured output generation
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Week 4-5: Integration & Testing

Connect everything: - API integrations and webhooks - Third-party service connections - End-to-end testing scenarios - Performance optimization
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Week 6: Polish & Deploy

Ship to production:
  • Complete testing and edge case handling
  • Deploy to your environment
  • Conduct handoff training with your team
  • Deliver documentation and maintenance guides

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What Makes a Good MVP Candidate?

Is my idea suitable for a 6-week MVP? If you have a clear workflow to automate, available data sources, and a single core use case, you’re likely a perfect fit for our MVP process.
  • ✅ Perfect Fit
  • 🔄 Needs More Scope
  • 🏢 Enterprise Engagement
  • Clear, repetitive workflow to automate
  • Existing manual process taking 10+ hours weekly
  • Available data sources and APIs
  • Single core use case to optimize
  • Team ready to adopt new tooling
Examples:
  • Lead qualification system
  • Document processing pipeline
  • Content generation workflow
  • Customer support automation
  • Data extraction and reporting

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Technology Stack

Why do you use these specific technologies? Our stack (TypeScript, React, Next.js) is chosen for rapid development, type safety, and proven scalability
  • enabling us to deliver production-ready systems quickly.

Core Technologies

TypeScript, React, Next.js, Node.js, Hono, Vite

AI/LLM Platforms

OpenAI GPT-4, Claude 3.5, Gemini, Llama, Mistral

Automation & Workflows

Playwright, n8n, Kestra, BullMQ, Trigger.dev

Infrastructure

Railway, Tailwind CSS, Zustand, TanStack Query

View Full Tech Stack

See our complete technology stack with detailed explanations of why we chose each tool and how they work together to ship production-ready systems.

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See Our Work in Action

Check out our open source projects and examples on GitHubWe believe in showing, not telling. Our GitHub showcases real implementations, from Chrome extensions to LLM workflows.

GitHub Portfolio

Browse our open source projects and code samples. See the quality and approach we bring to every project.

Case Studies

Read detailed breakdowns of systems we’ve built, including technical architecture and business results.

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Why Our Approach Works

We don’t build everything - we build the one thing that matters most. The 20% of features that deliver 80% of value. This laser focus ensures you get maximum impact.
After 100+ projects, we have templates, libraries, and patterns that accelerate development. We’re not figuring it out - we’re applying what works. This means faster delivery and fewer bugs.
Doug personally leads every MVP sprint. No junior developers learning on your dime. Senior expertise from day one means better architecture decisions and cleaner code.
We make technical decisions quickly based on experience. No analysis paralysis or committee reviews. We know what works and we implement it efficiently.

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Success Metrics We Target

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What Happens After the MVP?

Option 1: Run With It

Your team takes ownership and extends the system. We provide complete source code, documentation, and 30 days of support.

Option 2: Iterate Together

Continue development with our sprint cycles. Add features based on user feedback, scale to handle more volume, optimize performance.

Option 3: Full Production

Upgrade to enterprise-ready system with advanced monitoring, multi-tenant architecture, compliance audits, and custom UI.

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Common Questions

We expect some evolution. The weekly check-ins ensure we’re building the right thing. Major pivots might require scope adjustment, but minor changes are part of the process.
Yes. We integrate with your current systems, databases, and workflows. The MVP becomes part of your ecosystem, not a standalone island.
We follow security best practices by default. Specific compliance requirements (HIPAA, SOC2, etc.) may require additional scope, which we’ll identify during discovery.
Focus, experience, and proven patterns. We’ve built similar systems many times. We know what works and what doesn’t. We don’t waste time on things that don’t matter.
You get 30 days of support included. After that, we offer maintenance packages or can train your team to take over. The code is 100% yours.
Yes, we’re happy to sign NDAs. We treat all client work as confidential by default. Your competitive advantage stays yours.

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Real MVP Success: Snacker.ai

From Lovable prototype to production SaaS in 3 monthsSnacker.ai started as a broken Lovable prototype. We stabilized the codebase, integrated AI-powered video editing, and launched to market. Result: 5,000+ users, 20 paying customers, and a productized service that transformed GrowthMatch’s business model.Read the full Snacker.ai case study →

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What We Actually Built:

  • AI Video Editing: Zero-UI editing that removes gaps and mistakes automatically
  • 20-Second Turnaround: From recording to polished, shareable video
  • Browser-Based Processing: WebCodecs API with HLS streaming workarounds for iOS
  • Multi-Platform Distribution: One-click publishing to LinkedIn, X, YouTube
  • Investment: $35-40k over 3 months
  • Result: Production-ready app with paying customers

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Start Your MVP Sprint

Get Started

Learn more about how we work together and start your AI automation journey with our proven process.

View Our Work

Check out our GitHub to see code quality, project examples, and open source contributions.
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