MVP in 8 Weeks: The Venture Studio Sprint

Table of Contents

1. Why 8 Weeks? The Reality of MVP Development
2. Week 1–2: Problem Discovery & Scope Lock
3. Week 3–4: Product Design & Technical Architecture
4. Week 5–6: Core Build & AI Integration
5. Week 7: Testing, Iteration & Edge Cases
6. Week 8: Launch Prep & First Customer Acquisition
7. How Claude Opus Accelerates the Sprint
8. Common Bottlenecks & How to Avoid Them
9. Post-Launch: Scaling Beyond Week 8
10. When to Bring in a Venture Studio Partner

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Why 8 Weeks? The Reality of MVP Development

Eight weeks is not arbitrary. It’s the sweet spot where founders can validate a core business hypothesis without burning capital on feature creep, and where a disciplined team can ship something real enough to get customer feedback.

The problem most founders face is scope inflation. They start with a clear idea—a SaaS tool for X, an AI agent for Y—and then add features, integrations, and edge cases until month six rolls around and they’re still building. By then, capital is depleted, momentum is lost, and the original customer problem has either evolved or been solved by a competitor.

A venture studio sprint flips this. Instead of building everything you think you need, you build the smallest set of features that lets a real customer solve a real problem. Then you measure. Then you iterate.

This approach works because it’s grounded in Lean Startup principles, which emphasize rapid experimentation and validated learning over lengthy development cycles. The eight-week timeline forces ruthless prioritisation and keeps teams moving at pace.

At PADISO, we’ve run dozens of these sprints with founders at seed and Series A stages. The teams that hit launch in week 8 share three things: clear scope gates, daily stand-ups, and a willingness to cut features that don’t directly solve the core problem. Those that slip past week 10 usually didn’t have one of those three.

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Week 1–2: Problem Discovery & Scope Lock

You cannot sprint toward a moving target. The first two weeks are about nailing down exactly what problem you’re solving, for whom, and what success looks like in week 8.

Stakeholder Alignment & Customer Interviews

Start with your founding team and your first five to ten target customers (or users). If you don’t have them, recruit them now. These are not passive survey respondents—they’re people who will use your MVP and give you honest feedback.

Conduct structured interviews. Ask:

• What is the current workaround for this problem?
• How much time does it cost them weekly?
• What would they pay to solve it?
• What would cause them to switch to your solution?
• Who else in their organisation needs to approve the decision?

Document everything. Use a shared spreadsheet so the whole team sees the same customer truths. This prevents the founder from building what they think the customer wants instead of what the customer says they need.

Define Your MVP Scope

Now, list every feature you could build. Be exhaustive. Then score each feature on two axes:

1. Customer impact: Does solving this directly reduce the customer’s pain?
2. Technical complexity: How many days of engineering work does it require?

Keep only the features in the top-left quadrant: high impact, low complexity. Everything else goes into a “post-MVP” backlog. This is hard. Founders often resist because they see all those features as essential. They’re not. They’re distractions.

A concrete example: if you’re building an AI-powered expense management tool, the MVP might be:

• Receipt upload (photo or PDF)
• Claude-powered expense categorisation
• Simple CSV export

It is not:

• Multi-currency support
• Approval workflows
• Integration with 12 accounting platforms
• Mobile app

Those come in weeks 12–16. In week 8, you prove the core loop works: upload receipt → AI categorises it → user validates → data is useful.

Technical Architecture Decision

By end of week 2, your engineering lead (whether in-house or a fractional CTO) must have chosen your tech stack. This should take two to three days of research, not two weeks.

The criteria:

• Speed to MVP: Can we ship a working feature in days, not weeks?
• Team familiarity: Does the team know the stack, or will we spend week 1–2 learning?
• Post-MVP scalability: Can this stack handle 10x users without a rewrite?

For most SaaS MVPs in 2025, this means:

• Frontend: React or Next.js (familiar to most teams, fast to iterate)
• Backend: Node.js, Python, or Go (depends on team skill)
• Database: PostgreSQL (reliable, scales, no surprises)
• Hosting: AWS or Vercel (mature, no vendor lock-in)
• AI integration: Claude API, OpenAI, or Anthropic (whichever fits your use case)

Don’t overthink it. The best tech stack is the one your team can move fast with. Premature optimisation kills sprints.

Deliverables by End of Week 2

• Customer interview notes (5–10 interviews documented)
• Scope document (features locked, post-MVP backlog created)
• Tech stack decision (frontend, backend, database, hosting, AI model)
• Success metrics (what does a successful MVP look like? e.g., 10 beta users, $1k MRR, 50% activation rate)
• Weekly standup cadence locked (daily, 15 mins, async updates if distributed)

If you don’t have these by Friday of week 2, you’ve already lost pace. Push back on scope. Cut features. Move forward.

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Week 3–4: Product Design & Technical Architecture

Weeks 3 and 4 are about translating your scope into designs and system diagrams that engineers can build from without constant clarification.

Wireframes & User Flows

You don’t need pixel-perfect Figma designs. You need clarity on user flows. What happens when a user lands on your product? What’s the first action they take? What’s the second?

Draw these out. Use Figma, Miro, or even pen and paper. The goal is to make sure the founding team and engineering team agree on the user experience before a single line of code is written.

Common pitfall: designers spend three weeks perfecting the UI. You don’t have three weeks. Aim for rough wireframes in 2–3 days. Share them with customers. Iterate once. Lock them. Move on.

For an AI-powered tool, pay special attention to:

• Input interface: How does the user feed data to the AI?
• Output display: How is the AI’s response shown to the user?
• Validation loop: Can the user correct or refine the AI’s output?
• Error states: What happens if the AI fails or returns nonsense?

These aren’t edge cases—they’re core to your MVP. If your AI agent can’t handle a user saying “that’s wrong, try again,” your MVP is incomplete.

API & Database Schema Design

Your backend engineer should spend 2–3 days designing the API and database schema. This is not optional. A poorly designed schema causes rewrites later.

For your expense management example:

Core tables:

• users (id, email, created_at)
• receipts (id, user_id, image_url, raw_text, created_at)
• expenses (id, receipt_id, category, amount, currency, created_at)
• validations (id, expense_id, user_id, is_correct, feedback, created_at)

Core API endpoints:

• POST /receipts (upload receipt)
• GET /receipts/:id (fetch receipt + categorised expense)
• PUT /expenses/:id (user corrects categorisation)
• GET /expenses?user_id=X&date_range=Y (export data)

This takes one engineer 2–3 days. It’s not perfect, but it’s good enough to build against. You can refactor the schema in week 6 if needed.

AI Integration Points

If your MVP uses AI (and in 2025, most do), map out exactly where:

1. Receipt text extraction: Does Claude read the image and extract text, or do you use a separate OCR service?
2. Categorisation: Does Claude categorise the expense in one pass, or do you need multiple AI calls for validation?
3. Feedback loop: When a user corrects an expense, do you fine-tune the model, or just log the correction for later analysis?

For week 8 launch, you probably don’t need fine-tuning or multi-step AI workflows. You need one clean AI call that works 85% of the time and fails gracefully 15% of the time.

Deliverables by End of Week 4

• Wireframes for all core user flows (locked, shared with customers)
• API specification (endpoints, request/response schemas)
• Database schema (core tables, relationships)
• AI integration diagram (where Claude or another model fits in the flow)
• Engineering task breakdown (which engineer owns which feature, estimated days per task)

If any of these is missing or vague, you’ll pay for it in week 5–6 when engineers are blocked waiting for clarity.

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Week 5–6: Core Build & AI Integration

This is where the product comes to life. Two weeks to build the core loop and integrate AI.

Frontend Development

Your frontend engineer (or two) should focus on:

1. Authentication: User sign-up, log-in, session management. Use a library like NextAuth or Supabase Auth to avoid building this from scratch.
2. Core input form: The receipt upload interface. Make it work on desktop and mobile.
3. Results display: Show the AI-categorised expense clearly. Let the user edit it.
4. Basic navigation: Home, upload, expenses list, settings.

Don’t build:

• Advanced filtering or search
• Dark mode
• Internationalisation
• Mobile app (web-responsive is enough)

These are post-MVP features. They distract and delay.

Backend Development

Your backend engineer should build:

1. Authentication endpoints: Sign-up, log-in, token refresh.
2. Receipt upload handler: Accept image/PDF, store in S3 or similar, trigger AI processing.
3. AI integration: Call Claude API to extract text and categorise expense. Store result in database.
4. Expense CRUD: Create, read, update, delete expenses. Validate user ownership.
5. Export endpoint: Generate CSV of user’s expenses.

Use a framework you know. If your team knows Express and Node, use Node. If they know Django, use Django. Speed matters more than architectural purity.

AI-Powered Features

Here’s where Claude Opus or Claude 3.5 Sonnet shines. Instead of building custom ML models or training datasets, you use Claude’s API to handle the hard cognitive work.

For expense categorisation:

Prompt: "Categorise this receipt into one of: Travel, Meals, Office Supplies, Software, Other. Receipt text: [extracted text]. Return JSON: {category: string, confidence: 0-100}."

Claude handles the nuance. A receipt that says “Uber” gets categorised as Travel. A receipt from a cafe gets categorised as Meals. If the text is unclear, Claude returns low confidence and you prompt the user to correct it.

This approach gets you to 85% accuracy in week 5. Reaching 95% accuracy takes months of tuning and data collection. Don’t pursue it for launch.

Daily Standups & Blocking Issues

By week 5, you should be running daily 15-minute standups. Each engineer says:

• What did I ship yesterday?
• What am I shipping today?
• What’s blocking me?

If someone is blocked, the team unblocks them that day. A blocker that sits for 24 hours costs you a day of progress. A blocker that sits for a week costs you a week.

Common blockers in week 5–6:

• “I don’t understand the API spec.” → Clarify it in 30 mins.
• “Claude API is returning inconsistent results.” → Refine the prompt in 1–2 hours.
• “Database migration failed.” → Rollback and fix in 30 mins.
• “Frontend can’t connect to backend.” → Debug CORS or auth in 1 hour.

None of these should take a day. If they do, you’re overthinking the solution.

Deliverables by End of Week 6

• Fully functional frontend (sign-up, upload, results display, basic settings)
• Fully functional backend (auth, receipt handling, AI integration, CRUD)
• AI integration working end-to-end (upload receipt → Claude processes → result displayed)
• Database populated with test data
• All core features deployed to staging environment
• Known bugs and limitations documented

At this point, your product should be 80% feature-complete and 100% launchable. The remaining 20% is polish and edge cases, which you handle in week 7.

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Week 7: Testing, Iteration & Edge Cases

Week 7 is about finding and fixing the bugs that will embarrass you in front of your first customers.

Internal Testing & QA

Don’t rely on unit tests alone. Have humans use the product. Go through every user flow:

1. Sign up as a new user. Does it work?
2. Upload a receipt. Does it process?
3. View the categorised expense. Is it correct?
4. Edit the expense. Does the update save?
5. Export expenses as CSV. Is the file correct?
6. Try uploading a blurry receipt. Does the AI handle it gracefully?
7. Try uploading a non-receipt (a photo of a cat). Does it fail gracefully?
8. Try uploading a receipt in a foreign language. Does it work?

Document every bug. Prioritise by severity:

• Critical: Blocks core user flow (e.g., sign-up broken, AI integration fails)
• High: Degrades user experience (e.g., slow upload, confusing error message)
• Medium: Minor issue (e.g., button text is unclear, export format is slightly wrong)
• Low: Polish (e.g., spacing is off, colour is slightly wrong)

Fix all critical and high bugs. Leave medium and low for post-launch.

Beta Testing with Real Customers

Invite 5–10 of your target customers to use the MVP. Give them a task: “Upload three receipts and categorise them. Tell me if the AI got them right.”

Watch them use it. Don’t help. Let them struggle. Take notes.

You’ll learn:

• Which parts of the UI are confusing
• Which AI categorisations are wrong
• What features users actually need (vs. what you assumed)
• How long the core loop takes

If customers can’t figure out how to upload a receipt without help, your UI is broken. Fix it. If the AI categorises 70% of receipts correctly, that’s acceptable for launch (you’ll improve it post-launch based on user feedback).

Prompt Refinement for AI Features

If your AI integration is underperforming, refine the prompt. Instead of:

“Categorise this receipt.”

Try:

“You are an expense categorisation expert. Categorise this receipt into exactly one of: Travel, Meals, Office Supplies, Software, Other. If the receipt is ambiguous, choose the most likely category. Return only valid JSON: {category: string, confidence: 0-100, reasoning: string}.”

Test the prompt against 20–30 real receipts from your beta customers. Measure accuracy. Iterate until you hit 85%+. This takes 2–3 hours, not days.

Performance & Security Checks

Do a quick audit:

• Performance: Does the app load in under 3 seconds? Does a receipt upload process in under 10 seconds? If not, optimise or accept the limitation.
• Security: Are user passwords hashed? Are API endpoints authenticated? Can one user see another user’s receipts? If the answer to the last question is “yes,” fix it immediately.
• Data handling: Where are receipts stored? Are they encrypted? What’s your data retention policy? You don’t need military-grade security for launch, but you need some security.

For SOC 2 and ISO 27001 compliance (which you may pursue post-launch), document your security choices now. This makes the audit easier later. If you’re planning to work with PADISO on security audit readiness via Vanta, these notes are valuable.

Deliverables by End of Week 7

• All critical bugs fixed
• Beta testing completed with 5–10 customers
• AI prompts refined and tested
• Performance baseline established (load times, API response times)
• Security audit checklist completed
• Launch checklist created (what needs to happen in week 8)

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Week 8: Launch Prep & First Customer Acquisition

Week 8 is not about shipping a perfect product. It’s about shipping a real product to real customers and measuring their response.

Launch Mechanics

Deploy to production. Set up monitoring so you know if something breaks. Have a runbook for common issues (database connection fails, API rate limit hit, AI service down).

You don’t need a fancy launch event. You need real users. Reach out to your 5–10 beta testers and ask them to sign up for paid access (even if it’s a $1/month beta rate). If they won’t pay $1, they don’t believe in your solution.

If you have 5–10 customers willing to pay by end of week 8, you’ve validated the core hypothesis. If you have zero, you’ve learned something important: either the problem isn’t real, or your solution doesn’t solve it, or your positioning is wrong. That’s still a win—you learned it in 8 weeks, not 6 months.

Customer Onboarding & Support

You personally onboard every customer. Spend 15–30 mins with each one:

• Show them how to use the product
• Ask what they’d change
• Ask what they’d pay for next
• Set expectations: this is a beta, bugs may exist, you’re iterating fast

Don’t hide behind email or chat. Talk to them. Real conversation reveals what surveys and forms never will.

Metrics to Track

From day one, measure:

• Activation: % of users who complete the core action (upload receipt, get categorisation)
• Retention: % of users who return in week 2
• Revenue: How much are customers willing to pay?
• NPS: Net Promoter Score (ask: “How likely are you to recommend this to a colleague?”)
• Usage: How many receipts per user per week?

Don’t obsess over polish. Obsess over these metrics. If activation is 80% and customers are returning, you’re on the right track. If activation is 20%, something’s broken.

Post-Launch Communication

Send a weekly email to customers:

• What shipped this week
• What’s coming next week
• One thing you learned from their feedback

This keeps them engaged and reminds them that a human is building this, not a faceless corporation.

Deliverables by End of Week 8

• Product live in production
• 5–10 paying customers (even at $1/month)
• Week 1 metrics captured (activation, retention, NPS, usage)
• Week 2 roadmap created based on customer feedback
• Founder has spent 5+ hours talking to customers

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How Claude Opus Accelerates the Sprint

Claude Opus (and Claude 3.5 Sonnet) are not silver bullets, but they compress timelines significantly when used strategically.

Code Generation & Scaffolding

Instead of writing boilerplate authentication code from scratch, prompt Claude:

“Generate a Next.js API route for user sign-up with email and password. Use bcrypt for password hashing and JWT for tokens. Return the code.”

Claude generates 80% correct code in 30 seconds. Your engineer reviews it, fixes the 20%, and moves on. This saves 2–3 hours per sprint.

Documentation & Spec Writing

Prompt Claude:

“Write a concise API specification for a receipt upload endpoint. Include request schema, response schema, error codes, and rate limits.”

Claude generates a spec that your team can build against. Your engineer refines it, but you’ve saved the time of writing from scratch.

Prompt Engineering for AI Features

If your MVP uses Claude (as the expense categorisation example does), you’re using Claude twice: once to build the product, once to power the product.

Prompt Claude:

“I’m building an expense categorisation system. Here are 20 receipts and their correct categories. Generate a prompt that Claude can use to categorise similar receipts with 85%+ accuracy.”

Claude generates a refined prompt. You test it. You iterate. In 2–3 hours, you have a prompt that works.

Test Case Generation

Prompt Claude:

“Generate 30 test cases for an expense categorisation API. Include edge cases like blurry receipts, receipts in foreign languages, receipts with missing data, and receipts that don’t match any category.”

Claude generates comprehensive test cases. Your QA engineer runs them. Bugs surface earlier.

Time Savings

Across a typical 8-week sprint:

• Code generation: 10–15 hours saved
• Documentation: 5–8 hours saved
• Prompt engineering: 3–5 hours saved
• Test case generation: 2–3 hours saved

Total: 20–30 hours saved. That’s 0.5–0.75 FTE of engineering time. For a small team, that’s the difference between hitting week 8 launch and slipping to week 12.

The catch: Claude works best when you ask clear questions. Vague prompts get vague answers. Spend 15 mins crafting a detailed prompt, and Claude will save you 2 hours. Spend 30 seconds on a vague prompt, and you’ll waste 30 mins debugging the output.

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Common Bottlenecks & How to Avoid Them

We’ve run enough of these sprints to know where teams get stuck.

Scope Creep

The problem: Week 3 rolls around and the founder says, “Actually, we should also build X.” By week 5, the scope has doubled.

The fix: Lock scope in week 2. Create a post-MVP backlog. When new ideas come up, write them down and say, “That’s a week 12 feature.” Repeat this 50 times if needed. The discipline is worth it.

Unclear Requirements

The problem: Engineering starts week 3 without clear wireframes or API specs. By week 4, they’ve built something the founder doesn’t want.

The fix: Invest in design and architecture in weeks 3–4. This feels slow, but it prevents rework later. A day spent on wireframes saves three days of rework in week 6.

AI Integration Complexity

The problem: The team tries to build a sophisticated AI workflow (multi-step reasoning, fine-tuning, custom embeddings) instead of a simple Claude API call.

The fix: Start with the simplest possible AI integration. One prompt, one API call, one response. Get it to 80% accuracy. Launch. Iterate. Complexity is a post-launch feature.

Distributed Team Friction

The problem: Frontend and backend engineers are in different timezones. They can’t sync quickly. Questions go unanswered for hours.

The fix: Overlap working hours by at least 4 hours daily. Use async communication (Slack, not meetings) for everything that doesn’t need real-time discussion. Daily standups are non-negotiable.

Testing Neglect

The problem: Week 7 arrives and the team realises they haven’t tested the product end-to-end. Bugs surface in week 8 with customers.

The fix: Test continuously. Every feature that ships should be tested by a human before it reaches staging. Automated tests are nice, but manual testing catches UX issues that unit tests miss.

Unclear Success Metrics

The problem: Week 8 arrives and the team doesn’t know if they’ve succeeded. Is 3 customers a win? Is 20? Is 50%?

The fix: Define success metrics in week 1. Write them down. Share them with the team. “We succeed if we have 10 paying customers by end of week 8.” Clear. Measurable. Motivating.

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Post-Launch: Scaling Beyond Week 8

If you’ve hit week 8 with a working product and paying customers, congratulations. Now the real work begins.

Week 9–12: Iteration Based on Feedback

Your first customers will tell you what’s broken and what’s missing. Listen. Prioritise ruthlessly:

1. Fix bugs that block usage
2. Add features that 80%+ of customers request
3. Improve UX where customers struggle
4. Ignore requests that only one customer makes (for now)

During this phase, you’re not building new products. You’re improving product-market fit. Measure weekly:

• Activation rate (target: 80%+)
• Retention rate (target: 50%+ returning in week 2)
• Expansion revenue (customers upgrading to paid tiers)

If any of these is below target, fix the product before adding features.

Week 13–16: Revenue & Growth

Once product-market fit is evident (high activation, high retention, customers willing to pay), focus on revenue:

1. Pricing: Test different price points. If customers aren’t price-sensitive, you’re undercharging.
2. Sales: Reach out to 50 similar companies. How many convert to customers?
3. Retention: Why do customers churn? Fix it.
4. Expansion: What features would make customers pay more?

At this stage, you might bring in a fractional CTO or venture studio partner to handle technical scaling while you focus on sales and product.

Scaling the Engineering Team

If you’re approaching $10k MRR and need to ship faster, hire or contract. A CTO as a Service provider can help you:

• Design scalable architecture
• Hire the right engineers
• Build repeatable processes
• Plan for compliance (SOC 2, ISO 27001) if you’re selling to enterprise

Don’t hire too early. One engineer can maintain a product with 100–1,000 users. Hire when you have product-market fit and revenue to justify the cost.

Planning for Compliance

If your product handles sensitive data or you’re selling to mid-market companies, you’ll eventually need SOC 2 or ISO 27001 compliance. Start thinking about this in week 12–16, not week 52.

A security audit readiness process via Vanta typically takes 8–12 weeks. If you want to be audit-ready by month 6, start in month 4. Document your security practices now, and the audit is straightforward. Leave it to the end, and you’ll scramble.

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When to Bring in a Venture Studio Partner

Not every founder should run an 8-week sprint alone. Some have the expertise and bandwidth. Others don’t. Here’s when a venture studio partner adds value:

You Should Partner If:

• You’re non-technical: You have a domain expertise (healthcare, fintech, supply chain) but no CTO. A venture studio provides fractional CTO leadership and co-build support.
• You need speed: You’re in a competitive space and need to launch in 8 weeks, not 12. A venture studio has processes and tools to compress timelines.
• You need credibility: You’re fundraising and need technical validation. A venture studio’s involvement signals that your idea is technically feasible.
• You need compliance: You’re selling to enterprise and need SOC 2 or ISO 27001 audit-readiness. A venture studio can architect for compliance from day one.
• You’re building AI-heavy products: You need expertise in prompt engineering, AI orchestration, and agentic workflows. A venture studio specialising in AI can accelerate this.

You Should Go Solo If:

• You have a strong technical co-founder: You can move fast without external help.
• You have limited capital: You can’t afford to pay for a venture studio and want to preserve runway.
• You’re in a non-competitive space: You have time to iterate and learn.

What a Venture Studio Brings

At PADISO, we’ve built the 8-week sprint into our venture studio and co-build service. Here’s what we provide:

• Week 1–2: Problem discovery facilitation, customer interview coaching, scope definition, tech stack recommendation
• Week 3–4: Design review, architecture review, API specification validation
• Week 5–6: Code review, pair programming on complex features, AI integration support
• Week 7: QA coordination, beta testing facilitation, prompt refinement
• Week 8: Launch planning, customer onboarding, metrics setup

We also help with post-launch scaling: hiring, architecture decisions, compliance planning, and fundraising support.

Our AI & Agents Automation service is particularly relevant for founders building AI-powered products. We help with:

• AI strategy: Which AI models and APIs are right for your use case?
• Prompt engineering: How do you get Claude or GPT to do what you need?
• AI orchestration: How do you chain multiple AI calls into a coherent workflow?
• Agentic AI: How do you build AI agents that take actions autonomously?

We also provide platform engineering and custom software development to handle the non-AI parts of your stack.

If you’re serious about launching in 8 weeks and want expert guidance, reach out.

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Key Takeaways: Building an MVP in 8 Weeks

Here’s what separates teams that ship in 8 weeks from teams that ship in 16:

1. Scope discipline: Lock scope in week 2. Cut ruthlessly. Launch with 20% of features, not 80%.
2. Customer obsession: Talk to customers in week 1, week 4, and week 7. Let them shape the product.
3. Daily communication: Stand-ups, Slack, pair programming. Blockers get unblocked same-day.
4. AI leverage: Use Claude for code generation, documentation, and prompt engineering. Save 20–30 hours per sprint.
5. Clear success metrics: Define what “done” looks like before you start building. Measure weekly.
6. Ruthless prioritisation: Bugs and features that block core flow get fixed. Everything else waits.
7. Post-MVP roadmap: Know what you’ll build in weeks 9–16 before you launch. This prevents decision paralysis.

The 8-week sprint isn’t a race. It’s a discipline. It forces you to validate your hypothesis quickly and cheaply. If you fail, you fail fast. If you succeed, you have paying customers and momentum heading into fundraising or scaling.

Most founders underestimate what’s possible in 8 weeks when the team is focused and the scope is tight. Push yourself. You’ll be surprised.

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Next Steps

If you’re planning an 8-week MVP sprint:

1. This week: Schedule customer discovery interviews. Aim for 5–10 by end of week.
2. Next week: Lock scope. Create a feature list and post-MVP backlog.
3. Week 3: Hire or contract your engineering team (if you don’t have one). Choose your tech stack.
4. Week 4: Start design and architecture work.
5. Week 5: Begin building.

If you’re non-technical or in a competitive space, consider partnering with a venture studio. PADISO has run dozens of these sprints with founders at seed and Series A stages. We can help with fractional CTO leadership, co-build support, AI integration, and post-launch scaling.

For more on venture studio methodology, check out our AI agency methodology and AI agency project management guides. We also publish insights on AI agency growth strategy, AI agency pricing strategy, and AI agency scaling.

The 8-week sprint works. We’ve proven it dozens of times. The question is: are you ready to commit to scope discipline and ship?

Let’s build.