AI MVP & Rapid Prototyping

An AI MVP (Minimum Viable Product) is a lean, focused version of your AI product idea designed to validate one key assumption: do users want this? Does it work reliably? Can we acquire customers cost-effectively?

You should start with an MVP because:

Speed to market: Validate ideas in 4-8 weeks instead of 6-12 months. Get real user feedback before investing heavily.

Risk reduction: Discover technical obstacles, data challenges, and market fit issues early when they are cheap to fix.

Focused scope: MVP forces you to prioritise. Which one AI feature creates the most value? That is what you build first.

Cost control: MVP budgets are $40K-$80K versus $150K-$300K for a full product. You prove ROI before making bigger bets.

Data insights: Real user interaction with your AI features teaches you what works. You then scale with confidence, not guesses.

The teams that win with AI start lean, learn fast, and iterate based on real feedback. MVP is not cutting corners - it is being strategic about which corners matter.

AI MVP pricing depends on scope and technical complexity:

AI Feasibility Assessment: $8K-$15K - Structured 2-week review of your idea, technical options, data requirements, and realistic timeline. Output: Clear go/no-go recommendation and cost estimate.

Proof of Concept (4-6 weeks): $40K-$80K - Validate one core AI feature with real users. Typically uses existing models/APIs (OpenAI, Google, Anthropic). Narrow scope, focus on the riskiest assumption.

AI MVP (market-ready): $80K-$180K - Full product you can put in front of customers. Refined UX, basic monitoring, production infrastructure. Typically 10-14 weeks.

MVP to Production Transition: $150K-$300K+ - Scale your validated MVP to handle 10x users. Optimise costs, improve reliability, add features validated during MVP phase. 8-12 weeks.

Factors that affect cost: Custom model training (much more expensive than using APIs), data preparation (often 30-40% of the work), and infrastructure complexity (some AI requires specialised infrastructure, others run on standard servers).

We provide transparent estimates after discovery so you know exactly what you are investing.

Proof of Concept: 4-6 weeks. We validate one core assumption with real users. Scope is intentionally narrow. Uses existing AI models/APIs.

AI MVP: 10-14 weeks. Full product with refined experience, basic monitoring, production deployment. Market-ready, not just a prototype.

Rapid AI Prototype (technical feasibility only): 2-3 weeks. If you just need to know "can we build this?", we build a throwaway prototype that proves technical feasibility without polishing for production.

Speed is possible because we ruthlessly cut scope. An MVP is not "the full product minus 20%". It is "the 20% of features that prove 80% of the value". We identify exactly what you need to learn, build that, and ignore everything else.

The biggest variable is your availability. We need you or your team to review, validate assumptions, and make decisions quickly. Projects that stall are ones where feedback loops are slow.

Vibe coding is building fast without tests, monitoring, or error handling. You prompt the AI (or use AI tools), iterate quickly, and ship rough working code. Useful for prototypes. Dangerous for production.

Vibe coding is appropriate when: You are discovering whether something is technically possible. You have no real users yet. You plan to throw away the code and rebuild. Timeline: 1-2 weeks max.

Vibe coding is dangerous when: You ship it as a real product to real users. Money is involved. Failures harm users. Compliance is required. You plan to maintain and scale the code.

Here is our rule: We use AI-accelerated development during rapid prototyping (which includes some vibe coding), but we transition to engineered code before production. Your MVP must be production-ready - not polished like a mature product, but built to standards so it survives real usage and scales.

Many founders want "vibe coded" MVPs because they are faster to build. We argue for spending 2-3 extra weeks adding basic tests, error handling, and monitoring. When users hit your app and something breaks, they do not care if you were "just prototyping". You lose credibility and data.

We know the difference. Most AI agencies do not.

Validation happens in three stages:

1. Technical Feasibility (2 weeks): We build a rough prototype to answer: Can we actually build this with current AI technology? Do we have the data? Is latency acceptable? What infrastructure do we need?

2. Product/Market Fit (4-6 weeks MVP): We put a polished version in front of real users. Do they engage? Do they see value? Would they pay for this? Which features matter most? This is where most AI ideas fail - not because the technology does not work, but because users do not care.

3. Economics (post-launch): Can you acquire customers cheaper than the value you create? Can you scale without losing money? Does the AI feature justify its operational cost? These answers come from real usage data.

We measure ruthlessly at every stage. During MVP, we track: engagement (do users interact with the AI feature?), accuracy (does it work as intended?), latency (is it fast enough?), and retention (do people come back?). Real metrics, real users, real feedback.

The biggest validation is seeing users repeatedly choose your AI feature over the manual alternative.

Your MVP proved the idea works. Now production is about scaling three things: volume, reliability, and cost.

Volume: Your MVP might handle 1,000 users. Production needs to handle 10,000 without degrading. This means database optimisation, caching, load balancing, and sometimes model serving infrastructure.

Reliability: MVP can fail occasionally - you can call users and fix it. Production failures cost you customers. We add monitoring, fallback systems, and automatic recovery. If the AI model fails, we have graceful degradation (faster rule-based logic, human escalation).

Cost: Your MVP might call an AI API $100 per user per month. Production requires optimisation: batching calls, caching results, using cheaper models for routine tasks, fine-tuning your own models for better accuracy (fewer API calls).

The transition project: Usually 8-12 weeks, $150K-$300K+. We take your validated MVP, refactor for scale, optimize infrastructure and costs, add comprehensive monitoring, and prepare deployment.

Our approach: You run the MVP and gather real usage data for 2-4 months. That data tells us exactly what to optimise. We then make targeted improvements instead of guessing.

Yes - and we almost always start this way.

For most MVP phases: Use existing models. OpenAI GPT-4, Anthropic Claude, Google Vision, AWS Rekognition. These are production-grade, well-documented, and cheaper than building custom models. You validate your idea without months of data science work.

Custom models come later: Only build custom models when you have proved the need. Maybe your users want something the general models cannot do. Maybe you have proprietary data that creates competitive advantage. Maybe you need cost optimisation (your own model is cheaper at scale than API calls).

Typical progression: Weeks 1-4, use APIs and existing models to prove value. Weeks 5-10, if it works, explore whether custom models would improve accuracy or reduce cost. 6 months later, if the product scales, invest in custom model development.

This is the smart path. You validate quickly, cheaply, with minimal risk. Only technical teams building "important" projects fall into the trap of custom model development before validating the idea.

We have seen it a hundred times: founder spends 6 months building a custom ML model, releases to users, discovers they do not want the feature. Using existing models, they would have known in 6 weeks.

It depends on the type of AI feature:

Large Language Model (LLM) features: Chat, content generation, analysis - you need zero proprietary data. GPT-4 and Claude are already trained on billions of examples. You build immediately.

Computer Vision features: Image recognition, document processing - if you use Google Cloud Vision or AWS Rekognition, you need zero training data. If you want custom models, you need hundreds to thousands of examples.

Recommendation systems: Predict what users will like - you need user behaviour data. But you do not need much to start. Even 1,000 user interactions can power a basic recommendation engine. You build the system ready to improve as data accumulates.

Classification models: Spam detection, quality scoring - you need examples of both categories. Minimum 500-1,000 examples per category to train a useful model. We help you assess whether you have enough.

During discovery, we audit what data you have and what data you need to collect. Often you have more data than you think - buried in your database, in logs, in user behaviour patterns.

The conversation is never "we cannot start without data". It is "here is what we can build today with your existing data, and here is what we need to collect to improve over time".