Stop Talking About AI. Start Building It.
1-4 week sprint delivering a working proof of concept that answers "Will this AI solution actually work for us?"—with validated hypothesis, feasibility assessment, and clear path to production.
From Concept to Working Code
You have a specific AI idea. Maybe it's automating document analysis, building a recommendation engine, or creating an intelligent chatbot. You've done enough research to know it's theoretically possible—but you need to know if it's practically viable for your specific use case before committing to full development.
The Rapid Prototype service takes your hypothesis from concept to working code in 1-4 weeks. You'll get a functioning proof of concept built on your actual data (not synthetic examples), tested against your real-world constraints, and validated with your stakeholders. More importantly, you'll understand what works, what doesn't, and why—so you can make informed decisions about production investment.
We build using production-quality patterns from the start: proper error handling, security controls, and scalable architecture. This isn't a throwaway demo—it's a foundation you can build on. And because we use AI-augmented development (our specialized AI agent system accelerates coding, testing, and documentation), we deliver in weeks what traditional development would take months to produce.
What You Get
✓Working Proof of Concept
Functional prototype built on your actual data demonstrating core AI capabilities—not slides or mockups, but working software you can interact with and test against real scenarios.
✓Technical Feasibility Assessment
Documented analysis of what worked, what didn't, and why—including data quality findings, model performance metrics, integration challenges discovered, and technical risks identified during development.
✓Production Readiness Roadmap
Clear path from prototype to production: architecture recommendations, required infrastructure, data pipeline requirements, scaling considerations, and estimated timeline and budget for full implementation.
✓Validated Hypothesis Documentation
Evidence-based conclusions on your original hypothesis: which assumptions proved correct, which required adjustment, and which use cases delivered strongest results during prototype testing.
✓Source Code and Documentation
Complete codebase with inline documentation, setup instructions, and architectural decisions recorded—so your team can understand, modify, and extend the prototype independently.
✓Live Demonstration and Handoff
Working session where we demonstrate the prototype, explain technical decisions, answer your team's questions, and provide recommendations for next steps—whether that's iteration, production build, or pivot to alternative approach.
Example Scenarios
Retailer Needing AI-Powered Product Recommendation Engine
An online home goods retailer wanted personalized product recommendations to increase average order value but didn't know if their purchase history data would support collaborative filtering algorithms. The Rapid Prototype built a recommendation engine using 18 months of transaction data, tested against current customer sessions, and compared performance to their existing "frequently bought together" rules.
Key Outcome:
Prototype demonstrated 23% lift in cross-sell conversion; identified data gaps in product taxonomy; validated feasibility; production implementation launched 90 days later.
Law Firm Building Document Analysis Prototype
A mid-sized law firm wanted AI to extract key clauses from commercial contracts but wasn't sure if general-purpose LLMs would handle their specific contract language. The Rapid Prototype tested three approaches—fine-tuned model, prompt engineering with GPT-4, and hybrid rule-based extraction—using 200 sample contracts from their archives.
Key Outcome:
Hybrid approach achieved 94% accuracy on clause extraction; identified 12 contract types requiring custom handling; firm avoided $200K custom ML development by using LLM + rules approach.
Property Management Company Prototyping Tenant Communication Bot
A property management company handling 2,000+ units wanted an AI chatbot for tenant inquiries but needed to validate it could handle their specific communication patterns and integrate with their property management system. The Rapid Prototype built a conversational AI interface connected to their tenant database, tested with 50 common inquiry types, and measured response accuracy.
Key Outcome:
Bot handled 68% of inquiries without human intervention; identified 8 inquiry types requiring human handoff; validated Twilio integration approach; full deployment reduced response time from 4 hours to 3 minutes.
Content Publisher Building Automated Curation Prototype
A digital media company wanted AI to curate personalized content feeds for their subscriber base but needed proof their engagement data could train effective recommendation models. The Rapid Prototype built a content recommendation engine using 6 months of reading behavior, A/B tested against their current editorial curation, and measured click-through and time-on-site metrics.
Key Outcome:
AI curation increased engagement by 31% vs. editorial picks; revealed content category gaps; identified three subscriber segments with distinct preferences; production system launched with hybrid AI + human curation approach.
How It Works
Hypothesis Definition Workshop (Day 1-2)
We start with a structured working session defining your AI hypothesis, success criteria, and constraints. You'll describe the problem you're solving, the data you have available, and the outcomes you're targeting. We'll ask hard questions about edge cases, failure modes, and integration requirements to ensure we're building the right prototype.
Timeline: 2-4 hours working session + documentation
Rapid Development Sprint (Week 1-3)
We build the prototype using AI-augmented development: our agent system accelerates coding, generates test cases, and produces documentation. You'll see working increments every 2-3 days with opportunities for feedback and course correction. This isn't waterfall—it's iterative development compressed into weeks instead of months.
Timeline: 1-3 weeks depending on complexity
Real-World Testing & Validation (Final Week)
We test the prototype against your actual data and real-world scenarios—not synthetic test cases. You'll validate behavior with your stakeholders, we'll measure performance against success criteria, and we'll identify failure modes and edge cases that need handling in production.
Timeline: 3-5 days of testing and iteration
Findings Documentation & Handoff
We document what we learned: technical findings, architectural decisions, performance metrics, and production roadmap recommendations. You receive complete source code, setup instructions, and a live demonstration walking through the prototype's capabilities and limitations.
Timeline: 2 days for documentation + 90-minute handoff session
Post-Prototype Consultation (30 Days)
Thirty days after handoff, we schedule a follow-up session to answer questions that emerged during your internal review, provide guidance on production planning, and recommend refinements based on stakeholder feedback.
Timeline: 60-minute consultation at day 30
Is This Right For You?
This service is ideal if you...
- →Have a specific AI use case and need validation with working code — You're past the "should we explore AI?" phase and need "will this specific solution work for us?" answered with a functioning prototype.
- →Want proof before committing to full AI implementation — You need to demonstrate feasibility to stakeholders, validate technical approach, and de-risk production investment with evidence from a working system.
- →Have data available but don't know if it's sufficient for AI — You suspect your data could support AI but need validation: Is there enough? Is quality adequate? Will it train effective models?
- →Need fast iteration to test AI hypotheses — You want to fail fast or succeed fast—prove the concept works in weeks rather than debating possibilities for months.
Consider a different service if you...
- →Haven't yet identified your AI opportunity — The AI Strategy Session helps you discover and prioritize AI use cases before investing in prototype development.
- →Need comprehensive assessment of operations or technology — The AI-Augmented Assessment provides deeper analysis of your full business context, not just a single AI use case.
- →Are ready to build production AI systems — If you're past prototyping and need ongoing implementation support, Ongoing Advisory provides continuous guidance for production AI development.
Related Services
AI Strategy Session
Half-day to full-day engagement delivering AI opportunity assessment, readiness evaluation, and prioritized roadmap—ideal for identifying which AI initiatives to prototype.
$2,500–$5,000
When to choose this instead: You have multiple potential AI opportunities and need help prioritizing which to pursue first.
Learn More →AI-Augmented Assessment
Comprehensive analysis of operations, technology, or strategic decisions using our AI agent system—ideal when AI is one component of broader organizational challenges.
$10,000–$25,000
When to choose this instead: You need holistic evaluation of business operations or technology landscape, not just validation of a specific AI solution.
Learn More →Ongoing Advisory
Continuous AI-augmented guidance for organizations actively implementing AI, with month-to-month commitment after initial 90 days.
$5,000–$15,000/month
When to choose this instead: You're building multiple AI capabilities and need ongoing expert partnership beyond a single prototype engagement.
Learn More →Build Your AI Proof of Concept
Stop debating possibilities. Get a working prototype that validates your AI hypothesis with real data, real code, and real results—delivered in 1-4 weeks.