AI Rapid Prototyping: From Idea to MVP in 4 Weeks.
Validate your AI concepts before investing millions. We build functional, testable prototypes using agile methodologies to prove ROI fast.
Our Clients
Fail Fast, Scale Faster.
Traditional software development cycles (months/years) kill AI innovation. By the time you ship, the model is obsolete.
Our Rapid Prototyping Framework focuses on "Good Enough" architecture to validate user value, technical feasibility, and business impact immediately.
The 4-Week Sprint
A structured path to your first MVP.
Discovery & Data
We define the use case, audit available data, and select the best-fit foundation model based on your performance, cost, and privacy requirements, whether that's OpenAI, Mistral, Meta's LLaMA, or others. Data Audit, Tech Selection
Pipeline Build
Engineers build the RAG pipeline, set up the vector database, and perform initial prompt engineering tests.
Pinecone Setup, LangChain
UI & Integration
We wrap the logic in a simple UI (Streamlit/Vercel) and connect it to your sandbox environment for internal testing.
Frontend, API Connection
Demo & Roadmap
Executive demo day. We present the working prototype, gather user feedback, and outline the path to production.
MVP Delivery
Built with Modern Accelerators
We leverage low-code and rapid application development tools to focus on logic, not boilerplate.
Streamlit
Instant Data UIs
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Flowise
Drag & Drop LLM Chains
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Vercel AI SDK
React Streaming
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Replit
Collaborative Coding
Learn MorePrototyping Wins
Real results from rapid experimentation.
Clinical Trial Search
- Challenge: Doctors spent hours searching PDF protocols.
- Prototype: A RAG-based chat interface.
- Result: Validated search accuracy of 92% in 4 weeks.
Contract Summarizer
- Challenge: Paralegals drowning in paperwork.
- Prototype: An upload-and-summarize tool.
- Result: Reduced review time by 70%, greenlit for full dev.
Shopping Assistant
- Challenge: Low conversion on e-commerce site.
- Prototype: A personalized recommendation bot.
- Result: 15% uplift in conversion during A/B testing.
Maintenance Request Classifier
- Challenge: Facility teams drowning in daily work order backlogs.
- Prototype: NLP-based auto-classification and priority-routing system.
- Result: 65% faster triage, live across 3 enterprise clients in 4 weeks.
Frequently Asked Questions
How long does it take to build an AI prototype with Folio3?
Our standard sprint runs 4 weeks, structured across four phases: Discovery & Data, Pipeline Build, UI & Integration, and Demo & Roadmap. Some prototypes with well-defined use cases and clean data have been delivered in as little as 3 weeks.
What do I need to have ready before starting a prototyping sprint?
You need a clear problem statement and access to relevant data, even if it's unstructured. We handle the tech selection, model evaluation, and pipeline setup. Our Discovery phase is specifically designed to audit what you have and identify any gaps before we build.
Which AI models and technologies do you use for prototyping?
We select the best-fit foundation model based on your performance, cost, and privacy requirements, whether that's OpenAI, Mistral, Meta's LLaMA, or others. On the infrastructure side, we commonly use LangChain, Pinecone, Streamlit, Flowise, and the Vercel AI SDK depending on the use case.
What does the prototype actually deliver at the end of 4 weeks?
You get a functional, testable MVP with a working UI, a connected data pipeline, and an executive demo. We also deliver a production roadmap outlining the effort, architecture, and cost required to scale the prototype into a full product.
How much does an AI prototyping sprint cost?
Pricing depends on the complexity of the use case, data readiness, and integrations required. Contact us for a scoping call and we will provide a fixed-scope quote tailored to your project within one business day.
What happens after the prototype is built?
The prototype is yours. Many clients use the demo day output to secure internal buy-in or board approval for full development. If you choose to move forward with us, we transition seamlessly into full-scale AI development with the same team that built your prototype.
