AI Proof of Concept (PoC) &
MVP

We narrow down your vision to a single, testable hypothesis. We define exactly what the PoC needs to achieve (e.g., 85% accuracy in ticket classification) to be considered a success.

AI is only as good as its data. We request a sample of your actual data (documents, database exports) and clean it to ensure it's suitable for the prototype model.

We build the core AI logic using off-the-shelf APIs (like OpenAI) or pre-trained open-source models (like Hugging Face) to move fast. The focus is on functionality over perfect optimization.

A PoC needs to be tangible. We build a lightweight interface—often using Streamlit, Gradio, or Next.js—so non-technical stakeholders can interact with the AI directly.

We test the prototype against the success criteria defined in Step 1. We measure the AI's accuracy, response time, and the token cost per interaction.

We present the working MVP alongside an executive report. We provide a definitive Go/No-Go recommendation and a detailed roadmap and budget for the production-grade application.