Need an AI Proof of Concept (PoC)? 

Why do you need a Proof of Concept (PoC)?

A Proof of concept can significantly help you de-risk the initiative and confirm whether the business idea can be effectively achieved using Artificial Intelligence. These Proof of Concept engagements are small in scope, affordable and require much less human and financial resources.

How long does it take to implement a AI Proof of Concept (PoC)?

A typical AI Proof of Concept engagement takes only couple of weeks. The duration varies based on complexity (data availability & quality, machine learning, deep learning, neural network, computer vision, conversational AI etc.) and use case scenario (hypothesis, scenario, stakeholders, value creation etc.).

 

To implement the AI Proof of Concept, we work closely with you on:

 

  1. Stakeholder commitment: Engagement and alignment of key stakeholders.

  2. Business use case: Identification & definition of business use case and hypothesis.

  3. Data readiness: Identify data sources. Proxy data or Historical/Live data availability.

  4. Selection of AI model: Machine Learning, Deep Learning, Neural Network, Generative Adversarial Network, Convolution Neural Network, Pose Estimation, Haar Cascade, Image Classification, Custom Object Detection, Custom Object Tracking, Computer Vision, Conversational AI.

  5. Develop the AI Proof of Concept.

Key Objective for a Proof of Concept (PoC) engagement​

To determine feasibility of the Artificial Intelligence business use case and its success.

Key Outcomes from the engagement

A working Proof of Concept of the Artificial Intelligence model with key observations, learning, and outcomes. Timeline and effort estimates to implement a production ready model.

Got an idea for AI Proof of Concept? Let's connect.
Turn your idea into reality.

Thanks! Looking forward to connecting with you shortly.

 Methodology for developing AI Proof of Concept

  1. Define clear use case and scope for the proof of concept.

  2. Define hypothesis for the use case and expectation.

  3. Identify data that will be used to train model.

  4. Identify the model(s) and possible deployment scenario (cloud / on-prem/ edge)

  5. Develop the model, train the model, validate the model.

  6. Develop and present observations, learning, outcomes, pros, and cons.

  7. Timeline and effort estimates for full-fledge production model.