Discover your idea success

Have a better picture of your project and idea.

Our Discovery Roadmap

Our seven key steps to making your emerging technologies Proof of Concept a success:

1. Identify the business problem.

To be successful, a computer vision project should have a clear business goal and benefit. We are able to describe the goal and benefit in one to two sentences, in order to set a clear crystal mindset for everyone.

2. Define the success criteria.

The goal here is to translate the business outcome into simple success criteria that can be used to measure the effectiveness of the solution.

3. Determine the computer technique.

We identify the right techniques up front will clarify the data requirements and enable our team to focus during the execution phase.

4. Collect, label and training test data.

If the scenario is not focused on one of our more than +30 pre-trained models ready to use, we will need to collect and label data to train a new model.

5. Train and evaluate model.

Once we have a good set of images labeled we are ready to shift to model training. In transfer learning, a pre-trained model is repurposed for a new scenario.

6. Deploy, Test and Iterate on the solution.

With the model deployed, we are ready to interact with the new model in a real-world environment. At this point, we start to solicit feedback. Developing a machine learning model is an iterative process of trial and error. If the model is not performing well, there are many steps we can take to improve performance.

7. Gather and document feedback.

The gathered feedback lets us verify the usability and feasibility of the solution. It also informs of any needed improvements to the proposed product and gives significant insight for other relevant actions moving forward.

1. Identify the business problem.

To be successful, a computer vision project should have a clear business goal and benefit. We are able to describe the goal and benefit in one to two sentences, in order to set a clear crystal mindset for everyone.

2. Define the success criteria.

The goal here is to translate the business outcome into simple success criteria that can be used to measure the effectiveness of the solution.

3. Determine the computer technique.

We identify the right techniques up front will clarify the data requirements and enable our team to focus during the execution phase.

4. Collect, label and training test data.

If the scenario is not focused on one of our more than +30 pre-trained models ready to use, we will need to collect and label data to train a new model.

5. Train and evaluate model.

Once we have a good set of images labeled we are ready to shift to model training. In transfer learning, a pre-trained model is repurposed for a new scenario.

6. Deploy, Test and Iterate on the solution.

With the model deployed, we are ready to interact with the new model in a real-world environment. At this point, we start to solicit feedback. Developing a machine learning model is an iterative process of trial and error. If the model is not performing well, there are many steps we can take to improve performance.

7. Gather and document feedback.

The gathered feedback lets us verify the usability and feasibility of the solution. It also informs of any needed improvements to the proposed product and gives significant insight for other relevant actions moving forward.

Have a better picture of your project and idea

When you convert your idea into words in our POC Template, you’re reviewing your entire concept and finding issues that you wouldn’t uncover otherwise.  This process also helps you slow down your thoughts and think more clearly about what needs to be done.

Quick Feedback

This is ultimately the most valuable benefit out of a POC. The feedback you will gather will help fine-tune your idea. It will also help you understand if you found a gem.

Save your money

A POC helps you save money in two ways:

  • It lets you verify if your idea is feasible before putting any significant amount of money into building an MVP.
  • By setting the ground for the subsequent phases. It helps clarify the Scope so that you don’t spend money on the features and functionalities that aren’t required.

The 5 reasons why we should face a proof of concept on emerging technologies

Proof of Concept allows for testing and validation

A Proof of Concept (POC) allows for the testing and validation of an emerging technologies project. This helps to identify any potential issues that may arise and ensures that the project is feasible.

Reduces development time

POC helps to streamline the development process and reduce development time. This is because any problems are identified early on and can be addressed, ensuring that the project is completed within the desired timeframe.

Demonstrates the value of the project

A POC provides a demonstration of the value of the project to stakeholders and helps to secure funding and support. This is because it provides tangible evidence of the potential benefits of the project.

Minimizes risks

POCs minimizes the risks associated with emerging technologies projects. This is because problems are identified early on and can be addressed, reducing the chances of the project failing.

Offers cost-effectiveness

The emerging technologies POCs provide profitability by allowing companies to evaluate the value and potential of this technology before investing on a large scale, minimizing risks and optimizing resources based on the results obtained in the testing phase.

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