Machine Learning Proof of Concept

Proof of concept is a fast, reliable and cost-effective way of the feasibility of your machine learning use case. Drive business decisions with solutions validated by your data even in four weeks.

Start Machine Learning Proof of Concept with us

In the shortest possible scope of time, you seek precise and reliable information to make business decisions about product or service development. You don’t have months and an unlimited budget for futile failures.

Your question is whether it is possible to apply a machine learning to your business use case to either increase your revenue or optimize costs.

The goal of our service is to answer that question. By applying the state-of-the-art AI algorithms to your data, we assess the feasibility of creating a system that can generate valuable insights and decisions.

Machine Learning proof of concept gives you a clear view of the possible next steps to build a fully operational machine learning system. You will use that information in your business or attract investors.

What is Machine Learning Proof of Concept

It’s a research and feasibility assessment of a Machine Learning use case using your data, including the delivery of a Proof of Concept machine learning solution. It is the first step on the road to apply AI to deliver innovation and business value at reduced costs in many areas.

During the time of our engagement, we deep dive into your use case, analyze your goal, choose the possible state-of-the-art methods and algorithms. We also assess whether the data you are currently collecting is sufficient to create a robust and effective solution.

After four weeks you will receive:

  • Feasibility report – to get the knowledge of what you can and what you can’t do.
  • Trained ML model – so you can reproduce the results in your environment.
  • Visualization of PoC results – plot and graphs can give a clear view of the results
  • A set of recommendations about following possible steps – we want to provide you with the tips of what you should or should not do next.

Where to start with Machine Learning Proof of Concept

Schedule a call with us! During the 30-45 min conversation, we discuss the business context, the goal of the research, and briefly talk about the data you have gathered. As a result, we preliminarily:

  • Assess whether machine learning will help you to achieve your business use case goals
  • Set the scope of the project
  • Provide you info if your data might be useful for machine learning algorithms.
  • Establish a possible project kick-off date.

After the call, we send you an offer upon which we will discuss the contract details.

Schedule a call with a specialists

Key activities

Use Case Deep Dive

A profound understanding of your environment and challenges is mandatory to figure out how to apply machine learning. That’s why cooperation with your SME during the whole project is essential.

Data exploration and preparation

The exploration and understanding of the provided data are crucial to pick the machine learning algorithms that fit the most.

Modelling

We test and evaluate a bunch of different AI solutions to choose the one that suits with your goal and your data best.

Final report

The last step is about creating a final report, recommendations and visualizations. All of the documents, as well as the code, is now delivered to you.

Machine Learning Proof of Concept Timeline

timelinenew

Machine Learning Proof of Concept Timeline

timeline_mobilenew

Use cases of Machine Learning

Revenue increase in Retail

A wholesale footwear distributor wanted to maximize the revenue by ordering the right amount of products considering its styles and sizes. Every year available fashion styles vary and orders need to be placed six months ahead. The solution required to forecasts on historical data and predict new models, not seen before.

We decided to use a Convolutional Neural Network to extract information from products’ images and find the footwear with comparable appearance features, historical sales data and use the forecasting model prepared for it to estimate the new product’s demand. The solution increased revenue and reduced costs of stock. Accuracy of predictions is higher each year with an increasing amount of data.

Reduced complains in Manufacturing

The manufacturer of precise, high-quality electronic components due to increasing, production volume needed to detect defects faster and more precisely than ever before. An automated video inspection system to monitor the semi-finished products was required. Precision-time trade-off, in the case of humans, was unacceptable.

After finding best cameras setups for quality of data, we designed a data pipeline to acquire the video signal from the cameras, preprocess it and provide it to the model. We used the convolutional neural network to segment the objects of interest from the images. With the second deep learning model, we detect and mark any defects on products’ surfaces. The solution reduced the number of malfunctioning end products and complaints.|

Increase safety in Manufacturing

The manufacturing company with many factories employs thousands of workers. With the growth of production and employment, safety supervision became hard. It required lots of people to monitor the CCTV records constantly or checking archive records which were without any tags. No one could define all unwanted events that may occur.

We built a state-of-the-art models base on GANs (Generative Adversarial Networks) which takes into account objects appearance and motion patterns. Solution enhanced safety issues in the workplace with faster, a more directed reaction in case of an unwanted event. It also allowed allocating people time to other, more creative tasks.

Optimized process in the Energy sector

The company aimed to gather sensor data and perform improved analysis using cloud-based tools. A middleware service that would provide a robust and scalable integration of a self-contained on-premise solution and a cloud platform was needed.

We designed and implemented a connector service. The solution features data gathering, monitoring, querying, and out-of-the-box visualization. Thanks to the flexible architecture, more sophisticated use of data become possible if needed.

Use cases of Machine Learning

Revenue increase in Retail

A wholesale footwear distributor wanted to maximize the revenue by ordering the right amount of products considering its styles and sizes. Every year available fashion styles vary and orders need to be placed six months ahead. The solution required to forecasts on historical data and predict new models, not seen before. We decided to use a Convolutional Neural Network to extract information from products’ images and find the footwear with comparable appearance features, historical sales data and use the forecasting model prepared for it to estimate the new product’s demand. The solution increased revenue and reduced costs of stock. Accuracy of predictions is higher each year with an increasing amount of data.

Reduced complains in Manufacturing

The manufacturer of precise, high-quality electronic components due to increasing, production volume needed to detect defects faster and more precisely than ever before. An automated video inspection system to monitor the semi-finished products was required. Precision-time trade-off, in the case of humans, was unacceptable.

After finding best cameras setups for quality of data, we designed a data pipeline to acquire the video signal from the cameras, preprocess it and provide it to the model. We used the convolutional neural network to segment the objects of interest from the images. With the second deep learning model, we detect and mark any defects on products’ surfaces. The solution reduced the number of malfunctioning end products and complaints.|

Increase safety in Manufacturing

The manufacturing company with many factories employs thousands of workers. With the growth of production and employment, safety supervision became hard. It required lots of people to monitor the CCTV records constantly or checking archive records which were without any tags. No one could define all unwanted events that may occur.

We built a state-of-the-art models base on GANs (Generative Adversarial Networks) which takes into account objects appearance and motion patterns. Solution enhanced safety issues in the workplace with faster, a more directed reaction in case of an unwanted event. It also allowed allocating people time to other, more creative tasks.

Optimized process in the Energy sector

The company aimed to gather sensor data and perform improved analysis using cloud-based tools. A middleware service that would provide a robust and scalable integration of a self-contained on-premise solution and a cloud platform was needed.

We designed and implemented a connector service. The solution features data gathering, monitoring, querying, and out-of-the-box visualization. Thanks to the flexible architecture, more sophisticated use of data become possible if needed.

Top 4 reasons you get high quality results

01

Customers Customer

While delivering solutions to you, we do keep in mind your customers to add more business value.

02

Transparent communication

Inconvenient truth from the beginning lets us achieve better results at the end.

03

Agility

We review production cycles in short intervals for quick and appropriate decisions taking your voice into account.

04

Be LEAN

We remove waste on your projects, because of building-measure-learn process and using validated learning to adjust course.

We are trusted and recognized partner

PARTNERS WITH
AWARDS
1st

in Poland

14th

in Central Europe

Partners of Azure, GCP, Databricks, Datastax
 Over 8 Machine Learning engineers trained in Deep Learning
20% of workforce dedicated to research
Top academia with internal R&D and PhD’s on-board
Over 40 people in Warsaw, Poland

Technology Stack

Machine Learning Proof of Concept FAQ

How much does Machine Learning Research as a Service cost?

Typically, it’s $15000 for the four weeks engagement, but the cost may vary on the project scope. Also, with your consent, we can broaden the scope of the analysis during the project.

How long does it take for Semantive to deliver results?

It takes four weeks, and we establish the scope of the project to be feasible at that time. We will specify more details with you at the call.

How reliable is the Proof of Concept results?

We are applying the state-of-the-art machine learning algorithms to your data. At the end of the project, you get the exact percentage possibility of how feasible is your goal.

Where is Machine Learning Proof of Concept applicable?

Whenever you have a goal to achieve, a problem to be solved or a task to automate. And of course, you gather any structured or unstructured data like sounds, images, videos, tabular data, times series, that is related to the problem and is usable by the algorithms.

Find an opportunity

for your business GROWTH

Start Machine Learning with a guidance call