COMPUTER VISION ON AZURE PROOF OF CONCEPT

Within 2 months fill the Computer Vision expertise gap in your team and accelerate growth of your solutions with Semantive experts, know-how and iterative approach.

Automate video analysis

Looking to leverage Computer Vision as part of Artificial Intelligence initiative on Azure, but unsure about how to move forward? Without human supervision, Computer Vision allows AI to “see” your business all the time, in-store movement of your customers, increased production quality, smart city surveillance – Computer Vision is valuable in different use cases, from edge to cloud. 

Azure Cloud / AI on Edge

Computer Vision ML Researchers & Engineers

Azure Solution Architects

Reliable, enterprise level secure and operational AI solution

Accelerate PoC engagement

We offer you a consulting package of 2 month Proof of Concept engagement to solve a Computer Vision challenge. We are able to deliver a prototype of a solution using our iterative and experimental approach to development and benchmarking of Machine Learning models.

Semantives Data Science Team accelerates PoC engagement with custom Deep Learning models, pre-built and pre-trained models to decrease time to value and focus on your specific business use case for Computer Vision.

    “Four O’s” challenges in Computer Vision we solve:
  • Object detection
  • Object classification
  • Object segmentation
  • OCR

Expertise that makes the difference

Skilled Data Science Team

Dedicated team with expertise – Computer Vision ML Researchers & Engineers, Azure Solution Achitects

Solutions leveraging Azure Cloud

Starting from PoC leverage power of cloud. Incorporate easy integrations and scalability from the very beginning of creating the solution.

Customized Deep Learning Models

We use State-of-the-Art AI solutions customized to your needs. Selection of the best model and its customization gives the best proof of concept results which lead to the best further decisions.

Security and safety

As a partner of Microsoft we provide solution that guarantee corporate level security.

Get more details

Find estimated pricing, and more details about Computer Vision Proof of Concept before contacting with us.

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PoC stages

1

Business Objective & Envisioning

Set the stage with business use case, success KPIs, explore alternative approaches and envision how AI and Computer Vision might solve the challenge.

2

Data Assessment, Data Gathering

Assess existing data supporting business case. Plan a path forward for additional activities such as data gathering, data annotation.

3

Experimenting & Development

Establish a baseline model against success KPI and iterate over a series of experiments and development of an AI solution leveraging portfolio of our algorithms and expertise.

4

Publishing

Publish the best solution to Azure Machine Learning as a service, prepare recommendations & roadmap with next steps.

5

Outcomes session

Present outcomes against success KPIs, explore recommendations & roadmap. 

We are a laureate of

Deloitte Technology Fast 50 is a programme that recognises and profiles the fastest growing public or private technology companies in Central Europe. Semantive has reported a revenue growth of 1 027% over 4 years.

14th

in Central Europe

1st

in Poland

More details

Pricing and length of PoC engagement is affected by data availability, complexity of a Computer Vision challenge and how much dedicated development is required.

The engagement is structured towards quick results enabling assessment of business case to either continue or abort and move on to a different AI challenge as part of an AI portfolio.

Outcomes of this engagement include:

  • Documented solution approach 
  • Packaged model and deployed to Azure Machine Learning in customer subscription
  • Recommendations & Roadmap with next steps

Find more information downloading One Pager or contacting us.

Interested in case study?

Visual Inspection of products

Customer challenge: Automate quality inspection process of semi-finished products during the production process and recognize the broken ones.
Solution provided: A pipeline of custom deep learning models for detecting products and then marking defects with a feedback loop and logging to production system.

Go to case study→

Learn more

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We will contact you as soon as possible.


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