PoC
Amazinum’s specialists will help you define and understand the value, relevance, and challenges of a product, service, or service to make sure that it will meet the requirements and vision of the client.
PoC Essential (Discovery)
Stage of service
Week 1. Getting familiar with domain
- Discover business problems and opportunities
- Clarify the business needs and opportunities
- Investigate the available and potential data
- Investigate the available and potential data sources
- Execute EDA (exploratory data analysis)
- Define the expected deliveries
Week 2. Investigating and adopting solutions
- Define the metrics and evaluation of algorithms
- Investigate the available models, solutions, approaches
- Train models on domain data, evaluate, and compare.
- One-pager rapport on how to utilize ML in your project
- High-level Project Roadmap, with a breakdown into milestones
- Ballpark estimate of each milestone
- Ballpark time and cost estimation
- Suggested team composition
PoC PRO
Stage of service
Week 3. Improving results
- Achieving better model(s) performance
- Experiment with various datasets, tuning hyperparameters
Week 4. Finalizing research and preparing delivery
- Preparing models and improvements overview
- Preparing prototype demo
- Wrapping in web service with exposed API
- Dockerized solution ready to deploy on client’s infrastructure
- Deploying dockerized solution
- Preparing brief documentations
- Describing further tasks and improvements
- One-pager rapport on how to utilize ML in your project
- High-level Project Roadmap, with a breakdown into milestones
- Ballpark estimate of each milestone
- Ballpark time and cost estimation
- Suggested team composition
- Prototype
How to determine if you need a proof of concept
Why you need a POC
Projection of final cost, as well as assessment of efforts, investments, hidden costs, and savings measures.
Forecast of technical challenges, assessment of scale, and development of a work process plan, which will allow for making informed decisions.
Improvement of development strategy, optimization of product functions, and improvement of interaction with the user based on testing.
Risk reduction by experimenting with a smaller project model.
Checking the viability of the project to assess the expected results and make changes to the current plan.
Attracting investments and gaining the trust of partners.
Establishing a benchmark for performance measurement will allow the evaluation of actual and expected results. Thus, based on them, we can improve the decision-making process and product/service/service exit strategies.
Why you need a POC
Projection of final cost, as well as assessment of efforts, investments, hidden costs, and savings measures.
Forecast of technical challenges, assessment of scale, and development of a work process plan, which will allow for making informed decisions.
Risk reduction by experimenting with a smaller project model.
Checking the viability of the project to assess the expected results and make changes to the current plan.
Attracting investments and gaining the trust of partners.
Improvement of development strategy, optimization of product functions, and improvement of interaction with the user based on testing.
Establishing a benchmark for performance measurement will allow the evaluation of actual and expected results. Thus, based on them, we can improve the decision-making process and product/service/service exit strategies.
FAQ
What happens in the case of a failed PoC?
Not all proof of concepts yield success, and there can be various factors contributing to their failure:
- Data may be disorganized and impractical.
- The ETL (Extract, Transform, Load) workload can become prohibitively expensive when implemented at a larger scale.
In any case, the outcomes of failed proof of concepts should not be viewed as disappointments but rather as valuable learning experiences that can pave the way for future successful ideas.
Which companies used POC for their startups?
Airbnb
Initially, the founders created a simple website that verified people’s requests for a vacant room or property. This allowed the company to attract investments and articles with a successful and revolutionary platform.
Slack
The beginning of Slack was the creation of an internal communication platform for a gaming company. This allowed the team to assess its potential in the market and become a leading tool for team communication around the world.
Tesla
Tesla has been conducting POCs for electric vehicles and energy storage systems. Their success in creating long-range electric vehicles and energy products such as the Powerwall and Powerpack has transformed the automotive and energy sectors.
Which industries used POC for their product?
Self-Driving Cars: Companies like Waymo (formerly a project of Google) conducted POCs to demonstrate the feasibility of self-driving cars. These POCs eventually led to the development of autonomous vehicles and the emerging industry surrounding them.
IoT in Healthcare: POCs have been conducted to prove the effectiveness of Internet of Things (IoT) devices in healthcare. For instance, monitoring devices for patients with chronic conditions have shown how IoT can enhance patient care and reduce healthcare costs.
Artificial Intelligence in Healthcare: POCs using AI for diagnosing diseases and predicting patient outcomes have demonstrated significant success. IBM’s Watson for Oncology is a notable example.
Agricultural Technology: POCs for precision agriculture, using drones and data analytics to optimize farming, have proven to increase crop yields and reduce resource consumption.
Renewable Energy: Many renewable energy projects, such as solar and wind farms, began as POCs to prove the viability of clean energy sources, and they have since become major contributors to global energy production.
Food Delivery Apps: Companies like Uber Eats and DoorDash started as POCs in specific cities to prove the concept of app-based food delivery. Their success has reshaped the restaurant and food delivery industry.
Set Points
Data Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
Data Engineering
- Data collection
- Data cleaning
- Data normalization and storing
- Data pipeline management
ML & AI
- Computer Vision
- NLP & LLM
- Recommender Systems
- Time Series Forecast
Amazinum Portfolio
Advantages of engaging the Amazinum company
7
Years
We offer you our 7 years of expertise in data science.
40+
ML Engineers
We have a large talent pool of highly-skilled ML engineers of Middle and Senior-level professionals.
16
Active Projects
Our team has successfully connected Machine Learning Engineers to technical departments in more than 19 companies.
We develop unique solutions created for a specific project and client’s requirements.
The Amazinum team takes market features and micro and macro risks in the development of POCs in order to optimize your efforts and resources.
We take care of the qualifications of each specialist we offer for the project. Due to the presence of more than 40 specialists, we select a Data Scientist who has experience in a specific domain with specific technologies.
You may not worry about the employee if the case of termination or suspending of assignment, as well you don’t need to hire the specialist in advance in cases of uncertain project start
Our Industry Focus
Our industry knowledge and background give our clients and partners confidence that we understand their business. Here we highlighted a few top industries we are good at, penetrating to the smallest details and nuances of a certain branch.
SEO & Advertising
Healthcare
Safety & Security
Sport
E-commerce & Retail
Gambling and Casino
Technology (Localisation)
Partners & Clients
We deeply appreciate our partners for cooperation. Every member of the Amazinum Team does their best to provide the highest quality of services and solutions. We satisfy all your needs and requirements.
Technologies
Learn about technology stack we use to implement data science:
Programming languages:
for data analysis and processing:
for creating solutions:
for API development:
for visualization:
Deploying solutions:
Databases:
SQL:
NoSQL:
Cloud Solutions:
Vitaliy Fedorovych
CEO, Data Scientist at Amazinum
Hello there!
Amazinum Team assists you through all data science development processes:
from data collection to valuable insights generation.
Get in touch with our CEO and Data Scientist to figure out the next move together
- 4A Peremohy square, Ternopil, Ukraine, 46000
- +380 98 85 86 330
- vfedorovych@amazinum.com
Contact Us
Bazarnitska Alina
Client Partner
Ihor Khreptyk
Client Partner
Stopnyk Zoriana
Client Partner
Vitaliy Fedorovych
CEO, Data Scientist
