Prove your Business Ideas with PoC
To understand the importance of PoC for business, let’s have a short overview of what is a ‘proof of concept’ as a term.
PoC is a small-scale initial practical realization of business idea used to test its feasibility and demonstrate its viability. Proof of concept meaning and purpose is to validate whether the idea can solve business problems, cover its needs, deliver value and efficiently operate within existing infrastructure .
From the point of AI implementation, PoC, being a sufficient part of AI development, is a time saving experimental project that helps to explore AI potential and possible business benefits and value of AI solution.
In other words, if you want to see if the perspectives of AI tool or other business innovation worth additional investing and commitment, getting through proof-of-concept step seems to be an obligation.
What is AI PoC Process?
Though PoC development implies small project development, its importance for business can’t be overestimated.
Its goal is to demonstrate whether the idea is achievable with available resources and time and to quickly identify possible challenges of AI solution implementation. In further perspective, it allows to mitigate risks and prove the idea before committing significant resources.
Though PoC is typically considered as the first stage of any solution or product development, there are cases when the company wants to test the hypothesis that isn’t planned to be developed soon. As a result, PoC can be considered a separate narrow-focused project.
In both cases we typically divide the process of PoC development into stages, and each of them is crucial for the result.
- 1
Goal Setting
The first step defines the entire work. The same rule is for PoC development. We collaborate with your team or any representatives who have authority to share your business needs and ideas.
This communication stage is necessary for better understanding of the problem you face and objectives and success metrics you want to achieve. The result of the stage is a list of success metrics and desired insights along with the initial technological stack, number of specialists involved, deadlines and project budget.
Note! Sometimes, the information exchange during the goal setting is enough to realize that the project is irrelevant, and the idea is unattainable.
- 2
Data Collection and Preparation
Next, we gather all the data necessary to start working. Regarding AI development it means collecting the data for the initial dataset which will become the basis for future AI model training.
The required data may regard purchasing and subscription statistics, corporate knowledge base, conversation records, website metrics and more. They can be presented in tabular, textual, audio, image or video format , depending on the purpose of the project and tasks the AI model is to solve.
The collected dataset will get through data cleaning process, where all the irrelevant erroneous rates, metrics and other data pieces will be removed.
We used the following practice in one of our projects regarding an AI predictive analytics solution for agricultural business . When the company provided the data referring to the health and life history of animals on its farms, a number of values appeared to be irrelevant from a logical and practical point of view. So, our specialists carried out an additional process of data cleaning.
The result was a ready-to-use testing dataset which can be loaded into AI model.
Note! Data collection and preparation is the most important stage of PoC development, as the results depend on it directly. You should take great concern of it.
- 3
Development and Testing
Our developers use all the gathered data to create a prototype that should allocate with them and with the goals set.
For instance, if you seek for a solution based on generative AI capabilities, we can rely on ChatGPT API (or any other foundational large language models) or apply to custom LLM development with the usage of explicit frameworks such as PyTorch for AI model development and TensorFlow for its deployment.
This stage can also demonstrate that some technological stacks are inappropriate for goals realization, and another solution should be found.
For example, the above-mentioned situation happened in one of our projects on LLM deployment for marketing agency. The initial idea of running private LLM on an Azure server was quickly found to be too expensive for the client, so we shifted our efforts to a more cost-efficient solution.
Silk Data prepared a comprehensive blogpost regarding the business benefits and perspectives of ChatGPT and other LLMs usage. Feel free to read.
When it’s done, our QA specialists make sure that the result really meets the expectations, and all the features work correctly.
In case of AI solutions development, the development process means AI model basic architecture building and training (based on the testing dataset).
- 4
Results analysis
We test the results and compare them with success metrics set. After that, a detailed report is prepared containing useful insights and possible ways of scaling.
In other cases, the report explains why the solution didn’t work and what are other ways of achieving the goal.
Silk Data’s comprehensive expertise in IT consulting and AI consulting allows to provide you with a set of recommendations or even fully operatable solutions that better match your company’s needs or expectations. Want to find a trustworthy PoC development company? Discuss your idea with our managers!
What is Good PoC?
A successful AI PoC (or PoC in common) is more than just a technical experiment — it’s a strategic tool for decision-making, and here are the points that make it effective.
Clear and Realistic Objectives
Every successful PoC project starts from a well-defined problem statement and measurable goals. The success metrics should be realistic, and the project should have relevant deadlines and adequate amount of resources to spend.
It’s vital to remember that PoC is meant to test the idea and help to understand the necessity and efficiency of future large-scale changes.
Data Quality
PoC development and especially the one connected with AI implementation significantly depends on the initial data quality.
The development team should have access to relevant, clean and sufficient data for testing.
For example, you can’t expect the AI model to show you any valuable insights or relevant results, if the data used for its training are erroneous or insufficient.
Actionable Insights and Development Potential
The main result of projecting proof of concept is that it allows to see the results of the solution without committing excessive resources.
It is important to note that PoC is valuable for both technical specialists and business supervisors. The first group can get useful insights and recommendations on technological stack and technical challenges regarding further development, while the second is provided with practical demonstration of the product's business value and functionality.
In other words, your PoC should provide valuable information, the data that will enhance your decision-making and will allow to see the possibilities of further scaling.
What our C-level Experts Say
"Today the difference between success and costly missteps often lies in validation. A Proof of Concept isn’t just a technical checkpoint, but a strategic compass for further business development. At Silk Data, we transform uncertainty into clarity by testing your AI and tech ideas with real-world prototypes. In other words, together we can turn your vision into an actionable roadmap."
Yuri Svirid, PhD. — CEO Silk Data
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Frequently Asked Questions
Though PoC and MVP (minimum viable product) are both important in product development, they are not the same.
PoC focuses on proof that the idea or concept is technically implementable and can solve specific problems or lead to a specific result. It’s an early stage meant for developers, stakeholders and investors.
MVP is a mid-stage simplified product that is meant to test a product’s core functionality with real users. The main purpose is to get valuable feedback for further development or bug-fixing.
The required data depends on the particular use case. For instance, if you are looking for predictive analytics solution for sales department, we will require some historical and present data on sales and marketing strategy.
Anyway, the initial step of goal setting is where we define and choose the information we need for work.
The deadlines depend on the complexity of the idea and of the following project. Typically, full PoC development cycle lasts for several weeks (3-8), and can be reduced or expanded by many factors.
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