Turn your visionary ideas into reality
Proof of Concept (PoC)
We understand that innovation begins with exploring possibilities. Our R&D-focused approach helps you test and validate the feasibility of a machine learning approach for solving a complex problem, ensuring viability before full-scale implementation. Whether you have a groundbreaking idea or need to assess the potential of a new competitive scenario, our proof of concept service provides you with the insights and confidence to move forward. Let’s collaborate to push the boundaries of what’s possible and create the next big breakthrough in your industry.
Our delivery approach
Our Prototyping service is designed to explore the feasibility of innovative ideas through a research and development (R&D) approach. We understand that each project is unique, driven by the specific challenges and goals of your business. While the exact steps may vary depending on the nature of the project, our delivery approach follows a structured process to ensure thorough exploration, validation, and refinement of concepts.
01. Problem Definition and Desired Outcome
We start by clearly defining the problem, ensuring it is complex enough that simple coding rules or manual scaling wouldn’t suffice. This involves gathering a deep understanding of the problem from both business and technical perspectives. Our team works closely with you to understand the scenario, the scenario’s context, and the specific outcomes you desire. This phase might involve additional investigation if substantial knowledge gaps are identified during the discovery phase.
02. Data Access and Exploration
Next, we ensure that the full team has access to the necessary data, setting up dedicated environments if required and performing any needed de-identification or redaction of sensitive information. We then conduct a data exploration workshop with domain experts to understand data availability, quality, and relevance. This phase includes confirming access to data dictionaries, assessing data validation strategies, ensuring data volume sufficiency, identifying or creating an entity relationship diagram (ERD) if applicable, and identify potentially new useful data sources.
03. Architecture Discovery
During this phase, we develop a clear picture of the existing architecture and infrastructure. We may conduct infrastructure spikes to understand the current system's capabilities and limitations, paving the way for effective integration with the prototype solution.
04. Concept Ideation and Iteration
We collaborate with the users, and stakeholders to develop value propositions based on the contextual understanding obtained through research. We identify hypotheses or unknowns to be tested and continuously revisit and iterate on the concept as our understanding of the problem space evolves.
05. Exploratory Data Analysis (EDA)
In this phase, we conduct a deep dive into the data, focusing on understanding feature and label value distributions, correlations among features, and between features and labels. We also identify any data-specific constraints, such as missing values, categorical cardinality, or potential data leakage. This analysis helps in identifying gaps not uncovered during data discovery and sets the stage for applying suitable techniques.
06. Data Pre-processing
Data pre-processing begins during the EDA and hypothesis testing phase. This involves feature engineering, sampling, scaling, or discretization, and handling noise within the data. These steps prepare the data for model training and further analysis.
07. Hypothesis Testing
We design several potential solutions using theoretically applicable algorithms and techniques, starting with simple baselines. The team trains models, evaluates their performance, and determines if they meet the desired criteria. Based on the outcomes, we tweak experimental designs, iterate on the models, and thoroughly document each step and resulting hypotheses, ensuring a clear decision-making process.
08. Concept Testing
Where applicable, we test the value proposition, concepts, or aspects of the experience by planning and conducting user, stakeholder, and expert research. This involves developing and designing necessary research materials, synthesizing and evaluating feedback, and incorporating the findings into further concept development. We ensure the proposed solution aligns with the existing business goals and context.
Our team unit
At Stokedge, our AI Pod is designed to deliver comprehensive and cutting-edge AI solutions tailored to your business needs. Each Pod is a self-sufficient unit with a diverse team of experts who work collaboratively to bring your AI projects to life. Our unique Pod structure ensures that we provide high-quality, efficient, and secure machine learning solutions, driving tangible results for your business.
Meet the team
5. DevSecOps Engineer (Infra): Our DevSecOps Engineers focus on infrastructure architecture and implementation, model deployment, security, and monitoring. They guarantee that the AI solutions are deployed securely and efficiently, with minimal downtime.
6. Domain Expert (1-4): Domain Experts provide specialized knowledge in specific verticals or functional domains, such as industrial production, forex trading, or retail pricing. They may also offer expertise in cross-cutting technical domains like software engineering or UI/UX design. These experts ensure that our solutions are tailored to the specific needs and challenges of the industry or function.
1. Pod Lead: The Pod Lead is responsible for project management and acts as the single point of contact (SPOC) with the client's management team. They ensure that the project stays on track, all team members are aligned with the goals, and communication with the client is seamless.
2. Data Scientist: Our Data Scientists focus on algorithm selection, model testing, and feature engineering. They bring the theoretical and practical expertise needed to choose the right models and refine them for optimal performance.
3. Data Engineer: Data Engineers handle data collection and integration, information architecture, quality checks, bias checks, and data policy enforcement. They ensure that the data pipeline is robust, reliable, and compliant with relevant policies.
4. ML/AI Engineer: ML/AI Engineers are tasked with the software engineering aspects of the models, including production systems, continuous integration/continuous deployment (CI/CD) pipelines, and monitoring integration. They ensure that the AI solutions are scalable and maintainable.