Building an Artificial Intelligence Proof of Concept

A Comprehensive Playbook

At FullStack, we have partnered with leading research organizations, national logistics firms, and more to develop their cutting-edge AI proofs of concepts (PoCs). PoCs help companies assess feasibility and optimize their products before full development. This playbook details the FullStack Labs methods for building a best-in-class AI PoC.

What is an AI POC?

An AI Proof of Concept (PoC) is a small-scale prototype project designed to demonstrate an AI solution's feasibility and potential impact. It enables us to assess the technical viability, assess the performance, and identify any potential issues. A PoC also helps you estimate costs and timelines for production, delivery, and architecture before committing to a full-scale implementation.

AI Research Assistant Proof of Concept

Before developing a full AI solution, we designed a PoC of an AI Research Assistant for a leading research institution. The proof of concept helps clients gain insight reports and book time with analysts in seconds—but operates on a limited dataset. It is now being scaled up and built into a full solution with access to a full research library.

Phase 1

Product Ideation

Laying the Groundwork for Innovation

In the pre-discovery phase, we establish a shared understanding and strategy for successfully developing your AI PoC. We develop a sense of your envisioned application, discern your ambitions, and acknowledge potential hurdles. This process typically includes:

Stakeholder Identification

We identify all key stakeholders and their roles in the project. This ensures alignment and transparent communication.

Goal Setting

We define the project's objectives, ROI, key results, and performance indicators, informing our development.

Data Source Audit

We identify all relevant data sources required for the project and assess where and how the data is stored.

Technology Stack Assessment

We review your current technology stack, system, and infrastructure and determine its capabilities.

Engineering Team Review

We evaluate the existing client engineering team's capabilities and maturity and identify skill gaps.

End User Research

We determine who will use and interact with the output and develop their profile.

User Interface (UI) Clarification

We clarify the user interface requirements for your custom AI solution.

Expected Output Definition

We define what the final output should look like, which informs our technology options.

Security and Compliance

We identify the level of security and compliance required around the data.

Pre-discovery provides clarity from the start.

Completing the pre-development discovery phase sets clear expectations for the feasibility and trajectory of the rest of the AI PoC project.

Phase 2

Discovery

Understanding the Product Landscape

We select the appropriate technologies during this phase and assess the project's feasibility. We will also model your architecture, clarifying how the different elements of your tech stack will interact. We then create a robust development plan outlining the path forward for your AI PoC.

Platform & Environment Assessment

We assess the available platforms and environments and choose the most suitable ones for development.

Library, Framework, & Tool Assessment

We identify the necessary libraries, frameworks, and tools for the project.

Data Assessment

We examine data thoroughly, assessing its architecture, quantity, and quality.

Scale Assessment

We determine the total throughput that will be required for the production solution to ensure the correct architecture is validated during the POC.

Architecture Modeling

We Develop a preliminary architecture model and define how the elements of your AI solution will interact.

MLOps Feasibility

We evaluate the feasibility of implementing machine learning operations (MLOps).

Discovery defines concrete AI possibilities.

Completing the discovery phase provides real, actionable insights into your AI PoC. It defines what resources you need and documents the path for development.

Key Deliverables

Phase 3

Data Preparation

Building the Foundation of Your AI PoC

Proper data preparation is critical to the success of your AI model. We focus on gathering, cleaning, and transforming data to create a solid foundation for training and validation. This phase includes multiple sub-steps to prepare your data for use.

Gather Existing Training Data

Before writing a single line of code, we plan each stage of development. This includes assembling a skilled app development team, completing design-to-development handoffs, establishing key project milestones, and outlining sprints and stories for efficient progress.

Data Access

The first crucial step is accessing existing training data. Our team identifies and retrieves all relevant data sources available, which might be stored in databases, cloud storage, or other repositories. We ensure that the team has the correct permissions and access rights.

Data Cleaning

Data cleaning involves removing inaccuracies and inconsistencies within your datasets. At this step, we ensure the data is accurate, complete, and error-free. Techniques include handling missing values, removing duplicates, and correcting or removing corrupt records.

Data Transformation (ETL)

ETL (Extract, Transform, Load) is the process of extracting data from various sources, transforming it into a suitable format or structure for analysis, and loading it into a data warehouse or other target system. This step often involves normalization, aggregation, and other transformation techniques to make the data usable.

Data Splitting

Splitting the data into training and testing sets is a fundamental step in preparing your data. We ensure your AI PoC model can be trained on one subset of the data and validated or tested on another, measuring its performance.

Work with SMEs to Create More Training Data

Collaborating with Subject Matter Experts (SMEs) can help generate additional training data. SMEs provide insights and context that might not be apparent from the data alone, enabling the creation of a more comprehensive dataset.

Use Generative AI to Synthesize Training Data

If the project requires additional data, we can synthesize it. Generative AI can create synthetic data that mimics the characteristics of real-world data. Generative models such as GANs (Generative Adversarial Networks) can produce high-quality synthetic data to enhance the training dataset.

Set Aside Data for Validation Purposes

Creating a separate validation dataset is crucial for evaluating the model’s performance during training. Your AI development engineers use the validation set to tune model parameters and decide on which models to use, ensuring that the model generalizes well to unseen data.

Analyze the Data to Ensure Quality and Relevance

Data analysis involves examining the data to ensure it is of high quality and relevant to the problem at hand. This includes statistical analysis, visualization, and exploratory data analysis (EDA) to understand the underlying patterns and relationships within the data. Analyzing the data helps identify any biases or anomalies before proceeding to model training.

Data Preparation with Rigorous Standards

During the data preparation phase, our team ensures meticulous gathering, cleaning, and transformation of data, laying a solid foundation for your AI model. This phase verifies that all data processes align with project goals and quality standards. Upon completion, your data will be thoroughly prepared for the subsequent model training phase, ensuring accurate and reliable results.

Key Deliverables

  • Data Report covering:
    • Quantity
    • Quality
    • Planned Use
    • Security/Privacy
    • Findings

Phase 4

Technology Selection & Modeling

Choosing the Right Tools for the Job

New technology emerges almost weekly, and selecting the appropriate machine learning (ML) models is critical. This phase involves evaluating and identifying the best candidate models that align with your project’s objectives and data characteristics.

Assess Machine Learning Models for Suitability

We begin by exploring available machine-learning models that could solve the problem at hand. This includes traditional models (like linear regression, decision trees, and SVMs) and advanced models (such as neural networks, LLMs, and ensemble methods). We evaluate each model based on several criteria:

Performance

Assess the historical performance of the models on similar datasets.

Scalability

Determine if the model can handle the size and complexity of your data.

Compatibility

Ensure that the model is compatible with your existing technology stack.

Ease of Implementation

Consider the complexity of implementing and tuning the model.

Resource Requirements

Evaluate the computational and time resources needed for training and inference.

Identify Candidate Solutions for PoC Trial

Based on the evaluation, FullStack narrows down a shortlist of candidate models.  These models should meet the initial criteria and show promise in preliminary tests.

Key Deliverables

Phase 5

Proof of Concept Development

Turning Ideas into Reality

In this phase, we develop a minimally functional Proof of Concept (PoC) for your AI solution using the selected models and prepared data. This PoC demonstrates the feasibility of the proposed solution and helps identify areas for improvement before full AI development.

Implementation of Selected Solution

The FullStack team begins by implementing the selected solution into the development environment. This involves setting up the necessary infrastructure and tools to support the models. Key activities include:

Setting Up the Environment

Configure the development environment with the required software, libraries, and dependencies. This could involve using cloud-based platforms, local servers, or a combination of both.

Model Integration

Integrate the candidate models into the environment. This includes loading the model architectures, configuring their parameters, and preparing them for training.

Version Control

Utilize version control systems like Git to manage the different versions of the models and ensure reproducibility.

Data Integration

With the environment in place, we then begin integrating the prepared data. Key activities include:

Data Loading

Load the training and validation datasets into the development environment. Ensure that the data is accessible and correctly partitioned.

Preprocessing

Apply any necessary preprocessing steps to the data, such as normalization, scaling, or encoding categorical variables.

Data Pipelines

Set up data pipelines to streamline data flow from storage to the model. This ensures that data efficiently feeds into the model during training and testing.

Model Training

At this stage, we feed the training data into the models and optimize their parameters to minimize errors. Key activities include:

Training Process

Execute the training process, iteratively feeding batches of training data into the models and updating their parameters using optimization algorithms like gradient descent.

Hyperparameter Tuning

Experiment with different hyperparameters (e.g., learning rate, batch size) to identify the optimal settings that yield the best performance.

Monitoring Training

Continuously monitor the training process, tracking metrics such as loss, accuracy, and other relevant indicators to ensure that the models are learning effectively.

Initial Testing

Once the solution has been set up, AI engineers conduct initial testing using the validation dataset. This step assesses the model's performance and identifies any issues or areas for improvement. Key activities include:

Validation Testing

Evaluate the models on the validation dataset to measure their performance. Calculate key metrics such as accuracy, precision, recall, and F1 score.

Error Analysis

Analyze the errors and misclassifications made by the models to identify patterns and areas for improvement.

Performance Tuning

Based on the validation results, make necessary adjustments to the models, such as fine-tuning parameters or retraining with additional data.

Key Deliverables

Phase 6

Refinement & Feedback

Optimizing for Success

We iteratively refine the PoC based on performance against the defined KPIs and key stakeholder feedback. Throughout this process, FullStack ensures that the AI solution meets the outcomes and provides optimized performance.

Performance Metrics

Review quantitative metrics such as accuracy, precision, recall, F1 score, and other relevant indicators.

Benchmarking

Compare the PoC’s performance with baseline models or previous iterations to gauge improvement.

Stakeholder Feedback

Gather input from stakeholders regarding the model’s performance and any observed shortcomings.

Iterative Adjustments

Based on the evaluation results and stakeholder feedback, we make targeted adjustments to the model. This step focuses on enhancing the model's capabilities and addressing any identified issues.

Parameter Tuning

Fine-tune hyperparameters to optimize the model’s performance.

Algorithm Adjustment

Modify the model’s architecture or algorithm if necessary to better fit the data.

Data Augmentation

Incorporate additional data or apply data augmentation techniques to improve model training.

Iteration Cycle

Implement a cycle of evaluation, adjustment, and re-evaluation to enhance the model progressively.

Testing Variants

Test different model versions to identify the most effective configuration.

Continuous Feedback Loop

Maintain an ongoing feedback loop with stakeholders to ensure the refinements align with their expectations and requirements.

Key Deliverables

Comprehensive report detailing each iteration, including:

  • Iteration Summary: Overview of the changes made and their objectives.
  • Performance Metrics: Quantitative results for each iteration, showing improvements or declines.
  • Stakeholder Feedback: Summary of feedback received and how it was addressed.

Recommendations: Suggestions for further refinements or next steps.

Phase 7

Architecture for Production

Creating Solutions that Scale

Once you’ve got a firm sense of your PoC, we build the architecture for your full AI solution. This enables us to plan for the transition from a scale that works for the PoC to a delivery solution.

Understand Existing Technology & Infrastructure

Collect technical documentation, secure system access, analyze team skills, and conduct stakeholder meetings to understand the current technological environment.

Gap Analysis

Analyze code, review processes, evaluate infrastructure, and examine data handling to identify challenges in the current systems.

Research and Technology Assessment

Assess potential technologies, align solutions with business needs, evaluate custom vs. third-party tools, conduct cost/trade-off analysis, and perform feasibility tests.

Data Architecture Design

Audit existing data structures, determine data needs, design data architecture, evaluate storage solutions, check integrations, and document best practices.

Infrastructure Recommendations

Audit current infrastructure, determine future needs, select cloud solutions, evaluate on-prem options, design for scalability, and assess security.

Standards Conformity

Identify and incorporate standards for open source, security, and accessibility to ensure the architecture meets industry standards.

Operational Cost Analysis

Assess costs for hosting, infrastructure, and third-party services, and analyze operational metrics to evaluate financial sustainability.

Software Development Life Cycle (SDLC)

Document development processes and integrate best practices for versioning, code review, CI, and CD to provide a clear development roadmap.

Key Deliverables

Phase 8

Conclusions & Planning

Preparing for Full-scale Development

After successfully developing and refining the PoC for your custom artificial intelligence solution, the next step is transitioning to full-scale production. This involves detailed planning and integration with existing systems to ensure a smooth deployment.

Plan New Application Development

Define the scope, timelines, and resources required to develop new applications or features that must be integrated with the existing systems.

Product Design

For projects requiring a user interface, we gather requirements and create a high-fidelity prototype. For more details on product design, check our Designing an Application Playbook.

Identify Integrations

Identify all the necessary integrations with existing systems and applications to ensure seamless communication and functionality.

Resource Proposal

Outline the resources required to execute the integration plan effectively, including technical resources, tools, and additional personnel.

Prepare Project Proposal

Create a comprehensive project proposal that outlines the steps and resources needed to transition the PoC to a production-ready state.

Estimate Effort for Model Deployment

Estimate the effort needed to deploy the model in a production environment, considering infrastructure, data handling, and scalability factors.

Create Product Roadmap and Milestones

Create a detailed product roadmap that outlines the development and deployment phases, including key milestones and deliverables.

Key Deliverables

Project Proposal
Code Repositories
Design Documentation (if needed)

Design and Build an AI Proof of Concept with FullStack Labs

Partner with FullStack Labs for transparent collaboration and full-service excellence in custom software development. Contact our experts to start building your AI proof of concept, or hire expert AI developers to work with your team.

Transparent Collaboration

Our approach is flexible to accommodate each client's preferences. Whether you prefer to be closely involved or prefer periodic updates, we ensure clear and consistent communication throughout.

Full-Service App Development

With our team’s diverse skillsets, FullStack Labs is an ideal choice for a full-service app development company. Partner with us to begin your next digital transformation.

Are you ready to build your next app? Contact our experts at FullStack Labs and take the first step today.

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