DATA

In today’s digital world, data has become the life blood of organizations of all sizes across all industries. The ability to manage, analyze, and leverage data has evolved from a competitive advantage to an absolute necessity. Enterprise data underpins decision-making, operational efficiencies, fostering innovation, and regulatory compliance.

However, for those who thought that Digital Transformation to generate this data was the end of the journey, the reality is that to reap the benefits of Artificial Intelligence (AI), including GenerativeAI (GenAI), data in itself is not enough – the next leg of the journey hasstarted with Data Transformation.

AI has emerged as a transformative force, allowing organizations to optimize productivity, business insights, and customer experience. However, introducing uniquely tailored business processes,marketing campaigns, products and services, customer responses, and the like requires a foundation of the highest Quality Data.

Quality Data must be:

• Accurate: The efficacy of AI models significantly depends on the accuracy of the data used to train them. With highly accurate data, AI algorithms can yield correct and reliable outcomes. However, errors in data input can lead to inaccuracies in AI systems, reducing their effectiveness and ability to provide accurate predictions ordecisions.
• Complete: Incomplete or missing data can skew the results delivered by AI systems. A complete data set ensures the AI model has a comprehensive understanding, providing more accurate and reliable results.
• Relevant: The data used should be relevant to the problem at hand. Irrelevant data can cause an AI system to produce results that aren’t useful or applicable to the real-world problem it is meant to solve.
• Timely: The data fed into an AI system needs to be up-to-date. Outdated data can lead to irrelevant and incorrect results, reducing the effectiveness of AI systems.
• Consistent: Having a uniform dataset is essential for AI systems. Discrepancies or inconsistencies in the data can confuse AI systems and lead to less reliable results.

Maintaining high data quality is critically important in the era of automated decisions, AI, and continuous process optimization. By accurately assessing, managing, and continuously improving data quality, organizations can use AI and GenAI to their full potential, making more accurate predictions and well-informed decisions.

Epik has highly skilled data engineers who can help you to get the most from your business data, including:

• Architect an enterprise data strategy.
• Design, implement, and manage data repositories/data lake.
• Continuous ingestion and analysis of data.
• Curate data and optimize data quality and integrity.
• Report on business data and provide business insights.

Some clients we have successfully done this for include Ivy Fertility (Healthcare), ConMet (Automotive), and AuditMate (Facilities Services).

Artificial Intelligence (AI)

With quality data in hand, Epik has a cadre of accomplished data scientists who will work with you on  implementing your AI vision and strategy, iteratively working through identifying the right use case(s)*, solving the right problem(s), using the right method(s), to generate actionable result(s) that accelerate business growth:

• Identify opportunities to leverage AI technology.
• Develop, implement, and tune Machine Learning (ML) models.

*Top cross-industry use cases – App/Infrastructure Capacity, Infrastructure Predictive Capacity, Alert/Event Reduction, Intelligent Alert Escalation and Routing, and User Experience Impact Analysis.

Some clients we have successfully done this for include Endera Motors (Automotive), Limelight Health (Healthcare Insurance), and Patterson + Sheridan (Legal).

Generative AI (GenAI)

The next chapter in the AI storybook is GenAI...

For Small and Medium Businesses (SMB) dipping their toe into the world of GenAI, perhaps the first order of business will be to understand the use of tools such as ChatGPT across their organization and what potential impact they could have. Epik can help you:

• Plan a roadmap for discovery, and scope the skills, services, and investments needed.
• Identify relevant risks and their impacts on the organization.
• Understand the security and policy controls of the GenAI applications, such as ChatGPT.
• • Insecure Code Generation
  • Data Privacy and Confidentiality
  • 3^rd Party Security
  • Prompt Injection
  • AI Jailbreaking
  • Data Poisoning
  • Regulatory Compliance
  • Copyright, Ownership, and Licensing
  • Bias and Discrimination
  • Ethics, Trust, and Reputation
• Create policies on how and who can use these tools in a manner that mitigates the above risks.
• Work with departmental stakeholders to define usage guidelines and ensure understanding of the risks, issues, and best practices.
• Recommendations on Human-In-The-Loop inclusion for high-impact use cases. Understand the security and policy controls of the GenAI applications, such as ChatGPT.
• Apply Business Process Management (BPM) to realize gains in productivity.
• Determine potential on-premise alternatives under enterprise IT control*.

*We recommend the backplain platform.

Key elements of a successful GenAI implmenetaion may also necessitate:

Retrieval Augmented Generation (RAG)

Many large enterprises are now finding that the Large Language Models (LLMs) are falling short of their requirements and look to RAG as an essential capability to enrich the quality of these LLMs and provide more accurate, more up-to-date, context-specific responses by connecting them to your content. Epik can provide:

• Data preparation and vector extraction/database creation.
• Retrieval model setup.
• Integration of retrieval and generation.
• Model training.
• Evaluate the RAG model.
• Optimization.

Fine-tuning

However, in cases where better LLM performance in a given business function/task (e.g., customer question response) or domain-specific knowledge (e.g., healthcare) is required, fine-tuning is an alternative technique when prompt engineering and RAG do not suffice. Note that fine-tuning might not be the best approach if the data is frequently changing or used only rarely. Quality of data is of utmost importance. Epik can provide:

• Domain-specific insight and recommendations (Emerging Pharma, Agritech, Automotive, Carbon (Credit) Management)
• Task-specific insight and recommendations (Marketing, Development)
• Data collection and preparation.
• Model selection.
• (Multi-step) Fine-tune training.
• Evaluation and testing.
• Deployment.
• Monitoring and updating.

Reinforcement Learning from Human Feedback (RLHF)

A key component of the backplain platform, recommended to all our clients, is integrated Human-in-the-Loop (HITL) feedback on which responses are most accurate, as well as ensuring which best align with intended goals and values. RLHF, therefore, offers a powerful approach for improving the capabilities and aligning the outputs of LLMs with human expectations.

Prompt Engineering

Beyond prompt engineering training that Epik can provide, the backplain platform, provides suggested prompts to make LLMs more accessible to a ALL users, allowing them to leverage the power of these models without requiring extensive technical expertise.

Throughout your Data & AI Journey, Epik will be part of your team, kick starting ALL your Data, AI, and GenAI initiatives but always focusing on developing practices and transferring knowledge for our clients’ longer-term self-sufficiency.