What Are Ways You Can Incorporate Generative AI?
Conversational User Interface
Patterson Consulting can build a conversational user interface over any enterprise systems. This allows end users to naturally interact with legacy business systems using plain natural language. Use methods like Knowledge Graphs and Retrieval Augmented Generation to integrate a natural language interface over any corporate knowledge repository.
Workflow Automation
- Accelerate repetitive workflow tasks
- Keep humans in the loop but make them faster
- Let users focus on more important work
Scenario Analysis
Let a specialized agent take custom natural language parameters and run specific scenario simulations based on private enterprise knowledge.
Case Study:
Learn how Quantatec integrated generative AI into their Movias logistics platform.
Quantatec wanted a large language model (LLM) to answer customer’s questions about their own fleet data and Patterson Consulting helped us to integrate the LLM properly and provide consistent quality answers to the user.
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Further Reading on Generative AI
Below are some of our publications on Generative AI.
Whitepaper: Generative AI and Insurance
Download our whitepaper on how Generative AI is revolutionizing the insurance industry by streamlining complex processes and enhancing decision-making.
Why is Generative AI Important to Insurance?
The rapid adoption of generative AI is strikingly different from previous technological shifts like cloud computing and big data. Unlike the gradual uptake by smaller, agile companies, AI's integration is being driven by large companies' executives who mandate its use across their organizations.
DownloadWhitepaper: What is Data Engineering?
Download our whitepaper on how data engineers support the needs of data scientists, analysts, and other key stakeholders within an organization. Learn how data engineering provide the crucial data infrastructure for data-driven insights and strategic decisions.
How Does Data Engineering Impact My Organization?
Data engineering is the discipline focused on designing, constructing, and maintaining the systems that manage the reliable and efficient processing of large volumes of data. This encompasses tasks such as collecting, storing, transforming, and organizing data to make it accessible and usable for analysis, reporting, and decision-making.
DownloadWhitepaper: An Introduction to LLMs
Download our whitepaper to get an overview of how large language models are useful and how they are changing knowledge work in business.
Better Understanding Large Language Models
In this article we cover topics such as "prompt engineering" and "vector databases" to give an executive overview of "what are LLMs?". We also dive head-first into the looming existential dread for the knowledge worker, how this has played out over the past 200 years where technology changed society and the implications.
DownloadCommon Questions About our Generative AI Engagement Process
Here are some of the commons questions asked.
We integrate generative AI with our partners
Our latest blog resources
Articles on Generative AI from the Patterson Consulting team
Building Natural Language User Interfaces over Analytics Applications
Using LLMs and the Semantic Layer to Expand the Analytics User Base
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Evaluating the Reasoning Ability of LLMs
Use LangChain to evaluate the reasoning ability of a local LLM.
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The Evolution of RAG
This article delves into "What is a technical definition of RAG?" and "How is RAG evolving?"
ReadLet's talk about your Generative AI project.
Patterson Consutling engineering teams help you build out data pipelines and generative AI capabilities. We specialize in integrating cutting-edge Generative AI capabilities into legacy and new systems alike.
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