Artificial Intelligence

1. How to Experiment in AI
   The market offers a tremendous number of quickly evolving tools. All of them require some access to existing data, to new data, and quality control. Generative tools require careful thought for managing hallucinations, customer and situational tolerance for potential errors. Where does one begin?
2. How to Leverage Investments and Vendor Platforms
   Some products are net-new solutions uncoupled from legacy investments in data, data lakes and warehouses. Most however require access to these assets, and most data vendors now offer powerful AI tools. How should existing systems be accessed, and their tools leveraged, based on what I want to do?
3. How to Prepare Data for AI
   Use cases in Generative AI and more familiar Machine Learning impose requirements for data preparation. Generative use cases may involve classifiers and agents that are themselves consuming output from machine learning products and services.
4. Understand & Manage Cost, Now and Later: Deploying and maintaining AI systems can be expensive, with high upfront costs for hardware, software, and talent. There are also ongoing costs for data management, model maintenance, and infrastructure. Enterprises struggle to accurately forecast and budget for the total cost of AI initiatives.
5. Underutilizing Existing Analytics Capabilities: Most enterprises have existing analytics and business intelligence capabilities that could be leveraged to support AI initiatives. However, there is often a disconnect between these traditional analytics functions and emerging AI/ML capabilities, leading to underutilization of existing investments.

How Our Solutions Address These Issues

AI Value Consulting & Strategy
We have product innovation and engineering in our bones. We use that to help customers narrow in on the problems best solved with Machine Learning and AI, particularly generative AI. Quickly building experiments and proofs of concept (POC) is often the best way to sort out choices in architecture, data, data access, and cost. We leverage our experience in data and data platforms to ensure that you aren’t overlooking the benefits of AI tools in productivity acceleration. At the same time, our deep expertise in financial services on the cloud helps you anticipate and manage risks to privacy and security.

Gen AI Tools & Architecture
Generative AI is a field of active research and development. LLMs, RAG architectures, and related tools are new and rapidly changing. And yet, the tools you use are almost as important as the problems you choose to solve – not only for managing accuracy, but because the value you create might be overcome by the cost of the solution. Understanding how to leverage your current investments in ML, data, and cloud against tradeoffs in performance is key. Using our project accelerators, we can lay out your options and help make the selections that best suit your organizational needs.

AI & Data Integration
Effective AI solutions are connected to existing data, platforms, and data workflows, without disrupting them. Seamlessly fusing AI either onto your platform investments as a new product, or into your investments as an acceleration and optimization strategy, requires acute knowledge of both your vendor systems and their current state. Our deep expertise in cloud (AWS, Azure, Google) and platforms (Snowflake, Databricks, and others), along with our assessment accelerators and highly-certified consultants, help ensure your AI strategy is professionally integrated with the critical systems that feed it.