Harness AI Transformation for your Business: A Strategic Partnership for Amazon Bedrock AI Services
Exciting times for Trace3, as we’re partnering with Effectual to deliver cutting-edge artificial intelligence (AI) solutions on Amazon Web Services (AWS).
Effectual is an innovative cloud-first managed and professional services company. By combining Trace3’s expertise in cloud, data center, and workplace technologies with Effectual's prowess in AWS cloud-native development, machine learning, and DevOps, we now offer a suite of professional services for building, deploying, and managing AI applications in Amazon Bedrock. This end-to-end offering enables your businesses to quickly harness the power of AI, machine learning, and data analytics on AWS.
The Synergy of Trace3 and Effectual: Delivering Amazon Bedrock AI Services
The technical cornerstone of this collaboration is providing a secure AI foundation, including the integration of Retrieval Augmented Generation (RAG) in Amazon Bedrock.
Retrieval Augmented Generation (RAG): This approach combines a retrieval model with a generative model to produce more coherent and factual text outputs. In this, a retrieval model first searches through existing texts to find relevant information to the input query or context, providing a grounding for the generative model that uses the retrieved information, as well as the original input, to generate a response.
RAG and private datastores allow us to form Gen AI-backed access to sensitive data sets or areas of organizational knowledge. It enables your organization to create value from the existing business data you have today, for example connecting it to your document store for human-like conversational interactions with your organization’s FRPs, policies, CRM, and more.
The key advantages of retrieval augmented generation are:
Allows the generative model to leverage external knowledge sources, leading to outputs more factual, specific, and grounded in reality. Using RAG helps reduce and prevent generative model hallucination.
Provides relevant context to guide the generation, making responses more on-topic, focused, and coherent.
Enables your organization to create value from your existing business data.
Reduces repetition and contradiction in generated text, since the model can refer back to what was retrieved.
Requires less data training for the generative model since it relies on pre-existing texts for knowledge.
Using RAG for business-relevant and contextualized responses makes it an invaluable asset for industries ranging from healthcare and finance to e-commerce and beyond. By harnessing RAG, we help clients unlock the full potential of their data, paving the way for data-driven strategies and intelligent automation.
"Trace3 has roughly 1300 employees working on average 48 weeks a year at 40 hours every week generating artifacts, deliverables, responses to RFPs, and producing knowledge. This is a pivotal moment for us as we imagine capturing that knowledge and producing ongoing value through the use of Gen AI!" – Chris Nicholas, SVP, Cloud @ Trace3 Evolve 2023
Ensuring Security and Compliance with Bedrock Foundation and NIST AI-RMF
In the digital age, security and compliance are non-negotiable aspects of any technology implementation. To address these critical concerns, Trace3 and Effectual provide our clients with an Amazon Bedrock secure foundation, which adheres rigorously to the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF).
Bedrock Secure Foundation: The Bedrock secure foundation will protect your data and establish your company’s AI ecosystem in a trusted environment. The foundation encompasses a comprehensive array of security measures and best practices to safeguard your data, infrastructure, and applications using Amazon Bedrock.
NIST AI-RMF: The NIST AI Risk Management Framework provides guidance on managing risks associated with the design, development, deployment, and operation of artificial intelligence (AI) systems. The framework outlines a life cycle approach to identifying AI risks across areas that include fairness, explainability, safety, privacy, and security. The AI-RMF aims to foster responsibility and reduce unintended bias and harm from AI systems. It provides a principled foundation for auditable, reliable, and transparent AI through recommended practices such as documentation, traceability, testing, and continuous monitoring. The flexible framework allows organizations to align AI risk management with their values and risk appetite. It complements technical AI safety practices and ethical AI practices. The AI-RMF helps organizations build and operate AI responsibly throughout the AI system lifecycle.
By aligning with the NIST AI-RMF, we commit to ensuring our AI services not only deliver innovation but also meet the highest standards of security and compliance.
We [Trace3] have a hyper-focus on business value and helping our clients achieve their strategic business outcomes with AI. – Darren Patterson, AWS Cloud Practice Director
Conclusion: The Future of AI-Powered Transformation
As businesses increasingly recognize the potential of AI in driving strategic outcomes, Trace3 and Effectual are set to be your go-to services partners to harness the full power of AI while safeguarding your operations against cyber threats. The future of AI-powered transformation is here, and it's being shaped by this visionary collaboration with Effectual – delivering innovation, security, and compliance in equal measure.
In this age of rapid technological advancement, businesses must adapt and thrive. With Trace3 and Effectual as trusted partners, you can confidently embark on your AI journey, knowing you have the expertise and security at hand to turn your strategic aspirations into reality.
Darren Patterson has extensive experience in public Cloud with a focus on operationalization, security, and DevOps. With over 20 years in IT across multiple industries, an MBA and CISSP, Darren has broad experience leading strategic Cloud initiatives.
Throughout his career, Darren has specialized in architecting, building, securing, and supporting production cloud environments in AWS, Azure, and GCP. He has a passion for building sustainable cloud operations.
Darren grew up in California with a love for hiking, backpacking, climbing, snowboarding, camping and fishing in the Sierras. He studied Computer Science in undergrad at San Jose State University, and completed his MBA at the University of Denver in 2014. Between spending time with his wife and three kids, he has been exploring fly fishing, camping and snowboarding in the Rockies.