Sold by: Provectus
Provectus has proven expertise building and migrating AI use cases to AWS, including scalable platforms using services like Amazon Bedrock, AgentCore, and SageMaker. We help clients define evaluation criteria to identify the best foundational model(s) for each use case, then build platforms that align to our clients' internal standards while keeping their data on AWS. Expect initial results in as little as a few months.
Overview
Provectus has experience deploying Generative AI and Machine Learning use cases on AWS across a range of enterprise industries. The Provectus approach is to dedicate a cross-functional AI pod to accelerate the steps needed to migrate the workload to AWS, evaluate and refine models, and add new features that delight end users. We encourage our customers to involve Business Leaders, Data and AI/ML Leaders, and IT professionals to increase use case adoption and business functionality, measure success criteria across the organization, and help determine the return on investment.Our client use cases range in complexity and data sizes.
A few examples: Document Intelligence: turn complex documentation into meaningful insights that can be trusted to make high-value business decisions in minutes. Expert Review Accelerator: speed up manual tasks like extracting insights from large, complex document sets — dramatically reducing the time, effort, and cost of expert review processes. Generative AI Assistant: surface insights from external and private data sources like contracts, support documents, collaboration platforms, messaging apps, and task management systems to accelerate answers to essential questions. Market & Risk Intelligence: extract insights from historical data, reports, and current market conditions to identify opportunities and risks and support better decisions.
Highlights
- Roadmap from prototype to production: A clear deployment schedule that moves AI workloads from proof-of-concept to production on AWS, with milestones for model evaluation, refinement, and rollout.
- Adoption-driven feature prioritization: Features are sequenced to deliver immediate value to end users, accelerating adoption across the enterprise and shortening the path to measurable ROI.
- Ongoing support and feature delivery: Post-launch support that adds new data sources, incorporates user feedback, and ships customer-requested features to keep the platform improving over time.
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