Overview
The Gen AI Proof of Concept (POC) provides a structured and low‑risk way for organizations to validate how Generative AI can deliver measurable value within their AWS environment. Through a focused engagement, we collaborate with your business and technical teams to identify a high‑impact use case, assess data readiness, and design an architecture that leverages AWS services such as Amazon Bedrock, Amazon Q, Amazon SageMaker, and relevant foundation models.
The POC begins with strategy alignment to define objectives, review your existing AWS footprint, and select an achievable yet meaningful use case such as text generation, summarization, search enhancement, content creation, image generation, or personalization. We then assess and prepare the data required for the model, develop embeddings or preprocessing pipelines as needed, and design a clear POC architecture that includes API integrations, inference endpoints, and optional lightweight application interfaces for user testing.
Our team then develops the working prototype using AWS best practices, performing targeted fine‑tuning or model configuration to align outputs with your requirements. Functional testing and prompt refinement ensure the POC demonstrates performance, relevance, and operational feasibility. We conclude with a results evaluation, reviewing model accuracy, user experience, costs, and technical alignment. You receive a complete feasibility assessment, a TCO estimate, an architecture diagram, and a recommended roadmap for scaling into a production‑grade implementation. Where eligible, we also identify AWS POC funding programs that may offset costs.
This engagement provides a clear, evidence‑based view of GenAI’s potential within your organization and establishes the foundation for future adoption.
Highlights
- Rapid validation of a high‑value Generative AI use case with clear objectives, low risk, and measurable business outcomes within your AWS environment.
- End‑to‑end POC delivery including use case selection, data readiness assessment, architecture design, and model configuration using Amazon Bedrock, SageMaker, and AWS AI services.
- Evidence‑based results and scale‑out roadmap with performance insights, TCO estimates, feasibility analysis, and recommendations for production deployment and AWS funding eligibility.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Pricing
Custom pricing options
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