Quantinev GenAI Readiness Assessment on AWS

Most organizations know Generative AI matters but struggle to answer three questions: Are we ready? What should we build first? What will it cost to run safely on AWS? The Quantinev GenAI Readiness Assessment answers all three in 4–6 weeks.

What we assess

1. Current-state readiness across six pillars: data (quality, accessibility, lineage, unstructured content), infrastructure and MLOps maturity, security and identity, governance and compliance, cost and FinOps posture, and team skills and operating model. Each pillar is scored against the AWS Well-Architected Generative AI Lens and the AWS Cloud Adoption Framework for AI (CAF-AI).
2. GenAI use-case discovery: structured workshops with business and technology stakeholders to surface candidate use cases such as knowledge assistants and RAG, intelligent document processing, customer-support automation, developer productivity, and agentic workflows.
3. Prioritization: each use case is scored on business value, technical feasibility, data readiness, risk, and time-to-value, producing a ranked backlog and a recommended first pilot.
4. AWS service mapping: each priority use case is mapped to the right AWS services, including Amazon Bedrock (foundation models, Knowledge Bases, Agents, Guardrails), Amazon SageMaker AI, Amazon Q Business and Amazon Q Developer, Amazon Kendra, and Amazon OpenSearch Service, plus supporting data services such as Amazon S3, AWS Glue, and AWS Lake Formation.
5. Responsible AI and security review: data privacy, prompt-injection and data-leakage risks, model evaluation, human-in-the-loop controls, and guardrail configuration aligned with AWS responsible-AI practices.
6. Target architecture and cost: a secure, scalable AWS reference architecture for your top use cases, with a total cost of ownership (TCO) estimate covering model inference, tokens, vector storage, and operations, plus cost-optimization levers.
7. Phased roadmap: a 30/60/90-day implementation plan with milestones, roles, risks, and success metrics.

Deliverables

• GenAI readiness scorecard (six pillars)
• Prioritized use-case backlog with a recommended first pilot
• AWS target reference architecture
• Responsible-AI and guardrails checklist
• TCO estimate with cost-optimization levers
• 30/60/90-day implementation roadmap
• Executive readout

How it works

• Week 1: Kickoff, stakeholder interviews, and discovery questionnaire.
• Weeks 2–3: Technical deep-dives on data, platform, and security; use-case workshops.
• Weeks 4–5: Scoring, prioritization, architecture design, and TCO modeling.
• Week 6: Executive readout and handover of all deliverables.

Who it is for CTOs, CIOs, CDOs, and innovation leaders at startups, scale-ups, and enterprises who want a clear, defensible GenAI plan before committing budget, and teams that have run GenAI experiments but need a path to production.

Why Quantinev Quantinev is an AI innovation lab and AWS Partner specializing in Generative AI on AWS. We combine hands-on Amazon Bedrock and SageMaker AI delivery with FinOps expertise, so every recommendation is secure, production-ready, and cost-aware. Where eligible, we help customers explore AWS partner funding programs to offset pilot costs.

Pricing is fixed-fee and scoped to your organization through an AWS Marketplace private offer, so the engagement can count toward your AWS commitments and be consolidated on your AWS bill. Contact us to request a private offer.