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Transform your idea into a comprehensive LLM deployment plan, backed by AWS funding.

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HOW IT WORKS

Phase I: AI/ML & GenAI Maturity Assessment

AI/ML & GenAI maturity assessment report:
Current adoption level (incipient, intermediate, advanced).
Assess the existing tech stack and its compatibility with AI/ML and GenAI.
Identify potential AI/ML and GenAI use cases.

Phase II: Identification of Priority Use Cases

Prioritized list of use cases: Includes descriptions, expected impact, and required effort.
Definition of key metrics (KPIs): To measure the success of selected use cases.

Phase III: Technology Roadmap, Initial Architecture & Cloud TCO Analysis

Solution architecture to be implemented.
Timeline and activities for execution.
AWS cost estimation calculator.
Investment proposal to execute the project with AWS programs.

Start free in the AI Use Case Lab

See your use cases before you talk to us

Our free AI Use Case Lab runs the first pass on its own, in about ten minutes:

A prioritized shortlist of AI/ML and GenAI use cases, generated for your industry, stage and tech stack.
An impact-versus-effort matrix placing each one, so the first project picks itself.
Your own scores across seven weighted dimensions — team capability, ROI clarity, data readiness and four more.
A phased roadmap, and the AWS funding programs each use case qualifies for.
A PDF report to take into your next internal meeting.

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Fastest GenAI cloud Infrastructures for whatever you want to build

Unlocking the immense potential of Generative AI is essential for both startups and enterprises. To meet this challenge, binbash has developed a unique program offering founders and technical leaders expert guidance on deploying Large Language Models (LLMs) on AWS.

This framework covers critical areas such as model selection and tuning, decisions between training new models or using pre-trained ones, and hosting options including Amazon SageMaker and Amazon Bedrock.