GenAI

GenAI in production, on your infrastructure and your data

Assistants, agents and automation with Amazon Bedrock, built by the same team that already runs your cloud. From use case prioritization to production, with evaluation and cost under control.

Betterfly: 10,000+ users across 5+ countries

You don't build AI in a vacuum: you build it on your infrastructure, your data and your security. So it makes sense for the partner who knows your cloud end to end to build it. This line runs from the initial assessment to agents in production, with the same engineering and FinOps discipline we apply to everything else.

Featured case

Betterfly

For Betterfly we built 'Buddy AI', a multichannel GenAI assistant: RAG over 1,000+ documents, 5+ conversational personalities and ticketing integration. It now serves 10,000+ users across 5+ countries. We launched it in 8 weeks.

Built on AWS, with Amazon Bedrock

We choose the model by result and by cost, not by hype. We work with Amazon Bedrock as the foundation (including Anthropic's Claude models) and we're on track for the AWS Generative AI competency, on top of our Advanced Tier, EKS Service Delivery and Migration Competency credentials.

Before the AI, the data

If the use case depends on your internal knowledge, the outcome is decided in the data preparation. That part lives in the Data & Analytics line and runs in parallel.

See Data & Analytics

How we work

01

Discovery

We identify the highest-impact use case and validate technical and data feasibility. No hype: we prioritize what moves the needle.

02

Plan & Quote

We design the architecture (model, data, security, cost) and propose a tight MVP at a closed price.

03

Execution

We build the MVP, evaluate it with real metrics and iterate until it's production-ready.

04

Hand-off & MSP

We leave it in production, monitored and cost-controlled. We can keep operating and evolving it with you.

FAQ

Do you build GenAI from scratch or on what I already have?

Both, but the most common and most valuable path is adding GenAI on top of the infrastructure and data you already have on AWS.

Which models do you use?

Mainly Amazon Bedrock, which includes models such as Anthropic's Claude, depending on the use case. We choose the model by result and by cost.

Do you have a real case in production?

Yes: Betterfly. We built 'Buddy AI', a GenAI assistant with RAG over 1,000+ documents that now serves 10,000+ users across 5+ countries, launched in 8 weeks.

How do you control AI costs?

With the same FinOps discipline we apply to the whole cloud: AI in production has to be sustainable, not a surprise on the bill.

Shall we talk about your case?

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