How Colibri Built a GenAI Evaluation Platform to Optimise Workloads on Amazon Bedrock

As the GenAI ecosystem continues to evolve at pace, enterprise leaders face a new wave of challenges: Which model is right for this use case? How do we compare performance in real time? Can we manage cost without compromising quality?

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At Colibri Digital, we’ve developed a solution to answer these questions - fast.

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Drawing on our deep experience as an AWS Premier Tier Services Partner and holder of the AWS Generative AI Competency, we built a model-agnostic evaluation and orchestration platform that helps enterprise teams route, test, and scale GenAI workloads with confidence. And critically, it’s now available on the AWS Marketplace.

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Why Model Evaluation Matters

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Enterprise AI deployments aren’t failing because of a lack of models, they’re stalling due to a lack of governance, observability, and cost control. While public model benchmarks provide a useful start, real-world use cases demand tailored evaluation: how does one model perform against another for your domain, your context, and your requirements?

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We built the AI Switchboard Agent to address that challenge. Running on Amazon Bedrock, our platform offers:

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• Comparative model benchmarking – Evaluates accuracy, coherence and relevance
  
  
• Real-time routing – Automatically sends queries to the best-suited model
  
  
• Cost-performance optimisation – Prioritises value and efficiency
  
  
• Plug-and-play deployment – Now live on the AWS Marketplace
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“Our goal was to simplify the last mile of GenAI adoption. One place to compare, route, and optimise workloads, with minimal integration and maximum confidence.”
— Jason Oliver, Principal Solutions Architect & AWS Ambassador, Colibri Digital

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What we Built (and Where it’s Going)

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We deployed the platform using Amazon Bedrock’s model-agnostic APIs, which allowed us to rapidly integrate and test leading LLMs without the overhead of managing custom SDKs or vendor-specific endpoints.

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With the AI Switchboard Agent, clients can:

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• Evaluate new models in minutes, not months
  
  
• Route by policy or use case (e.g., legal queries to Claude, creative to GPT)
  
  
• Test live traffic via A/B comparisons
  
  
• Track usage, drift, and environmental impact through integrated observability tooling
  
  

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And this is just the beginning. Our next release will extend support for models like Claude 3.5, Gemini, and Mistral, while introducing sustainability-aware routing and carbon impact tracking.

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Responsible AI isn’t Optional

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We believe that sustainability belongs in every AI conversation. That’s why we’re exploring AWS regions like Stockholm for their greener energy profiles, and factoring carbon efficiency alongside latency and cost when making routing decisions.

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It’s part of a wider principle: GenAI needs engineering discipline. That means thinking beyond demos and toward governance, cost visibility, scalability, and real-world alignment.

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“Whether you’re launching your first GenAI feature or managing a fleet of AI services, evaluation is the foundation of responsible growth.”
— Daniel Sadler, Data Scientist, Colibri Digital

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What it Means for you

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If you’re exploring GenAI at scale, this platform offers a zero-friction way to evaluate and optimise your options. It’s designed to integrate into enterprise architectures, minimise operational risk, and reduce the time from PoC to production.

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Available now on the AWS Marketplace:
AI Switchboard Agent

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Watch the AWS OnAir demo:

Summary

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• Challenge: GenAI adoption slowed by unclear model evaluation and poor routing
  
  
• Solution: Colibri's AI Switchboard Agent: a platform for comparing, routing, and optimising LLMs on Amazon Bedrock
  
  
• Outcome: Faster model testing, lower costs, better governance, with sustainability and scale built in

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