The GenAI wave hit fast, and it hit hard. Over the past year, we’ve seen LLMs go from experimental tools to critical infrastructure. But as enterprise teams move from exploration to production, they’re facing a new kind of complexity. The problem isn’t just choosing a good model. It’s choosing the right model, for the right use case, at the right time, and doing it consistently, efficiently, and with evidence.

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At Colibri Digital, our AI team encountered this challenge repeatedly in client projects, so we built a solution. We call it the AI Switchboard Agent, and it’s now live on AWS Bedrock. Here’s why we built it, what it does, and how it’s helping our clients scale GenAI responsibly.

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The Problem: GenAI Model Selection Is a Bottleneck

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Most clients love the idea of having multiple models to choose from, Claude, GPT-4, Amazon Titan, and others. But when it comes to actually deploying something in production, they quickly hit decision fatigue.

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What looks like optionality turns into model chaos. And for teams under pressure to deliver quickly, this is a serious blocker.

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We were seeing the same thing in our delivery cycles:

• Engineers debating which model stack to use
• Data scientists manually running benchmarks across different LLMs
• Business stakeholders asking for cost/performance trade-offs — but no clear way to answer

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We realised the underlying issue: there’s no standardised, scalable way to evaluate and route GenAI tasks across model combinations. So we stopped guessing. And started building.

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Why We Built the AI Switchboard Agent

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We needed a system that could:

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• Compare multiple foundational models
• Pair them with different embedding models for RAG-based tasks
• Apply both quantitative (latency, cost, output quality) and qualitative (tone, relevance, usefulness) evaluations
• Score them dynamically against the actual use case
• Route future traffic intelligently based on these evaluations

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This couldn’t be a spreadsheet. It had to be automated, scalable, and plug-and-play. That’s what led to the creation of the AI Switchboard Agent.

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How It Works

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We built the AI Switchboard Agent on top of AWS Bedrock, which gives us secure access to a range of LLMs via a unified API. Bedrock’s model-agnostic infrastructure makes it ideal for orchestrating a dynamic evaluation pipeline without the overhead of managing model endpoints ourselves.

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Here’s how the agent works:

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• Inputs: A set of GenAI tasks (real or synthetic), domain-specific prompts, and evaluation criteria
• Exploration Engine: It runs combinations of models, embeddings, prompts, and decoding strategies
• Evaluation Layer: It applies automated metrics like latency, token cost, embedding relevance scores, and precision/recall when applicable
• Human Feedback Loop (optional): For subjective attributes like fluency or tone
• Switchboard Logic: Based on all scores, it picks the best setup and routes incoming queries accordingly

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Think of it as a smart control tower that supervises model infrastructure and keeps optimisation front and centre.

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What It’s Solving for Enterprise AI

We didn’t build this to solve specific pain points in enterprise AI delivery:

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Evaluation Efficiency

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What used to take us a full week of model testing per use case is now completed in hours. That’s a 70% reduction in time-to-decision.

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Cost Transparency

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By comparing token cost vs. accuracy across models, we’ve helped clients make smarter trade-offs. Some moved away from high-cost LLMs where mid-tier models did just as well — resulting in up to 30% cost savings.

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Trust and Adoption

Having an evidence-based evaluation process builds confidence. Stakeholders no longer need to “trust the AI blindly” — they can see the evaluation scores, understand the model routing logic, and audit the output.

Faster Deployment

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With model-routing logic embedded in the pipeline, we’re helping clients ship GenAI features faster, with less rework and fewer surprises in production.

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Showcasing the Work at AWS OnAir

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We were proud to present the AI Switchboard Agent at AWS OnAir, a global showcase of production-ready AI solutions. It was an honour to share the stage with AWS Cloud Optimization and demo how we’re using Bedrock, LangChain, and internal evaluation tooling to build model-aware GenAI infrastructure. The response was immediate: This is what our team needs. We’ve been stuck in pilot purgatory, this could help us move faster, with confidence.

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That validation confirmed we’re not alone. Many teams are struggling with the “last mile” of GenAI adoption, not building the models, but choosing and operating them.

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What’s Next

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We’re extending the Switchboard Agent in new directions:

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• Live traffic A/B testing to evaluate in real time
• Per-use-case policy routing (e.g., “Use Claude for compliance queries, GPT for creative writing”)
• Integration with our GenAI observability stack, including explainability and drift detection

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And of course, every new model that drops , Gemini, Mistral, Claude 3.5 — we can evaluate it immediately with zero integration overhead.

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Closing Thoughts

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The AI Switchboard Agent isn’t just a tool. It’s part of our belief that GenAI shouldn’t rely on guesswork or gut feel. If we want to bring these technologies into high-stakes, production-grade environments, we need tools that reflect:

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• Engineering discipline
• Operational maturity
• Business alignment

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That’s what the Switchboard delivers: A rigorous, repeatable, explainable way to choose the right model for the job. The era of vibe-driven AI is over. Welcome to the age of intelligent orchestration.

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💬 Curious how the AI Switchboard Agent could fit into your GenAI stack?
We’d love to explore it with you. Whether you’re navigating model selection, RAG architecture, or LLM evaluation frameworks, the Colibri Digital team is here to help. Get in touch to see how we can bring clarity, speed, and confidence to your GenAI journey.

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