Automat-it LLM Selection Optimizer

Choosing the right Large Language Model (LLM) for your workload is a decision that directly impacts your application's performance, accuracy, and operational costs.

Many organizations default to the largest, most capable models—assuming bigger means better—only to discover they're overpaying or using the largest models for all tasks, reducing profitability.

The LLM Selection Optimizer is a fixed-scope engagement where Automat-it benchmarks your actual production data against Amazon Bedrock's model catalog to identify the optimal model (or combination of models) for your specific use case.

Unlike generic model comparisons, this engagement uses your real datasets and workflows to measure what matters: latency, accuracy, throughput, and cost-per-request for your workload.

Our team analyzes whether complex tasks can be decomposed into simpler sub-tasks handled by lighter, faster models. A technique that frequently reduces inference costs by 40-60% while maintaining or improving response quality. The deliverable is a comprehensive report with quantified performance metrics, cost projections, and a clear recommendation for your Amazon Bedrock implementation.

Automat-it brings deep expertise in generative AI and machine learning across industries, with a team of ML engineers and data scientists who have optimized LLM deployments for startups at every stage. We handle the technical heavy lifting, you provide the data and requirements, and you receive actionable intelligence to make a confident, data-backed model selection decision.