This project eliminated guesswork, providing a clear guidance for a optimal model choice.
Meet our client
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Client’s Challenge
The client sought to build an intelligent personal data vault but faced a significant hurdle in selecting the optimal AI models to power the platform. The core challenge was balancing high-level performance, inference costs, and accuracy across diverse document types, such as tax forms, wills, and insurance policies.
Our Solution
We built a comprehensive AI Model Evaluation Framework to benchmark leading commercial and open-source models within an AWS environment. This involved cleaning and annotating real documents to establish a ground-truth and a reproducible pipeline of benchmarking scripts to measure accuracy, latency, and cost-per-inference, culminating in a data-driven analysis that compared model performance.
Client’s Benefits
This project eliminated guesswork, providing a clear guidance for a optimal model choice. By identifying the best-fit models for specific tasks, the client secured the accuracy needed for user trust while optimizing their long-term cloud spend. Additionally, the benchmarking tool and annotated dataset allow the client to pivot to new models as they hit the market, preventing vendor lock-in and significantly reducing future R&D costs.
