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NIX developed a comprehensive LLM evaluation platform connector to benchmark OpenAI and AWS Bedrock models.
Internet Services and Computer Software
Data Science, AI, Chatbot, Generative AI
AWS Bedrock, Python, GIT, Prompt Engineering
Our client is an experienced technology company specializing in enterprise-grade AI solutions for highly regulated and sensitive sectors like FinTech and the healthcare industry. Their core offering is a proprietary LLM evaluation platform that benchmarks various AI agents to define which specific models deliver the optimal balance of accuracy, latency, and transparency for their customers’ unique requirements. By utilizing this platform, their customers can compare the results of the models, examine the reasoning, filters, and rules used, and identify the segment of data that was used to achieve this result.
Historically, the client’s expertise and infrastructure were centralized exclusively within the OpenAI ecosystem. Recognizing the strategic necessity of diversifying their LLM testing offerings to meet the growing AI market, the client partnered with NIX to expand their platform’s capabilities through a robust proof of concept (PoC). Our objective was to engineer a seamless integration layer—a universal connector bridging their existing OpenAI-based framework with the diverse suite of foundation models available via Amazon Bedrock.
During the development of the AI connector and training the Bedrock models, our specialists had to adhere to the core client’s principles, namely:
The client’s existing evaluation workflow relied on a structured process, leveraging OpenAI models alongside specialized libraries to parse PDFs and unstructured text. The models would generate 5–10 targeted questions, extract answers via text parsing, and build a gold-standard dataset to assess model accuracy and precision.
The client’s ecosystem operates across four distinct functional pipelines:
To diversify the client’s capabilities, our team integrated and compared three leading foundational models within Amazon Bedrock: Anthropic Claude, Mistral AI, and Meta Llama. Given the highly sensitive nature of the client’s domain, we developed the AI connector in a strictly secured environment via GitHub, eliminating the risk of unauthorized access and ensuring that all integration code and LLM evaluation framework met enterprise security standards.
The PoC was executed in two strategic phases:
The result was a unified interface that united OpenAI LLMs with Amazon Bedrock models. This allowed the company to provide its customers with a full spectrum of AI models, offering precise, transparent Bedrock LLMs for comprehensive training that meet their unique business needs.
Upon the successful completion of the PoC, the client expanded their service offering beyond OpenAI, integrating the full suite of Amazon Bedrock foundation models into their LLM evaluation platform. This diversification allows their enterprise customers to choose the specific model that best aligns with their security, speed, and accuracy requirements. Our comprehensive testing of the three Bedrock models yielded high-performance LLM evaluation metrics across critical categories:
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