LastMile AI Platform: Streamline LLM App Development with Precision and Control

LastMile AI empowers developers to build, debug, and deploy AI applications with enterprise-grade reliability. Its AutoEval tool redefines evaluation workflows by enabling custom metric creation for RAG and multi-agent systems. Developers can upload app data, generate labels via LLM judges, and fine-tune evaluation models for metrics like faithfulness, toxicity, or custom criteria—ensuring apps meet precise quality standards.

The platform’s alBERTa model (400M parameters) delivers rapid 300ms inference on CPUs, generating numeric scores for real-time performance monitoring. Combined with Realtime Guardrails, it blocks hallucinations, toxicity, and safety risks during runtime.

Security-conscious teams benefit from VPC deployment and full data control, while a free tier and comprehensive API/docs lower entry barriers. For developers prioritizing performance, customization, and compliance, LastMile AI is the end-to-end solution to ship LLM apps with confidence.

Explore free access and enterprise options at LastMile AI.

Custom Evaluator Model Fine-Tuning

LastMile AI’s AutoEval enables developers to create and fine-tune custom evaluation metrics tailored to their AI applications. The workflow involves uploading application data, using an LLM judge to label outputs, and refining evaluation models to assess criteria like faithfulness, relevance, toxicity, correctness, and summarization. This function addresses the challenge of quantifying performance in complex AI systems, particularly for RAG (Retrieval-Augmented Generation) and multi-agent applications. By allowing custom metric creation, developers can align evaluations with domain-specific requirements, ensuring their LLM apps meet quality benchmarks before deployment. The fine-tuning service reduces reliance on generic evaluation frameworks, offering precision in identifying weaknesses. This feature directly supports performance monitoring and integrates with runtime guardrails to create a closed-loop system for continuous improvement.

Specialized Evaluation Model (alBERTa)

The alBERTa model is a 400M-parameter evaluation tool optimized for speed and efficiency, delivering inference results in 300ms even on CPU environments. It generates numeric scores for evaluation tasks, enabling quantitative analysis of AI outputs. Unlike larger LLMs, alBERTa balances accuracy with resource efficiency, making it ideal for real-time or large-scale applications. Its customization capability allows fine-tuning for niche evaluation needs, such as detecting industry-specific hallucinations or compliance violations. This model complements AutoEval by providing a lightweight, scalable alternative to LLM-based judges, reducing operational costs while maintaining evaluation rigor. Developers can deploy alBERTa alongside runtime guardrails to enforce quality thresholds during app operation, creating a cohesive evaluation-to-enforcement pipeline.

Real-Time Application Guardrails

The platform offers runtime guardrails to enforce safety, accuracy, and compliance during AI application operation. These guardrails use online evaluators (including alBERTa or custom models) to perform instant checks for hallucinations, toxicity, safety breaches, or user-defined criteria. For example, a customer support chatbot could block toxic responses before they reach end-users. This proactive mitigation of risks ensures reliable production deployments and reduces post-launch troubleshooting. The guardrails integrate with evaluation metrics from AutoEval, enabling dynamic adjustments based on performance data. Combined with VPC deployment options, this feature provides enterprises with granular control over data security and compliance, aligning with strict regulatory requirements while maintaining operational flexibility.