Stax

Product Introduction

1. Stax is an AI evaluation platform developed by Google Labs designed to systematically measure and benchmark generative AI models and applications against user-defined criteria. It provides tools to build custom autoraters, manage datasets, and analyze performance metrics to replace subjective manual testing with data-driven insights. The platform supports all major model providers and enables end-to-end evaluation workflows from experimentation to production readiness.
2. The core value of Stax lies in its ability to transform AI development by replacing generic benchmarks with tailored evaluations aligned with specific product requirements. It empowers teams to quantify improvements in model performance, prompt engineering, and AI agent orchestration through repeatable testing processes. This ensures developers can deploy AI systems with measurable confidence in quality, cost-efficiency, and reliability.

Main Features

1. Stax enables the creation of custom evaluators using Python or natural language to define metrics like fluency, safety, or domain-specific criteria, which automatically score AI outputs against user-configured thresholds. These autoraters support both LLM-based scoring and programmatic rules for hybrid evaluation strategies. Users can also leverage pre-built system evaluators for common quality checks while retaining full control over evaluation logic.
2. The platform provides managed datasets and project templates to execute batch evaluations across thousands of inputs, comparing multiple models (including Gemini, GPT-4, Claude, and custom endpoints) simultaneously. This feature integrates version control for prompts and model configurations, enabling systematic A/B testing of AI iterations with historical performance tracking through visual dashboards.
3. Stax offers granular analytics with performance heatmaps, cost-latency-quality tradeoff comparisons, and drift detection across evaluation runs. The analytics engine aggregates results from custom metrics and ground truth data, generating exportable reports that highlight statistically significant improvements or regressions in AI behavior. This includes real-time monitoring of production systems through API integrations.

Problems Solved

1. Stax addresses the inherent challenge of objectively assessing generative AI systems where outputs are non-deterministic and lack single correct answers. It eliminates "vibe-based" decision-making by providing quantitative metrics for subjective qualities like creativity and factual accuracy. The platform automates the detection of model hallucinations, prompt injection vulnerabilities, and consistency failures across diverse input scenarios.
2. The tool primarily serves AI developers, ML engineers, and product teams building LLM-powered applications requiring rigorous quality assurance. It is particularly valuable for enterprises deploying chatbots, content generation systems, and complex AI agent workflows that demand measurable compliance with functional and safety requirements. Regulatory-focused industries like healthcare and finance benefit from its audit-ready evaluation trails.
3. Typical use cases include comparing 5-10 foundation models during prototyping phases using custom relevance scoring, validating prompt engineering changes against 500+ edge-case queries, and stress-testing retrieval-augmented generation (RAG) systems for document grounding accuracy. Production teams use Stax to monitor live AI services, triggering alerts when response quality drops below predefined service-level objectives.

Unique Advantages

1. Unlike generic AI evaluation tools, Stax provides native integration with Google's AI infrastructure while maintaining provider-agnostic support for OpenAI, Anthropic, Mistral, and self-hosted models through a unified API layer. This cross-platform compatibility enables direct performance comparisons between proprietary and open-source LLMs without data silos. The platform uniquely combines automated metric calculation with human-in-the-loop evaluation workflows for hybrid validation.
2. The platform introduces patent-pending "evaluation chaining," allowing users to create multi-stage assessment pipelines where initial LLM-based scoring feeds into programmatic validation rules. This innovation enables complex quality checks like verifying citation integrity in RAG outputs before measuring response coherence. Another breakthrough feature is synthetic test dataset generation using adversarial AI techniques to probe model weaknesses.
3. Stax's competitive edge stems from its enterprise-grade scalability, handling evaluations across 100,000+ test cases with parallel execution clusters while maintaining per-inference cost tracking. The platform offers on-premise deployment options with air-gapped data security, differentiating it from cloud-only competitors. Its evaluation templates for GDPR compliance and AI Bill of Materials (AI BOM) reporting provide regulatory advantages in controlled industries.

Frequently Asked Questions (FAQ)

1. What is Stax's data privacy policy? Stax does not use user data to train Google models and allows full data ownership with export/delete capabilities through its dashboard. All data transmission uses AES-256 encryption, with optional private cloud deployment for enterprises requiring HIPAA-level compliance. Third-party model API calls (e.g., to OpenAI) adhere strictly to each provider's data retention policies.
2. How does Stax handle evaluation of non-English language models? The platform supports 48 languages through Unicode-compliant text processing and locale-specific evaluators for metrics like grammatical correctness. Users can create language-specific ground truth datasets or use Stax's multilingual toxicity detection models covering 15 languages. Custom token counters and latency metrics adapt to non-Latin character sets.
3. Can Stax evaluate real-time production AI systems? Yes, through API webhooks that sample live traffic for continuous evaluation without impacting service performance. The production monitoring module calculates rolling 24-hour averages for key metrics and integrates with incident management platforms like PagerDuty. Users can configure automated rollback triggers when error rates exceed thresholds.