Ready-to-Run Evaluation Packages
Kickstart your evaluations with pre-built evaluation packages from AI Atlas - aligned with the standards and frameworks that matter to your use-case.
EvaluateOne Evaluation System for Your Entire AI Portfolio
Generic evals with inconsistent methodologies leave teams flying blind.
Our evaluations target your use-case and are pre-aligned to governance frameworks.
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Evaluation Features
The Agent Evaluation Infrastructure Your AI Portfolio Needs
Learn how LatticeFlow AI turns evaluations into a repeatable, portfolio-wide capability.
Fully customizable, repeatable, and transparent
No-Code declarative YAML massively speed up evaluation definition and simplify maintenance. Your methodology is explicit and auditable, not buried in your codebase. Let our evaluation engine handle the complexity of determinism and repeatability.
Red-Teaming Features
New Technology, New Security Threats
Security teams are not yet equipped to assess the attack surfaces that AI Agents introduce.
Keep Your AI Estate Protected. Always
Automated red-teaming and system-level security checks for AI applications, aligned with OWASP, MITRE ATLAS, and the frameworks your governance teams require.
Agent-Based Red-Teaming
An adaptive red-teaming agent probes your AI system across multiple attack strategies covering data leakage, unauthorized actions, privilege abuse, goal hijacking, and denial-of-wallet. Identify permission gaps, missing auth controls, and misconfigured access before deployment.
Frequently Asked Questions
Evaluate is a central execution engine for running technical evaluations on AI applications, covering performance, quality, bias, robustness, and safety, with consistent methodology across teams.
Evaluate supports any evaluation that can be expressed as a metric and dataset, including performance, quality, privacy, bias, fairness, robustness, and safety. Security evaluations are run through the same engine via Secure.
Evaluation packages are ready-to-run technical tests, curated by LatticeFlow AI and aligned with specific governance frameworks via Atlas. They let teams generate measurable evidence without designing evaluations from scratch.
Usually no. Synthetic dataset generation creates evaluation datasets directly from your application context: knowledge base, documentation, or code. Some QA and curation are typically still required, but manual test case authoring is no longer the default starting point. Of course, if you already have “golden” evaluation datasets, these can be integrated directly into Evaluate.
Evaluate is ecosystem agnostic. Custom model adapters connect to any model endpoint, deployment mode, or ecosystem, including agentic AI systems.
Turn AI Risk into AI Advantage
See the LatticeFlow AI Platform in Action.