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Evaluate: Run Trusted Evals Across Your AI Portfolio

A central platform that runs consistent, reliable evaluations across every AI application your teams build. From performance to bias, robustness, and safety - profit from ready-to-run eval packages and alignment to business risks.

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Pain Points

Evaluations Are Inconsistent, Disconnected, and Slow to Build

Fragmented evaluations can't be compared, reused, or tied to governance.

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Every Team Runs Evaluations Their Own Way

Each team uses different tools, methodologies, and rigor. Results can't be compared, knowledge can't be transferred, and the portfolio can't be governed as a whole.

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Tooling and Datasets Slow Every Evaluation Cycle

Engineers lean on public benchmarks of dubious quality. Brittle integrations and mid-run failures turn every evaluation into a bottleneck.

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Technical Results Don't Map to Governance

Evaluations output a wall of metrics with no clear link to standards, regulations, or internal frameworks. Governance teams can't tell whether a system meets the bar.

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Building Relevant Evaluations Takes Weeks

Designing metrics, curating datasets, and setting up the technical infra for each use case demands domain expertise most teams don't have on staff.

Product Features

The Evaluation Infrastructure Your AI Portfolio Needs

Learn how LatticeFlow AI turns evaluations into a repeatable, portfolio-wide capability.

Connect to Any AI System

Custom model adapters connect Evaluate to any AI system, in any ecosystem and across cloud and on-prem deployments.

  • Ecosystem Agnostic: Custom adapters connect to any model endpoint or deployment.
  • Agent-Ready: Evaluate connects to and tests agentic AI systems out of the box.
Model adapter and connection configuration screenshot

Declarative, Shareable Methodology

Define evaluations declaratively instead of writing custom code. Speed up definition and simplfiy maintenance of your evaluations.

  • Transparent and Repeatable: YAML definitions make methodology explicit, not hidden in someone's notebook.
  • Shareable Across Teams: Codify best practices once. Reuse it across every application in the portfolio.
YAML evaluation definition file screenshot

Battle-Tested Execution Engine

The Evaluator service runs evaluations reliably at scale, so that engineers spend time on results, not error logs and infrastructure.

  • Reliable at Scale: Robust job management, caching, and clean error handling keep evaluation cycles fast and reproducible.
  • Fully Traceable: Every result includes a unique reference to the dataset, model version, and metric definition used.
Evaluation run and job management UI screenshot

Ready-to-Run Evaluation Packages

Skip weeks of manual setup with pre-built evaluation packages from AI Atlas - aligned with the standards and frameworks that matter to your use case.

  • Kickstart your Evaluations: Run governance-aligned evaluations in minutes instead of building them from scratch.
  • Built on LatticeFlow Expertise: Packages bundle years of evaluation know-how from industry and academia.
Evaluation package selection screenshot

Synthetic Dataset Generation

Generate evaluation datasets directly from the context of your AI application: knowledge base, documentation, or code.

  • Use Case-Specific: Evaluation datasets reflect what your application actually does, instead of testing generic capabilities.
  • Faster Time-to-First-Eval: Bootstrap an evaluation in hours instead of weeks of manual test case curation.
Synthetic dataset generation flow screenshot

Governance-Aligned by Design

The LatticeFlow AI platform maps results to the risks, controls, and frameworks that matter.

  • From Metrics to Risk Decisions: Map raw results to specific governance requirements and risk scores.
  • Packaged Interpretaion Methodology: AI Atlas pacakges relevant interpretation methdology based on industry best practices.
Evaluation results mapped to framework screenshot
How It Works

Evaluate is the Heart of the LatticeFlow AI Platform

Evaluate produces the key technical evidence underlying your risk decisions.

  1. 1Use Atlas to identify which evaluations your use case requires
  2. 2Configure and run evaluations with Eval, scoped by the system graph from Discover
  3. 3Explore traceable results in the Evaluate UI: metrics, samples, and full context
  4. 4Feed results into Govern for interpretation, dashboards, and continuous oversight
LatticeFlow AI platform infographic

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.
Atlas tells you what to evaluate. Evaluate runs the evaluations. Govern interprets results into risk decisions. Discover gives Evaluate the system context it needs for accurate, targeted assessments.
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One Evaluation System for Your Entire AI Portfolio

Connect any AI system, run governance-aligned evaluations, and get traceable results. No building from scratch.

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