# Multimodal AI evaluation > Task-grounded benchmarks and human evaluation for multilingual text, audio, image, and video model behavior. [Multimodal AI capability](https://admas.net/capabilities/llm-solutions/index.md) ## The challenge A single aggregate score can look healthy while a model fails the languages, intents, or risk categories that matter to your product. We design evaluation around real product behavior and representative language communities. Automated measures are combined with calibrated human judgment where meaning and cultural context matter. Results are segmented so product and model teams can make release, routing, data, and adaptation decisions. ## How Admas works 1. **Define success:** Map product tasks, languages, user groups, harm areas, quality dimensions, and decision thresholds. 2. **Build the evaluation:** Create representative prompts, references, rubrics, evaluator guidance, and automated checks. 3. **Turn results into action:** Analyze failure patterns, compare systems, recommend interventions, and establish release gates. ## Typical outputs - Multilingual evaluation framework - Curated task and challenge sets - Human-evaluation protocol and calibration - Segmented results with model recommendations ## Frequently asked questions ### What is multimodal ai evaluation? Task-grounded benchmarks and human evaluation for multilingual text, audio, image, and video model behavior. In practice, the work is bounded by a defined product or model decision, named audiences and locales, representative inputs, and acceptance criteria that can be reviewed. ### When does a team need multimodal ai evaluation? A single aggregate score can look healthy while a model fails the languages, intents, or risk categories that matter to your product. The useful starting point is the smallest representative flow that can expose the cause, impact, and ownership of the problem. ### What does a multimodal ai evaluation engagement include? Define success: Map product tasks, languages, user groups, harm areas, quality dimensions, and decision thresholds. Build the evaluation: Create representative prompts, references, rubrics, evaluator guidance, and automated checks. Turn results into action: Analyze failure patterns, compare systems, recommend interventions, and establish release gates. ### What should we provide before multimodal ai evaluation starts? The most useful inputs are the product task and user journey, candidate models or system access, priority languages and communities, policies and risk thresholds, representative prompts, media, tools, and and expected outcomes. Admas can begin with a partial package, but missing context, rights, access, owners, or acceptance criteria will be made visible in the plan rather than treated as harmless assumptions. ### What does Admas deliver for multimodal ai evaluation? Typical outputs include multilingual evaluation framework, curated task and challenge sets, human-evaluation protocol and calibration, and segmented results with model recommendations. Deliverables are adapted to the team that must use them, with decisions, evidence, limitations, owners, and next actions made explicit. ### How is the quality of multimodal ai evaluation evaluated? Quality is measured against the real task and risk. Relevant evidence can include task success by language and scenario, human-rated meaning and usefulness, safety and policy performance, retrieval and citation fidelity, tool-call correctness, and regressions and disparities hidden by aggregate scores. Sampling, severity rules, reviewers, adjudication, and pass or fail thresholds should be agreed before the result is used as a release decision. ### Can AI replace the human work in multimodal ai evaluation? Models and automated checks can generate candidates, expand test sets, cluster failures, and accelerate analysis. Qualified humans define what good means, identify culturally or linguistically plausible failures, adjudicate close cases, and own consequential release judgments. The right allocation depends on consequence, content stability, available references, language coverage, reversibility, and the cost of a plausible but wrong result. ### How much does multimodal ai evaluation cost? The estimate changes with number of languages, modalities, systems, and scenarios, risk level, dataset creation needs, evaluator specialization, sampling and adjudication depth, and experiment and reporting cadence. Pricing should distinguish setup and discovery, repeatable units, specialist or engineering time, independent review, management, and external costs. A low unit price is not comparable if it excludes the QA cycle or shifts rework back to the buyer. ## Related multimodal ai services - [Model localization & adaptation](https://admas.net/capabilities/llm-solutions/model-adaptation/index.md): Data and adaptation strategies that improve model behavior for specific languages, cultures, modalities, and product tasks. - [Retrieval, agents & safety](https://admas.net/capabilities/llm-solutions/retrieval-safety/index.md): Grounded generation, language-aware retrieval, agent behavior, policy evaluation, and safeguards across markets. - [Multilingual agent engineering](https://admas.net/capabilities/llm-solutions/multilingual-agent-engineering/index.md): Agent architectures that preserve locale, language, meaning, and policy across prompts, memory, retrieval, tools, and actions. - [Multimodal safety evaluation](https://admas.net/capabilities/llm-solutions/multimodal-safety-evaluation/index.md): Language- and culture-aware safety evaluation across text, speech, images, video, retrieval, and model-mediated actions. ## Start a project - [Build a project brief](https://admas.net/start-a-project/index.md?focus=llm-solutions): Tell Admas what you are building, which modalities and languages matter, and where progress is blocked.