Capability 01
AI Quality & Intelligence Platform
Evaluation Platform
Measure LLM, agent, and RAG quality before failures reach production.
Evaluation Platform gives AI teams a repeatable way to test models, agents, retrieval systems, prompts, and workflows against representative datasets and business criteria. It combines automated checks, human review, experiment comparison, and production analytics so quality becomes an operating discipline rather than a one-time demo result.
Quality control loop Live
AI experiment
Evaluation Platform control loop
01Representative set
02Business rubric
03Regression run
04Release gate
Output · Evidence-backed release
Trace 01
Define success
Trace 02
Run evaluations
Trace 03
Compare and gate
Trace 04
Learn from production
Outcome model
Examples · not guaranteesWhat this platform is designed to change.
Repeatable
AI quality gates
Intended valueEarlier
Failure detection
Intended valueComparable
Model and prompt decisions
Intended valueEvaluation reduces uncertainty but cannot prove that an AI system will be correct in every real-world situation.
Capabilities
The system behind the experience.
The core building blocks Evaluation Platform brings together for a production workflow.
Capability 02
Multi-step agent and tool-use evaluation
Capability 03
RAG retrieval and answer evaluation
Capability 04
Benchmark and regression suites
Capability 05
Dataset and test-case management
Capability 06
Human review and rubric workflows
Capability 07
Experiment comparison and release gates
Capability 08
Quality, latency, and cost analytics
Capability 09
Production trace review and failure analysis
Use cases
Bounded roles. Real operational work.
Use Evaluation Platform where the journey is frequent, measurable, and connected to a clear owner.
01
Pre-release model, prompt, and workflow comparison
02
Agent task-completion and tool-use testing
03
Retrieval relevance and grounded-answer evaluation
04
Regression testing after content or model changes
05
Human quality review for high-impact use cases
06
Production failure analysis and dataset expansion
Operating model
From context to controlled action.
A clear deployment loop keeps business rules, people, evidence, and improvement connected from the beginning.
- 01
Define success
Translate business goals, risks, and failure modes into datasets, rubrics, and measurable checks.
- 02
Run evaluations
Test models, retrieval, agents, and complete workflows using automated and human assessment.
- 03
Compare and gate
Compare experiments across quality, latency, cost, and risk before approving a release.
- 04
Learn from production
Turn real traces and failures into new test cases so the evaluation suite evolves with the system.
Enterprise fit
Connect the systems the work already depends on.
Integration design is validated during discovery against available APIs, identity rules, permissions, write boundaries, and failure-handling requirements.
Orchestration layer
Evaluation Platform
LLM and embedding providers
Agent frameworks and trace stores
Vector databases and search platforms
CI/CD and release workflows
Observability and analytics systems
Datasets, warehouses, and review queues
API · events · approved data exchange · access controls
Related products
Extend the operating layer.
Pair Evaluation Platform with adjacent Brioworkx platforms when the workflow spans channels, knowledge, operations, or quality assurance.
Evaluation Platform FAQ
Questions before a working session.
Direct answers on product fit, controls, integrations, and deployment considerations.
What can the Evaluation Platform measure?
It can measure criteria such as retrieval relevance, groundedness, task completion, tool use, policy adherence, response quality, latency, and cost. The exact rubric is defined for each use case.
Does evaluation still require human reviewers?
Often yes. Automated checks provide scale and consistency, while subject-matter reviewers are important for nuanced, high-impact, or domain-specific judgments.
Can production failures become new evaluation tests?
Yes. A mature workflow captures reviewed production failures, removes sensitive information where required, and adds representative cases to regression suites.
Start a conversation
Put Evaluation Platform against a real workflow.
Bring the journey, systems, constraints, and baseline. We’ll map a bounded deployment and the evidence needed to decide what comes next.