PRODUCT
VeriCore AI Evaluation Studio
A quality and evaluation layer for LLM, RAG, and agentic systems.
The problem it solves
Why this exists
GenAI systems pass demos and fail in production because nobody tests them like enterprise software.
How it works
What it actually is
A structured evaluation architecture: datasets, regression suites, scorecards, and monitoring wired into your delivery pipeline.
AI evaluation pipeline · RAGAS-based
An AI evaluation architecture.
How we test LLM, RAG, and agent systems like enterprise software — measured against a dataset, gated on release, and continuously improved.
Build the test set
Questions and ground truth are structured into a versioned evaluation dataset.
Run against the system
A test runner invokes the RAG or agent system and captures the context and answers returned.
Score with RAGAS
Embedding and LLM-judge layers grade context precision, context recall, answer relevancy, and faithfulness.
Aggregate & gate
Scores roll up into a scorecard, with pass/fail release gates and ongoing monitoring.
Close the loop
A feedback agent root-causes low scores and feeds tuning back into the pipeline — evaluation that improves the system.
Evaluation scorecard
Dimensions evaluated — illustrative structure, not live scores.
Example output
Reference architecture.
Test harness
Execution & capture
RAGAS evaluation engine
Scoring & reporting
Feedback & tuning loop
↺ Tuning feeds back into the pipeline — evaluation that improves the system, not just scores it.
Illustrative reference architecture · representative stack, adapted per engagement · no client data shown.
Modules
What is inside.
- LLM output validation
- RAG retrieval quality
- Prompt regression
- Agent task evaluation
- Safety & policy checks
- Drift monitoring
- Cost & latency tracking
What you receive
- Evaluation harness and datasets
- Regression baselines and scorecards
- Release quality gates
- Monitoring configuration
Review AI-QE Readiness.
We will walk through the architecture and how it maps onto your environment.