Enterprise AI Evaluation with Traceable Results
Run deterministic evaluations, compare models on quality and cost, and catch regressions before they ship. Built for teams that need evidence over vibes.
Evaluation Suites
Unlimited
Define as many suites as you need
Graders
Built-in + Custom
Rule-based, LLM, and semantic graders
Regression Gates
CI/CD Ready
Thresholds with absolute and relative limits
Model Providers
OpenAI, Anthropic, Google
Adapter pattern for any provider
What IAnversic EVAL Solves
AI output is probabilistic, so classic tests cannot tell you whether your application is actually getting better. IAnversic EVAL turns that question into a reproducible experiment — with sample-level evidence and a gate that stops regressions from shipping.
Why AI Is Hard to Test
The problemA classic test asserts input → expected output. An AI application returns probabilistic output — valid but wrong, relevant but incomplete, safe but useless. Unit tests cannot cover that, and ad-hoc prompt checks are not comparable, so the question "is it getting better?" has no answer.
Classic test input → expected output pass / fail AI application input → probabilistic output no ground truth → No repeatable answer to: is it actually getting better?
Core Principles
Principles that distinguish IAnversic EVAL. Every design decision traces back to these.
Immutable Artifacts
Datasets, prompts, and runs are content-addressed and never mutate. Reproducibility is guaranteed.
Deterministic Execution
Same config + same input = same output. Workers use fixed seeds, structured outputs, and durable queues.
Evidence Over Vibes
Every claim backed by graded samples. No aggregate metric without sample-level evidence.
CI/CD Native
Regression gates with configurable thresholds. Exit codes for pipeline integration. No manual review needed.
Lifecycle & Architecture
Phases from dataset creation to continuous regression testing. Modular monolith with durable Postgres runtime.
Pipeline
1. Dataset Creation
Upload JSONL/CSV/JSON. Content-hash versioning. Immutable once created.
2. Prompt Versioning
System prompt + user template with declared variables. Every edit creates new version.
3. Grader Configuration
Rule-based, LLM, and semantic graders. Thresholds, pass criteria, and cost limits defined upfront.
4. Evaluation Run
CLI or API triggers run. Workers process samples with retries, checkpoints, and structured outputs.
5. Grading & Aggregation
Per-sample grading with evidence. Statistical aggregates: mean, p50, p95, pass rate, cost, latency.
6. Run Comparison
Compare any two runs on all metrics. Deltas with absolute/relative thresholds. Verdict: OK, IMPROVED, REGRESSION.
7. Regression Gate
CI/CD integration. Configurable thresholds per metric. Exit code 2 on regression. Zero manual steps.
8. Continuous Monitoring
Trends over time. Model usage dashboards. Cost/quality tradeoff analysis. Alerting on drift.
Architecture Designed for Reliable AI Evaluation
An AI evaluation platform designed to help teams measure, compare, and improve the quality of AI applications through structured evaluations, reproducible experiments, and actionable insights.
Measure What Matters
Evaluate AI outputs using configurable metrics, deterministic checks, and LLM-as-a-Judge to assess quality against defined criteria.
Compare Models and Prompts
Run controlled experiments across models, prompts, and configurations to understand performance differences and identify the best fit for each use case.
Detect Regressions Early
Track evaluation results across runs and versions to identify quality regressions before they affect users.
Reproducible and Traceable Evaluations
Organize datasets, evaluation runs, and results to support consistent testing, transparent comparisons, and evidence-based decisions.