Generative AI and LLM evaluation
Evaluate factuality, groundedness, completeness, instruction following, consistency, refusal behaviour, retrieval quality and useful uncertainty across prompts, models and datasets.
AI QUALITY ENGINEERING · SHYENA
Test the AI.
Prove the outcome.
Shyena helps teams assure AI across its lifecycle—from data and feature pipelines to predictive models, generative AI applications, RAG, document processing, computer vision and multi-step agent workflows. Validate accuracy, robustness, business rules, integrations, security and real-world outcomes with evidence, not assumptions.
Map the full workflow
Trace user input through retrieval, model calls, classification, tools, APIs, data stores, human review and final business outcome.
Measure against evidence
Combine exact assertions and reference datasets with calibrated semantic evaluation. Report failures by criterion, not only as one average score.
Gate risky releases
Apply explicit thresholds for critical accuracy, authorization, execution integrity and privacy. Block releases when mandatory controls fail.
PRACTICAL DELIVERY MODEL
From experiment to production evidence.
Start with one high-impact workflow and define what a correct outcome means with the business owner or subject-matter expert. Build a versioned reference dataset, identify critical and adversarial cases, run the system, compare results to the expected contract, and turn every meaningful failure into a regression test.
BUSINESS GOAL → DATASET → SCENARIOS → EXECUTION TRACE → ASSERTIONS + EVALUATION → THRESHOLDS → RELEASE DECISION
Integrate the checks into CI/CD where possible, and schedule repeat evaluations to detect regressions or drift. Keep the evidence needed to explain what changed, which cases failed, how severe the failures are and whether the release policy permits deployment.
WHAT WE VALIDATE
One quality model. Different AI workloads.
Choose the tests that fit the system. A predictive model, a generative application, a vision pipeline and an autonomous workflow need different metrics, but each needs representative data, repeatable evaluations, clear thresholds and evidence that supports a release decision.
Document AI and OCR
Measure field-level extraction accuracy, classification precision and recall, confidence handling, malformed documents, scans, tables and low-quality inputs.
Predictive and traditional ML
Validate labelled datasets, feature pipelines, model metrics, class imbalance, threshold behaviour, robustness, fairness considerations and model-version regressions.
Computer vision and multimodal AI
Test image and video classification, detection, segmentation, multimodal inputs, edge cases and performance across representative conditions.
Data and feature pipelines
Check schema contracts, data quality, lineage, transformations, leakage risks, distribution changes and the impact of data refreshes on model outcomes.
Agent and workflow execution
Assert the intended tools and APIs were called with valid arguments, the right identity and permissions were used, and authoritative business state changed correctly.
Evaluation engineering
Build reusable Python evaluators, deterministic assertions, semantic rubrics and calibrated LLM-as-judge checks. Keep hard business rules out of subjective scoring.
Regression and drift
Run fixed baseline datasets on every material change and on a schedule. Compare results by model, prompt, retrieval configuration, dataset and release.
Governance and evidence
Keep dataset versions, model and prompt versions, run IDs, traces, criteria, scores, thresholds, findings and release decisions linked for audit and investigation.
EXAMPLE: DOCUMENT INTAKE
Test the extracted facts, not the confidence of the explanation.
For a tax or immigration document workflow, test document type classification, required-field extraction, source grounding, missing or conflicting evidence, low-quality scans, unsupported claims, routing and escalation. Compare extracted values with expert-labelled ground truth and calculate field-level error rates.
Illustrative release checks
- • Required fields meet agreed accuracy thresholds.
- • Missing or ambiguous evidence triggers clarification or human review.
- • Every extracted value is traceable to its source where required.
- • Downstream records match validated values; no silent data corruption.
- • Regression results are compared with a versioned baseline.
- • Critical privacy, authorization and execution failures block release.
Thresholds must be agreed with process owners and measured on representative, labelled data. No performance figures are assumed here.
Make AI quality measurable.
Define a critical workflow, build a representative baseline and connect test evidence to deployment decisions.