DigiEdze AI · AI Evaluation
Measure How Well Your AI Works
Evaluation is what turns an impressive demo into a system you can change safely. We build the golden datasets, scorers and regression gates that let you ship AI changes with the same confidence as code.
- accuracy
- groundedness
- hallucination
- safety
- task success
- agent success
- latency
- cost
Reference architecture
- 06Golden dataset & taxonomy
- 05Offline evaluation harness
- 04Regression gates in CI/CD
- 03Online sampling from production
- 02Human adjudication queue
- 01Quality dashboards & drift alerts
The problem
What changes when this goes wrong?
Quality is judged by whoever last tried it, so nobody can approve a change with confidence.
A prompt edit that fixes one case silently breaks four others.
Regulators and risk committees ask for evidence, and screenshots are the only artefact available.
What we build
Our AI Evaluation Approach
Golden datasets
Representative cases drawn from real traffic, including the edge cases and the failures, with expected outcomes agreed by the business.
Automated scorers
Groundedness, correctness, safety, format compliance and task success — deterministic where possible, model-graded where necessary and calibrated against human judgement.
Agent trajectory evaluation
Scoring the path by understanding the right tools it has chosen, in the right order, within budget.
Regression gates in CI
Every prompt, model, index or tool change runs the suite before it can be promoted.
Online evaluation
Sampling live traffic for continuous scoring, so drift is detected in production rather than in a quarterly review.
Human review workflow
A queue where experts adjudicate disagreements, and their verdicts flow back into the dataset.
How it works
Tailoring the right evaluation dimensions based on your business decision. We bring the following tech stack to drive enterprise-wide AI evaluation.
Technology
- Evaluation frameworks
- LLM-as-judge with calibration
- Trajectory scoring
- CI/CD integration
- Annotation tooling
Use Cases
Proven AI Evaluation Across Verticals
BFSI
Evidence packs that show a risk committee exactly how a model performs on defined case classes.
Healthcare
Safety and refusal behaviour measured continuously, not asserted once at launch.
Technology
Prompt and model changes shipped weekly because the regression suite makes the blast radius knowable.
Outcomes
What We Measure
AI systems are tested against real-world applications continuously, and aligns with your business strategic objectives.
- Evaluation coverage of live traffic
- Regression escape rate
- Time to approve a change
- Measured drift over time
Questions
AI Evaluation, Answered Directly
Can a model grade another model reliably?
Model-graded evaluation is useful when calibrated: agreement with human judgement is measured on a labelled subset, and the judge is re-calibrated whenever it changes. Uncalibrated LLM-as-judge scores drift silently and should not gate releases.
How large should a golden dataset be?
Large enough to cover your case taxonomy with several examples per class, including known failure modes — typically a few hundred cases to start. Coverage of the taxonomy matters more than raw count.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Get an AI Evaluation Framework
Evaluate every data asset and enterprise sovereignty with us where intelligence that transforms your enterprise meets innovation that thrives for tomorrow.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT