AI Evaluation: How We Ensure AI Quality in Nectain

What is AI evaluation—and why does it matter?

In a broad sense, AI evaluation is the measurement of how well AI performs within a DMS architecture—and whether it does so reliably and securely in real-world conditions.

It answers questions such as:

• How well does the AI actually work?
• When does it fail?
• Can we trust it?
• Is it good enough to automate decision-making?

AI evaluation typically covers five core dimensions.

1. Performance and accuracy

Does the AI produce correct results?

We measure error rates, confidence levels, and accuracy by comparing outputs with ground-truth data or human results.

For example, in Nectain, this may include evaluating:

• Does the model classify documents correctly?
• Does the chatbot provide factually accurate answers?

2. Reasoning quality and understanding

Does the AI truly understand the input—or is it just matching patterns?

We assess context awareness, semantic understanding, logical consistency, hallucination frequency, and the ability to follow instructions

Examples:

• Can the AI summarize a contract without missing key obligations?
• Does it understand intent, not just keywords?

3. Reliability and robustness

Does the AI behave consistently under different conditions?

We test performance on edge cases, how it handles noisy, incomplete, or ambiguous inputs, stability across versions, and updates

E.g., we test documents with different scan quality and check whether the AI produces consistent results.

4. Risk, security, and ethics

Can the AI cause harm if it makes a mistake?

We evaluate bias, data leakage risks, overconfidence, explainability, compliance with laws, and internal policies

A practical Nectain example: can AI-driven invoice payment decisions be explained during an audit?

5. Business and human impact

In our view, this is one of the most critical aspects: Does the AI actually create value?

Implementing a new DMS—especially an innovative one—requires a significant investment of valuable human hours.

Therefore, a key responsibility of DMS vendors is to evaluate time savings, cost reduction, error reduction, user trust, and the effectiveness of human-in-the-loop workflows.

Examples of AI automation evaluation in Nectain include:

• Measuring the reduction in the number of documents reviewed manually
• Surveying how often users trust AI recommendations
• Comparing overall team productivity before and after AI automation is introduced
• AI Evaluation in Nectain: trust your AI before you automate

How AI Evaluation works in Nectain

In real work, the key question is simple: can I trust the result?

That’s exactly why we added "AI Evaluation" to Nectain.

"AI Evaluation" lets you test your AI scenarios on real documents and see how well they actually work — before you rely on them in daily processes. Instead of guessing, you can measure.

Let’s say you have an AI recognition scenario (“scenario” = a predefined AI setup that knows what to read and what result to return).

1. Preparing a test set

• Upload documents (or add links)
• Define what result you expect from AI (for example: document type, extracted fields, classification)

2. Running AI Evaluation

Nectain will:

• Run the same AI scenario on each document
• Compare the AI result with your expected result
• Calculate a score
• Show token usage (cost transparency)

3. Results monitoring

For each document, you can:

• Open AI execution — what the AI actually did
• Open AI evaluation — how the result was checked
• See overall quality, not just one example

No black box. Everything is visible.

How results are evaluated

In Nectain, you choose how strict the evaluation should be:

1. Exact match → AI result must fully match the expected result
2. Custom logic → useful when partial matches are acceptable
3. AI judging AI (yeah, we can do this!) → a separate AI scenario evaluates the quality of the result

You control the rules — not the model.

Confidence before automation - you know how well AI works before connecting it to workflows.

Comparing models and providers - you can run the same documents through OpenAI, Azure, etc., and see what works best for your case.

Improving results step by step - you can adjust the scenario (prompt, logic), run the evaluation again, and compare with previous results. This turns AI from experimentation into a controlled process.

Full history and transparency - you can see all evaluations saved in a journal - scores, statistics, trends over time. It is perfect for audits, improvements, and long-term trust.

One-click evaluation

In Nectain, we like "one-click" and "one-button" features. We added the possibility to start AI Evaluation directly from a scenario - click “Evaluate”, select a document set, and run.

Trust your AI before you automate

AI only creates value when it works reliably in real workflows — not just in demos.
That’s why Nectain includes built-in AI evaluation tools that help teams test, compare, and improve AI execution before trusting it with real documents and decisions.

With AI Evaluation in Nectain, you can:

• Run the same AI scenario on multiple documents and compare results
• Test AI behavior across different document types and scan quality
• Measure consistency, accuracy, and failure cases over repeated runs
• Understand where AI performs well — and where it needs adjustment

Instead of guessing whether AI is “good enough,” you get clear, repeatable evidence of how it behaves in production-like conditions.

Want to see how AI evaluation works on your own documents? Schedule a personal demo, and we’ll walk you through real evaluation scenarios inside Nectain — using your use cases, not generic examples.