AI Evaluation and Reliability

AI you can measure and trust.

A demo shows what AI can do once. Evaluation shows what it does every time. Metacto tests AI systems against your real cases, agrees on acceptance criteria before launch, and keeps measuring quality as models, prompts, and data change.

45 minutes with our AI experts to find your highest-value opportunity and a practical next step. No preparation required.

Talk to an expert Explore Managed AI Services

For leaders who need evidence that an AI system works before they rely on it, and confidence that it keeps working after launch.

When evaluation becomes essential

Without a way to measure quality, every change to an AI system is a guess.

No definition of good enough

The system launches on impressions from a few demos, with no agreed standard for what counts as correct.

Changes break what worked

A new prompt, model version, or data source fixes one case and quietly breaks three others.

Quality drifts after launch

Inputs, data, and models change over time, and nobody notices until users stop trusting the output.

Cost and speed surprise you

Latency and usage costs grow without visibility, until they become a budget or experience problem.

What Metacto builds

Each evaluation element is paired with the business reason it exists.

Test sets from real cases: examples drawn from your own work, so results reflect actual conditions.
Acceptance criteria before launch: an agreed quality bar, so the decision to go live is based on evidence.
Regression testing: every prompt, model, or context change is tested against the full set before release.
Quality scoring: automated and human review methods matched to what correct means for each task.
Production monitoring: quality, latency, and cost tracked continuously, so problems surface early.
Drift detection: shifts in inputs or results are flagged before they affect the people relying on them.
Model upgrade testing: new model versions are evaluated side by side before any switch is made.
Incident response: a defined process to diagnose, roll back, and correct when quality drops.

How evaluation is built and run

Evaluation starts before the build, decides the launch, and continues for as long as the system is in use.

01Define

Define what good looks like

We work with your team to collect real cases, describe the correct result for each, and agree on acceptance criteria for launch.

A test set and quality bar your team agrees reflect the work.

02Prove

Test before every release

We run the system against the test set, review failures with your team, and repeat the full run whenever prompts, models, or context change.

Release decisions based on measured results rather than impressions.

03Operate

Monitor in production

We track quality, latency, and cost in use, watch for drift, and respond with a defined process when something degrades.

A system whose reliability is visible, reviewed, and maintained over time.

Example: what an evaluation report covers (illustrative)

A simplified outline of the report we review with your team before launch and after major changes. Shown for illustration, not taken from a client engagement.

Scope: the system, version, and change being evaluated
Test set: the number and type of real cases used, and how they were chosen
Acceptance criteria: the quality bar agreed before testing began
Results by category: where the system met, missed, or exceeded the bar
Failure review: representative failures, their likely causes, and proposed fixes
Regression check: cases that changed compared with the previous version
Latency and cost: performance and usage compared with expectations
Recommendation: launch, hold, or revise, with the reasons stated

Controls

Reliability you can inspect

Evaluation results, monitoring, and incidents are visible to your team, not hidden inside the system.

Release control

Nothing ships untested

Acceptance criteria agreed before launch
Full regression run on every change
Rollback path for every release

Monitoring

Quality tracked in production

Quality, latency, and cost measured continuously
Alerts when results drift from the baseline
Reports your team can review on a set schedule

Data handling

Test data handled with care

Test cases stored under your access rules
Sensitive fields masked where required
Evaluation runs inside agreed environments

We agree on test data handling, review responsibilities, and escalation contacts with your team before evaluation begins.

Common questions

Why not just test the system by trying it?

Trying it shows how it behaves on the few cases you happen to pick. A test set built from real work shows how it behaves across the range of cases it will actually see, and makes it possible to compare one version with the next.

Where do the test cases come from?

From your own work: past requests, documents, records, and decisions, selected with the people who know what a correct result looks like. We include common cases, edge cases, and the cases that matter most when they go wrong.

How do you measure quality for open-ended outputs?

We combine methods suited to the task: exact checks where an answer is either right or wrong, structured scoring against criteria for drafted work, and human review for judgment calls. The method is agreed with your team as part of the acceptance criteria.

What happens when a model provider releases a new version?

The new version is evaluated against the same test set before any switch. We compare quality, latency, and cost side by side and recommend whether to move, wait, or adjust.

What happens when quality drops in production?

Monitoring flags the change, and a defined incident process follows: diagnose the cause, roll back if needed, correct the issue, and add the failing cases to the test set so it does not recur. Systems can also run on Vocion, Metacto's open-source operating layer.

Which service includes this capability?

Managed AI Services is where most clients rely on this capability after launch. Custom AI Development and AI Implementation use it to set and confirm acceptance criteria before a system goes live.