AI Reliability Engineering

Test. Verify. Improve AI.

AI Evaluation & Quality Engineering for Agents and AI Systems

AI can produce the right answer and still do the wrong thing. UpCheckAI evaluates complete AI workflows, from intent and reasoning to tool use, actions, safety, and outcomes, combining automated evaluation with human judgment to find failures and turn them into permanent regression tests.

Human + Automated EvaluationMultilingual by DesignEnterprise-Grade Security
The Problem

AI Systems Are Shipping Faster Than They Are Being Tested

Most teams find out an agent fails at a task when a customer hits it, not before.

AI systems ship faster than they're tested

Live on the strength of a demo, not a test suite.

A passing demo isn't a working system

Sounds correct while using the wrong tool or breaking policy.

Failures reach users before they reach you

The first sign of a regression is a user complaint.

AI Reliability Test Lab

See AI Reliability Testing in Action

Watch a run, get evaluated, and become a regression test.

Interactive Demonstration: Simulated Evaluation
1. Select an AI system type
2. Select an evaluation suite
Language Reliability

AI Reliability Across English & Swahili

The same system, checked in every language its users speak.

Interactive Demonstration: Simulated Evaluation

Same task, English

I want to return my order. Am I eligible for a refund?

  • Intent understood
  • Policy understanding
  • Tool selection
  • Task completed

Agent verified eligibility, then issued the refund correctly.

Multilingual evaluation isn't just checking whether a model can translate. It checks whether the AI can perform the same job reliably in each language it's used in.

What We Evaluate

Built Around How AI Agents Actually Fail

Pick a scenario to see the checks we run.

Refund EligibilityCancellationsEscalation BehaviorAmbiguous RequestsPolicy Adherence
How It Works

One Evaluation Engine, However You Reach the Agent

Nine stages, from defining success to catching regressions.

1

Define

Turn policies into measurable criteria.

Success CriteriaPolicy Rules
2

Generate

Build test cases from real and adversarial scenarios.

Edge CasesAdversarial Cases
3

Simulate

Run the system through real workflows.

Trace CollectionTool Execution
4

Evaluate

Score with checks, judges, and metrics.

Automated ChecksModel Judges
5

Verify

Experts review ambiguous or high-stakes cases.

Expert ReviewCultural Context
6

Diagnose

Cluster failures by root cause.

Failure ClusteringSeverity Tagging
7

Improve

Turn failures into fixes.

Remediation PlansPolicy Updates
8

Regress

Save every failure as a permanent test.

Regression SuiteFailure Memory
9

Monitor

Re-run the suite as the system changes.

Continuous RunsDrift Detection

↻ Loops back to Define

Agent v1

94% reliability

change →

Agent v2

78% reliability

Regression caught automatically

Evidence-First Evaluation

Know Why a Result Passed or Failed

Every verdict points to evidence, labeled by how sure we are.

Observed

Directly seen: a tool call, a response, a page state.

Inferred

A judge's reasoned conclusion from available evidence.

Not Available

Can't be seen. We say so, instead of guessing.

“An empty tool-call list does not automatically mean the agent made no tool call. If the integration cannot see tool calls, UpCheckAI says so.”

AI Reliability Across Languages and Contexts

AI Quality Can't Be Global If Evaluation Isn't

Works in English but fails in Swahili? Not reliable globally.

Native English & Swahili Evaluators

Judge understanding, not just translation.

Cultural & Contextual Review

Checks local norms and terminology, not just grammar.

Regional Safety Judgment

Catches what a generic safety filter misses.

Deep Domain Familiarity

Knows the real workflows of the markets served.

Evaluating AI systems worldwide, from East Africa

United StatesCanadaUnited KingdomEuropeUAESaudi ArabiaQatarIndiaAustraliaEast Africa

2

Languages Evaluated

5

East African Countries

24/7

Multilingual Coverage

Resources

Frequently Asked Questions

Who is UpCheckAI for?

Teams shipping an AI agent or AI-powered app who need evidence before release, not just a demo that worked once.

How is this different from a model benchmark?

Benchmarks score a model in isolation. We evaluate the full system: prompt, retrieval, tools, and workflow.

How do we get started?

Share your system, we build a test suite around it, run evaluation, and hand you an evidence-backed report and regression suite. We connect through whatever access you can give us — API, browser, or your own SDK.

Do we need a finished product to start?

No. We evaluate systems at any stage, from prototype to production.

How is our system data handled?

Access and data are scoped to the engagement under a clear agreement. We retain no more than necessary.

Ready to Find Out?

See Your Agent's Reliability Score.

Get an evidence-backed evaluation of how your AI actually performs.

Evidence-backed reports