Testing-AI systems that think, change, and drift

MoolyAImpact catch and prevent the risks others miss - hallucinations, bias, drift, and security gaps before your AI hits production

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Full-Spectrum AI Testing

Tailored to what you’re building

LLMs, chatbots, and ML models tested for

Hallucinations & factual accuracy

Prompt injection & guardrails

Bias, explainability & threshold tuning

CV, OCR, and language models tested for

Edge-case accuracy & noise sensitivity

Cultural nuance, slang handling, and intent clarity

Autonomous & Agentic AI tested for

Fail-safes & unpredictability

Adversarial prompts & injection threats

The Hidden risks inside AI systems

Reveal blind spots and restore control over your AI’s behavior

1. Hallucinations & False Responses

AI models can generate misleading, made-up answers that look convincing but are factually wrong this damages user trust fast

2. Bias & Fairness Gaps

AI decisions may favor certain demographics unfairly leading to compliance issues and reputational risks, especially in regulated sectors

3. Model Drift & Security Flaws

AI performance can silently degrade over time or become vulnerable to attacks like prompt injections and adversarial inputs without warning

4. Lack of Explainability

When models make complex decisions without transparency, teams can’t explain why something went wrong creating a black box no one wants to own

5. Regulatory Blind spots

Most AI systems aren’t designed with GDPR, HIPAA, or the EU AI Act in mind leaving organizations exposed to audits, penalties, and loss of trust

How our MoolyAImpact Process works

How we ensure AI quality at every stage

Step-1

Discovery & Alignment

Analyze your AI model, tech stack, and risk areas

Step-2

Test Strategy & Design

Build a custom test plan tailored to your business goals
Design test harnesses and stress-testing methods

Step-3

Execution & Feedback Loop

Run manual + automated tests on your AI systems
Provide detailed reports, logs, and action-ready fixes

Step-4

Continuous Testing & Model QA

Integrate testing with your CI/CD or MLOps pipelines
Retest as models evolve, retrain, or drift over time

How our AI Testing Process works

How our MoolyAImpact Process works

How we ensure AI quality at every stage

Step-1

Discovery & Alignment

Analyze your AI model, tech stack, and risk areas

Step-2

Test Strategy & Design

Build a custom test plan tailored to your business goals
Design test harnesses and stress-testing methods

Step-3

Execution & Feedback Loop

Run manual + automated tests on your AI systems
Provide detailed reports, logs, and action-ready fixes

Step-4

Continuous Testing &Model QA

Integrate testing with your CI/CD or MLOps pipelines
Retest as models evolve, retrain, or drift over time

Test Frameworks:

Selenium, Appium, Playwright, Cypress, Robot

Version Control:

Git

Parallel Execution, Zero Overlap

Run development and backlog resolution together. Avoid sprint delays and reduce engineering costs by $300K–$800K.

Automation-Led Fix Validation

Reduce manual QA effort by 30–50%. Save $150K–$300K annually on validation cycles.

Why leading teams trust MoolyAImpact?

Ensuring accuracy, fairness, and security in AI

Model Drift Monitoring

We track model behavior over time and adjust tests as your AI evolves catching silent failures before they cause damage

Human in-the-loop validation

When automation isn’t enough, our expert testers review critical AI outputs to ensure accuracy, fairness, and usability

Bias & Fairness Audits

We detect hidden biases in your models across race, gender, geography, and age using advanced explainability tools

Privacy & Compliance checks

We validate your AI against GDPR, HIPAA, SOC2, and EU AI Act standards protecting your users and your business

Prompt Injection & Security Testing

We test your models against malicious prompts, adversarial attacks, and tricky edge cases keeping your AI secure and resilient

Model Drift Monitoring

We track model behavior over time and adjust tests as your AI evolves catching silent failures before they cause damage

Human-in-the-Loop Validation

When automation isn’t enough, our expert testers review critical AI outputs to ensure accuracy, fairness, and usability

Bias & Fairness Audits

We detect hidden biases in your models across race, gender, geography, and age using advanced explainability tools

Privacy & Compliance checks

We validate your AI against GDPR, HIPAA, SOC2, and EU AI Act standards protecting your users and your business

Prompt Injection & Security Testing

Prompt Injection and Security Testing

We test your models against malicious prompts, adversarial attacks, and tricky edge cases keeping your AI secure and resilient

We test AI the way it actually behaves

AI does not give one fixed answer. We define what is acceptable using golden datasets, semantic scoring, thresholds, and human review

We catch AI failures most teams miss

Hallucinations, bias, grounding issues, broken JSON, prompt exploits, and drift. These are the bugs that quietly break AI in production

Automation where it scales. Humans where it matters

Tools help us test faster and wider. Humans handle nuance, context, and safety

We monitor drift continuously

Models change even when you do nothing. We track drift and performance decay before users feel it

We test the entire AI product

The model is only part of the system. We validate retrieval, agents, UI, integrations, latency, cost, and fallbacks

We test AI the way it actually behaves

AI does not give one fixed answer. We define what is acceptable using golden datasets, semantic scoring, thresholds, and human review

We catch AI failures most teams miss

Hallucinations, bias, grounding issues, broken JSON, prompt exploits, and drift. These are the bugs that quietly break AI in production

Automation where it scales. Humans where it matters

Tools help us test faster and wider. Humans handle nuance, context, and safety

We monitor drift continuously

Models change even when you do nothing. We track drift and performance decay before users feel it

We test the entire AI product

The model is only part of the system. We validate retrieval, agents, UI, integrations, latency, cost, and fallbacks

We test AI the way it actually behaves

AI does not give one fixed answer. We define what is acceptable using golden datasets, semantic scoring, thresholds, and human review

We catch AI failures most teams miss

Hallucinations, bias, grounding issues, broken JSON, prompt exploits, and drift. These are the bugs that quietly break AI in production

Automation where it scales. Humans where it matters

Tools help us test faster and wider. Humans handle nuance, context, and safety

We monitor drift continuously

Models change even when you do nothing. We track drift and performance decay before users feel it

We test the entire AI product

The model is only part of the system. We validate retrieval, agents, UI, integrations, latency, cost, and fallbacks

Transformative Journeys, Documented

Case Studies that show measurable results

E-Commerce

2 Min Read

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Tomi Schütz, CTO

Moolya helped in preventing bugs going into a major release

Rahul Chari, Co-Founder & CTO

Moolya is a partner to my entrepreneurial journey for over a decade

Huib Schoots, Program Director

Moolya brought in CDT approach to Testing and made a large banking transformation go smooth

Natarajan Alagappan (Nattu),
‍Head of Engineering & Technology

Moolya helped prevent bugs slipping into our users by bringing in a unique value

Frequently Asked Questions

Why can’t AI systems be tested the same way as traditional software?

AI systems don’t behave deterministically. The same prompt may produce different outputs, and “correctness” becomes a range, not a single expected value. That means testers need to validate outcomes using thresholds, semantic scoring, golden datasets, and contextual acceptability rather than simple assert-equals checks.

How do we define a “bug” in an AI system when answers vary?

AI bugs often fall into new categories: bias bugs, hallucinations, data bugs, security/fuzz exploits, or rule-violation bugs (e.g., breaking output constraints like word limits or JSON structure). These require new evaluation approaches, not the old pass/fail model.

How do we make sure the AI hasn’t drifted over time?

AI needs continuous testing, not one-time QA. Drift happens when data changes, real-world patterns change, or the model silently upgrades and starts behaving differently. Detecting drift requires ongoing monitoring of precision/recall, golden dataset checks, guardrails testing, and automated pipelines that catch changes before they hit production.

Can AI test itself?

Not yet, not safely. AI can help evaluate outputs at scale using semantic scoring tools (BLEU, ROUGE, DeepEval, RAGAS), but human judgment is essential for edge-cases, safety, and acceptability. Think of testers as “the adults in the room” who validate whether the AI’s decisions make sense.

What does “security testing for AI” look like?

AI systems can be tricked, overloaded, manipulated, or coerced into revealing information. Fuzz testing becomes essential throwing random, malformed, extreme, or adversarial inputs to uncover jailbreaks, prompt injections, and unexpected responses. AI testing now blends functional, security, and performance testing in one discipline.

If my AI product uses external LLMs (OpenAI, Claude, etc.), how do I test what I can’t fully control?

You focus on what you build: grounding data quality, prompt patterns, guardrails, constraints, API behavior, latency, error tolerance, and safety checks. You test the “20% AI layer” integrated inside your product while still testing the remaining “80%” using traditional methods. Continuous testing becomes mandatory because upstream models evolve without notice.

Frequently Asked Questions

How is AI testing different from traditional software testing?

AI testing isn’t just about functionality it’s about behavior.
We test how your model makes decisions, handles unexpected inputs, and adapts over time.
It’s about accuracy, fairness, security, and long-term reliability.

Do I need AI testing if my models already passed internal QA?

Yes traditional QA often misses hidden risks like:
● Hallucinations
● Bias against certain demographics
● Model drift or degradation
● Vulnerabilities to prompt injection
Our testing is designed specifically to uncover these AI-specific issues.

How long does an AI testing project take?

It depends on the complexity of your models and systems.
Most initial audits and testing cycles take 2 to 6 weeks.
For ongoing testing (like drift monitoring), we integrate directly with your MLOps pipelines.

What types of AI models do you test?

We test a wide range of AI systems, including:
● Generative AI (LLMs, text/image generators)
● Predictive models (recommenders, classifiers)
● Computer Vision & OCR
● NLP & Speech Systems
● Autonomous Agents & RPA

Can you integrate testing into our existing CI/CD or MLOps pipeline?

Absolutely.
We specialize in continuous testing setups and can plug directly into your workflows whether
you’re using standard CI/CD, custom MLOps, or cloud-based pipelines.

Is your testing compliant with regulations like GDPR or HIPAA?

Yes.
We incorporate compliance checks for GDPR, HIPAA, SOC2, and the EU AI Act as part of our
testing process where relevant.

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Testing-AI systems that think, change, and drift

Trusted by 200+ Startups and Enterprises

Empowering Global Innovators Across Industries

Full-Spectrum AI Testing

The Hidden risks inside AI systems

1. Hallucinations & False Responses

2. Bias & Fairness Gaps

3. Model Drift & Security Flaws

4. Lack of Explainability

5. Regulatory Blind spots

How our MoolyAImpact Process works

Discovery & Alignment

Test Strategy & Design

Execution & Feedback Loop

Continuous Testing & Model QA

How our AI Testing Process works

How our MoolyAImpact Process works

Discovery & Alignment

Test Strategy & Design

Execution & Feedback Loop

Continuous Testing &Model QA

Test Frameworks:

Version Control:

Parallel Execution, Zero Overlap

Automation-Led Fix Validation

Why a Well-Developed Platform Matters

Why leading teams trust MoolyAImpact?

Model Drift Monitoring

Human in-the-loop validation

Bias & Fairness Audits

Privacy & Compliance checks

Prompt Injection & Security Testing

Model Drift Monitoring

Model Drift Monitoring

Human-in-the-Loop Validation

Human-in-the-Loop Validation

Bias & Fairness Audits

Bias & Fairness Audits

Privacy & Compliance checks

Privacy & Compliance checks

Prompt Injection & Security Testing

Prompt Injection and Security Testing

We test AI the way it actually behaves

We catch AI failures most teams miss

Automation where it scales. Humans where it matters

We monitor drift continuously

We test the entire AI product

We test AI the way it actually behaves

We catch AI failures most teams miss

Automation where it scales. Humans where it matters

We monitor drift continuously

We test the entire AI product

We test AI the way it actually behaves

We catch AI failures most teams miss

Automation where it scales. Humans where it matters

We monitor drift continuously

We test the entire AI product

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Transformative Journeys, Documented

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Healthcare App

Myntra

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API Testing

Streaming

Energy Management

Fintech

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JioHotstar

Healthcare App

Myntra

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