Testing AI-Enabled Systems

Your chatbots hallucinate. Your AI outputs vary unpredictably. Your integration with OpenAI or Anthropic works in dev but fails in production. Forte's AI Testing practice brings structure to the chaos - so you can ship AI features with confidence.
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Your AI features aren't working like they should. We fix that.
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You've shipped AI features. Now you're dealing with outputs that vary unpredictably, chatbots that confidently give wrong answers, and API integrations that behave differently in production than in testing. Traditional QA doesn't catch these problems - and your team wasn't trained for this.
AI-enabled applications introduce quality challenges that traditional testing can't address: non-deterministic outputs, hallucinations, prompt sensitivity, and integration failures that only appear at scale. Most QA teams aren't equipped for this.
We've built a practice specifically for testing AI-enabled systems, combining specialized methodologies with deep experience across OpenAI, Anthropic, Google, AWS, and Azure integrations.

The problems we solve

Your AI outputs wrong or inconsistent answers

The same prompt returns different results. Your chatbot confidently states incorrect information. Users get inconsistent experiences. We validate output quality, consistency, and reliability so you know what to expect before users do.

Your chatbot or copilot embarrasses you

It worked in the demo. In production, it hallucinates, goes off-brand, or handles edge cases poorly. We test conversational AI systematically - across thousands of scenarios your team hasn't thought of.

Your AI integration works until it doesn't

OpenAI rate limits. Anthropic model updates. Timeout handling that seemed fine until load hit. We test your AI API integrations for the failure modes that don't show up in happy-path testing.

Your prompts are fragile


Small changes break your AI features. Model updates require prompt rewrites. We engineer and test prompts for robustness across model versions, input variations, and edge cases.

You don't know what you don't know

Your team is new to AI testing. You're not sure what's working, what's at risk, or where to start. Our AI Testing Readiness Assessment gives you a clear picture and a prioritized path forward.

What we deliver

AI output validation & consistency testing

Systematic testing for non-deterministic AI outputs. We validate that your AI features produce reliable, consistent results across inputs, sessions, and time - using LLM-as-a-judge evaluation, semantic similarity analysis, and human review.

Hallucination detection & fact validation

Automated and human-in-the-loop testing to catch when your AI generates false, misleading, or ungrounded information - before your users do.

Conversational AI & chatbot testing

End-to-end testing for chatbots, copilots, and conversational interfaces. We validate conversation quality, context retention, tone alignment, edge case handling, and graceful failure across thousands of scenarios.

AI API integration testing

Testing for integrations with OpenAI, Anthropic, Google, AWS Bedrock, and Azure OpenAI. We validate connectivity, error handling, timeout management, rate limiting, fallback behavior, and the failure modes traditional API testing misses.

Prompt engineering & regression testing

Design, validation, and regression testing for the prompts that drive your AI features. We ensure your prompts are robust to input variations, model updates, and edge cases.

AI testing readiness assessment

A 2-3 week diagnostic engagement that evaluates your current AI testing capabilities, identifies gaps and risks, and delivers a prioritized roadmap. The low-risk starting point for organizations new to AI testing.

Testinf AI-enabled quality case studies

40% faster test case creation for biopharma company

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99% Order Fill Rate with API Performance Optimization for Apex Fintech

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Optimizing Healthcare Mobility App Through Scalable Test Automation

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Why Forte Group for AI testing

We're not generalists adding "AI" to our menu

Most QA firms are retrofitting traditional testing approaches for AI. We've built a dedicated AI testing practice from the ground up — with methodologies, tooling, and expertise designed specifically for non-deterministic systems.

We've done this across your stack

OpenAI, Anthropic, Google Vertex, AWS Bedrock, Azure OpenAI—we've tested integrations across all major AI providers. We know where each one fails and how to catch it.

We know what your team doesn't (yet)

AI testing requires skills most QA teams weren't trained for: prompt engineering, LLM-as-a-judge evaluation, semantic similarity analysis, statistical validation of non-deterministic outputs. We bring that expertise so you don’t have to build it from scratch.

We start where you are

Whether you need a full testing engagement or just want to understand your gaps, we meet you at your current maturity level. Our Assessment gives you clarity without commitment.

FAQs

Can you test our chatbot before we launch?

Yes. We run systematic testing across conversation flows, edge cases, adversarial inputs, and failure scenarios—typically thousands of test cases—to identify issues before your users do. Most clients engage us 4-6 weeks before launch.

What's the difference between you and our existing QA team/vendor?

Traditional QA validates that code produces expected outputs. AI testing validates that non-deterministic systems produce acceptable outputs within defined bounds. It requires different skills (prompt engineering, statistical validation, LLM-as-a-judge) and different tooling. We specialize in this; most QA teams and vendors are still learning it.

Our AI feature is already live and causing problems. Can you help?

Absolutely. We often engage post-launch to diagnose and remediate AI quality issues. Our Assessment can quickly identify root causes and prioritize fixes.

How do you test when there's no "right answer"?

We use multiple validation approaches: semantic similarity scoring, LLM-based evaluation against rubrics, statistical consistency analysis, and human review for subjective quality. The goal is the appropriate level of confidence based on risk, not binary pass/fail.

Know you have a problem? Let's scope a solution.

What our experts say

AI systems don’t fail like traditional software: they drift, bias, and behave unpredictably as data changes. Testing AI-enabled systems requires validating not just code, but data quality, model behavior, and ethical constraints.

Organizations that test AI rigorously reduce risk while building trust with users and regulators alike. Continuous validation ensures models remain reliable even as real-world inputs evolve.
Lee Barnes
CQO at Forte Group

What our experts say

Accuracy alone doesn’t define AI quality. We test for fairness, explainability, robustness, and real-world decision impact, ensuring models behave responsibly under edge cases and changing conditions. Companies that invest in AI-specific quality engineering deploy models with confidence instead of discovering failures in production. Responsible AI testing protects both business reputation and customer relationships.
Pavel Chechat
VP Delivery at Forte Group

What our experts say

Off-the-shelf solutions force your business to adapt to software. Custom development does the opposite. We build systems that flex with your unique workflows, integrate seamlessly with your existing tools, and evolve as your market shifts. Organizations investing in tailored solutions gain 3-5 years of competitive runway because their technology becomes a strategic asset, not just a cost center.
Egor Goryachkin
CDO at Forte Group

What our experts say

The most successful digital transformations don't just digitize existing processes: they reimagine them entirely. Organizations need partners who understand both the technical architecture and the business outcomes. We've seen companies accelerate time-to-market by 60% when they move from legacy systems to modern, scalable custom solutions that grow with their business needs.
Lucas Hendrich
CTO at Forte Group