AI Application Testing & LLM Evaluation Services
Deploy AI applications with confidence. We provide rigorous quality assurance for GenAI products, LLM pipelines, autonomous agents, and RAG systems — eliminating hallucinations, defending against prompt injections, and evaluating response accuracy.
QA & Software Testing Services Suite
Explore our dedicated testing services designed to help software teams, startups, and product leaders ship with zero release anxiety.
Why Traditional QA Breaks Down on AI Applications
Large Language Models are non-deterministic, probabilistic systems. Traditional test assertions fail because outputs vary across runs. GreeLogix uses specialized evaluation harnesses that measure semantic quality, factual grounding, and adversarial safety.
Hallucination Prevention
Catch false assertions, fabricated citations, and ungrounded statements before your users act on bad data. We score answers against verified knowledge bases.
Adversarial Red-Teaming
We attack your AI interface with sophisticated prompt injection, context escape vectors, and social engineering payloads to expose security gaps in system prompts.
Latency & Cost Control
We benchmark Time-to-First-Token (TTFT), token consumption runaway, stream dropouts, and fallback model routing under high concurrent user load.
Our Comprehensive AI Testing Capabilities
From simple chat assistants to complex autonomous agents and enterprise RAG architectures, we test every layer of the modern AI stack.
LLM Response Evaluation & Evals
Systematic evaluation of natural language outputs using automated semantic benchmarks, factual consistency scores, and golden dataset validation.
- Semantic similarity & tone adherence
- Hallucination detection & ground-truth alignment
- Answer relevance & completeness scoring
- Automated regression evals on model upgrades
Prompt Injection & Safety Red-Teaming
Adversarial security testing to ensure your application guardrails resist malicious exploitation, system prompt leaks, and toxic content generation.
- Direct & indirect prompt injection probes
- System prompt leak & override vulnerabilities
- PII and sensitive data extraction attempts
- Content moderation & safety policy verification
RAG Pipeline Retrieval Audits
Deep validation of Retrieval-Augmented Generation pipelines to guarantee retrieved context is accurate, uncorrupted, and properly synthesized.
- Retrieval Precision@k and Recall@k benchmarks
- Document chunking & embedding boundary tests
- Context truncation & token overflow prevention
- Source citation attribution verification
Autonomous Agent & Tool Calling
Rigorous end-to-end verification of AI agents executing multi-turn actions, external API integrations, and database operations.
- Tool-calling JSON schema & parameter validation
- Multi-turn conversation state persistence
- Graceful degradation on external API failure
- Infinite loop & recursive action prevention
Shepherd AI: Multi-Modal AI Study Companion & Quiz Generation
Our QA team verified complex multi-modal prompt pipelines, automated document parsing, hallucination bounds, and token streaming consistency across thousands of diverse academic study sources.
Delta Book: Intelligent E-Learning & Automated Content Generation
We implemented rigorous quality gates for AI-generated educational curriculums, testing output accuracy, schema formatting, and multilingual translation reliability.
Enterprise-Grade QA Rigor. Startup-Speed Delivery.
We combine deep engineering experience with structured quality assurance so your team can ship ambitious updates without breaking production.
Proven Track Record
Engineering and quality assurance experience since 2019, with over 150+ products shipped shipped across web, SaaS, and mobile platforms.
Embedded Team Workflow
We plug directly into your Jira, Linear, GitHub, and Slack channels. We test PRs in staging before merge and attend your daily standups.
Global Remote Delivery
Trusted by engineering leaders and startups across the US, UK, UAE, Australia, and Canada, with guaranteed daily timezone overlap.
Manual + Automation
Humans discover edge cases and usability flaws scripts miss; automated suites run on every commit to guard critical revenue flows forever.
Tested & Automated With Industry-Standard Tooling
Frequently Asked Questions About AI Application Testing
Practical answers on how we test, evaluate, and benchmark LLMs and AI agent workflows.
How do you test applications when LLM responses are non-deterministic?
Traditional assertions like 'assert response == expected' don't work for generative AI. We employ modern LLM evaluation methodologies: we benchmark outputs using semantic similarity metrics, embedding distance, factual consistency scorecards, and model-graded evals (LLM-as-a-judge) across large, version-controlled ground-truth golden datasets.
How do you test for hallucinations and factuality in RAG pipelines?
We evaluate the entire Retrieval-Augmented Generation (RAG) lifecycle: retrieval precision (whether relevant document chunks were extracted), context relevance (filtering noisy chunks), faithfulness (whether the model's answer is strictly grounded in retrieved context without fabricating facts), and citation accuracy.
What is your approach to prompt injection and adversarial safety testing?
We conduct red-teaming and adversarial security testing against your LLM interfaces. This includes direct prompt injections, indirect injections hidden within scraped web or document inputs, jailbreak prompts designed to bypass guardrails, system prompt extraction attempts, and PII leakage probes.
Can you test autonomous AI agents and complex tool/function calling workflows?
Yes. We test multi-step AI agents by validating tool-calling JSON schemas, verifying that arguments passed to external APIs match requirements, testing state preservation across multi-turn interactions, and checking error-recovery behaviors when third-party tools fail or return timeouts.
Do you test AI voice agents and real-time streaming interfaces?
Yes. We test real-time streaming token delivery, Time-to-First-Token (TTFT) latency, speech-to-text (STT) accuracy under audio background noise, voice latency, interruption handling (barge-in), and websocket stability during network degradation.
What tools and frameworks do your AI QA engineers use?
We leverage industry-standard evaluation and testing frameworks including Ragas, DeepEval, Promptfoo, Playwright for end-to-end UI automation, LangSmith/Langfuse for trace inspection, and custom Python test harnesses integrated into your CI/CD pipelines.
Request an AI QA & Evaluation Assessment
Tell us about your AI application, LLM architecture, and evaluation needs. Our AI QA engineers will review your architecture and provide an evaluation strategy within 4 business hours.
Explore Our Full QA Suite
Every type of testing your product needs — from manual audits to AI-app hardening and accessibility compliance.