LLM Quality Assurance as a Service
Ensure Your GenAI Applications Are Production-Ready with Enterprise-Grade QA
Innodata delivers scalable, repeatable QA for large language models — combining automation, human expertise, and best practices to ensure your GenAI outputs are accurate, safe, and ready for production. Our proven evaluation framework benchmarks LLM performance for response relevance, accuracy, brand alignment, and safety—enabling continuous improvement, faster launches, and higher model confidence.
Why LLM QA Matters
Today’s GenAI products drive customer interactions, product discovery, internal tools, and user-generated content at scale. But without a systematic QA process, LLMs are vulnerable to:
LLMs hallucinate, drift, and misclassify — QA identifies silent failures before they impact users.
Tone, refusal, and safety must align with brand — QA ensures output is appropriate, trustworthy, and policy compliant.
Factual errors damage trust — QA validates responses against gold or known-correct answers.
Toxicity and unsafe content must be prevented — QA enforces safety, brand, and community standards.
Inappropriate refusals degrade experience — QA flags unnecessary model refusals that block valid user needs.
Tone misalignment impacts user trust — QA ensures the output matches brand voice and user expectations.
Query logs are goldmines — QA analyzes them to surface unreported failures, broken or unanswered prompts, and behavioral anomalies, enabling proactive prompt tuning and model fixes.
Continuous updates introduce continuous risk — Frequent prompt, model, or API changes demand regression testing.
Multi-step logic introduces complexity — Agentic systems and copilots require QA for reasoning chains, memory, and tool use.
RAG systems must be grounded — QA verifies that generated responses truly reflect the source documents.
Innodata’s specialized QA approach addresses these challenges through structured testing frameworks. An automated Model Test and Evaluation Platform, scalable LLM-as-a-Judge techniques, human-in-the-loop validation, and continuous improvement loops aligned with real-world production behavior.
How We Measure LLM Quality
Innodata applies a structured QA rubric to evaluate every model output across 12 enterprise-grade dimensions:
Relevance – Does the response directly address the prompt or question?
Contextualization – Is the response aligned to the appropriate context, such as user role, intent, or product nuance?
Accuracy – Is the information factually correct and free from hallucinations?
Formatting – Is the output well-structured, readable, and optimized for UX?
Completeness – Does the output include all necessary elements to fully answer or fulfill the request?
Flexibility – Can the model adapt to variations in phrasing, ambiguity, or changing inputs?
Parameterization – Does the model maintain consistency across controlled variables (e.g., tone, persona, instruction following, ambiguity and complexity of prompt)?
Performance – Is the output delivered within expected latency or within defined UX performance thresholds?
Toxicity & Safety – Is the output free from offensive, unsafe, or policy-violating content?
Response Evasiveness – Does the model avoid unnecessary refusals?
Brand Alignment – Does the response reflect the intended brand tone, friendliness, and personality?
Groundedness (if RAG) – Is the output faithfully derived from retrieved or referenced content? (Only for RAG-based systems)
Built-In QA Metrics
Our teams track and report key performance indicators such as:
100%
Coverage for New Features, Flows & Prompts
≥85%
Regression QA Coverage across top tasks, intents, or queries
≥80%
Rubric Pass Rate across all 12 dimensions
0
Guardrail Breach Tolerance
(toxicity, bias, compliance issues)
≥95%
Multi-turn Stability Rate (for agents and copilots)
≥90%
Tool Use Accuracy (for API-integrated or agentic LLMs)
≥90%
RAG Faithfulness Score (citation matches and retrieval grounding)
All Metrics Customizable to Your Success Criteria
Want to see how we define “quality”?
Innodata’s Proven Approach for Scaling LLM QA Testing
We provide full-spectrum QA services, purpose-built to support the evolving lifecycle of GenAI applications:
-
Pre-Testing Setup
- Requirements Traceability Matrix (RTM)
- Golden and synthetic dataset creation
- Testbed configuration: prompt templates, models, environment access and configuration
-
New Feature Testing
- Prompt evaluation
- Functional and non-functional validation
- Fall-back logic and success criteria coverage
-
Regression & Log-Based Testing
- Baseline comparison
- Refreshing test cases from user logs
- Hallucination and toxicity detection
-
Human-in-the-Loop + Automation
- LLM-as-a-Judge automation for scalable scoring
- Expert evaluation and error categorization
- Dual evaluation methods: freeform response grading + MCQ-style knowledge checks
- Gold answer benchmarks and rubric-based scoring across 12 dimensions: relevance, factuality, brand alignment, refusal handling, groundedness, etc.
- Human-in-the-loop reviews and audit-of-audits to ensure rubric alignment
Why Innodata
Proven LLM QA Expertise
- 87M+ LLM labels delivered YTD across 15+ GenAI workflows
- Specialists in hallucination detection, groundedness scoring, brand alignment compliance, and refusal analysis
- Repeatable QA metrics to inform model tuning and roadmap alignment.
- Structured QA frameworks for copilots, agents, RAG, and embedded LLMs—adapted for task and product context
- Data-driven insights that inform model tuning, prompt optimization, and roadmap alignment
Hybrid QA Model
- 87M+ LLM labels delivered YTD across 15+ GenAI workflows
- Specialists in hallucination detection, groundedness scoring, brand alignment compliance, and refusal analysis
- Repeatable QA metrics to inform model tuning and roadmap alignment.
- Structured QA frameworks for copilots, agents, RAG, and embedded LLMs—adapted for task and product context
- Data-driven insights that inform model tuning, prompt optimization, and roadmap alignment
Global, Enterprise-Ready Delivery
- 87M+ LLM labels delivered YTD across 15+ GenAI workflows
- Specialists in hallucination detection, groundedness scoring, brand alignment compliance, and refusal analysis
- Repeatable QA metrics to inform model tuning and roadmap alignment.
- Structured QA frameworks for copilots, agents, RAG, and embedded LLMs—adapted for task and product context
- Data-driven insights that inform model tuning, prompt optimization, and roadmap alignment
Ready to Launch Confidently?
Whether you’re tuning a sales assistant, scaling an AI companion, or deploying a GenAI support tool—Innodata ensures your LLMs are ready for production.
Schedule a Free QA Consultation
Let’s talk about your model goals and testing needs.
CASE STUDIES
Success Stories
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