AI Powered QE Domain
Executive Overview
AI Testing covers two things: validating AI-powered products (LLM applications, chatbots, ML models and the pipelines around them) and using AI to make testing itself more efficient. Qaston builds evaluation sets, checks output quality, safety and consistency, and integrates the results into your release process.
Traditional software tests expect a single deterministic output for a given input. Products built on large language models, retrieval-augmented generation (RAG) or predictive machine learning do not work that way: a model upgrade, a tweaked system prompt or a shift in source documents can quietly degrade answers without throwing a single HTTP error. Our AI testing services replace ad-hoc spot checks with repeatable evaluation sets, scoring criteria and regression gates.
At the same time, engineering teams want to use artificial intelligence to speed up testing of their core software. Qaston approaches AI software testing from both angles: we validate AI-powered applications using tools such as Promptfoo, Ragas, Deepchecks, LangChain and MLflow, and we apply AI-based test case generation and Playwright automation under strict human review so test suites stay maintainable.
Based in Madurai, India, Qaston works remotely with product and data teams in India and abroad. Whether you are launching a customer-facing assistant, validating a classification pipeline or looking for practical AI testing consulting, we help you define what good looks like and measure it before every release.
Key Strategic Benefits
Core Deliverables & Work Products
Tools & Technology Matrix
Our certified engineers leverage industry-leading toolsets, open-source automation libraries, and proprietary accelerators:
Testing generative AI, LLMs and machine learning applications
Generative AI testing services and RAG evaluation
For chatbots, copilots and document assistants built on LangChain or custom APIs, we use Ragas and Promptfoo to evaluate context recall, answer faithfulness, relevance and formatting constraints against curated golden datasets. When a prompt or underlying model version changes, prompt regression suites show exactly where output quality improved or regressed.
Safety, hallucination and prompt-injection checks
Generative interfaces face adversarial inputs that ordinary forms never see. We run structured adversarial prompts to test whether users can override system instructions, extract restricted context or trigger off-policy responses, and we verify that fallback guardrails behave predictably.
Machine learning model testing and data integrity
For predictive, classification and recommendation models, we use Deepchecks and MLflow tracking to inspect train-test leakage, feature drift, slice-level performance across segments and behavior on boundary or malformed inputs. This complements broader pipeline checks in our data quality and analytics testing.
Agentic AI testing for tool-using workflows
Modern AI features often call internal APIs, query databases or chain multiple reasoning steps. We test tool selection accuracy, loop termination, permission boundaries and error recovery, and teams building autonomous test workflows can also explore our dedicated agentic AI practice.
Using AI inside software testing and test automation
AI-based test case generation with engineer review
We use structured prompting workflows to draft functional, boundary and negative test scenarios from user stories, API specs and acceptance criteria. Every generated scenario is reviewed, deduplicated and refined by a QA engineer so the resulting suite reflects real business rules instead of generic boilerplate.
AI test automation and resilient UI checks
Brittle locators and slow script authoring are common bottlenecks in UI automation. We combine Playwright with intelligent test automation techniques to assist with selector resilience, page object scaffolding and failure triage, connecting directly with our AI-enabled testing and test automation services.
Prompt and model regression gates in CI/CD
Just as unit and API tests run on pull requests, evaluation suites in Promptfoo and Deepchecks can run automatically when prompts, embeddings or model configurations change, giving developers a clear comparison against baseline scores before deployment.
How an AI quality assurance engagement works
Risk mapping and evaluation criteria design
We identify what a bad output looks like in your domain: fabricated facts, leaked customer data, biased decisions, broken JSON schemas or slow inference responses. From there we define measurable scoring rubrics combining deterministic checks, semantic similarity and LLM-as-a-judge metrics.
Building golden datasets and adversarial packs
Working with your domain owners, we assemble representative test prompts, edge cases, multi-turn conversations and adversarial inputs. These datasets are versioned alongside your code so every future model or prompt experiment is measured on the same yardstick.
Automated evaluation runs and human spot-audits
Automated scorers in Ragas, Promptfoo and Deepchecks handle scale, while human reviewers inspect low-confidence outputs and subtle domain nuances to calibrate the automated judges and prevent metric gaming.
Release reporting and continuous monitoring handoff
You receive a clear quality report comparing candidate builds against baselines, along with reusable evaluation scripts tracked in MLflow or your repository so your team can keep running checks after launch.
AI testing consulting and choosing the right starting point
AI testing consulting for product and QA teams
If your team is shipping its first LLM feature and is unsure how to test non-deterministic outputs, we run focused consulting workshops to set up an initial evaluation harness, choose open-source tooling and train your engineers to maintain it.
Connecting technical testing with AI governance
Hands-on model testing works best when backed by clear organizational guardrails, risk tiers and compliance policies. When you need readiness audits, responsible AI charters or alignment with frameworks such as the NIST AI RMF or EU AI Act, see our AI advisory services.
Qaston 4-Phase Delivery Framework
Discovery & Architecture Audit
Evaluating application dependencies, defect history, test environments, and SLAs.
Framework Engineering & Design
Designing modular test suites, synthetic data pipelines, and CI/CD quality gates.
Continuous Automated Execution
Parallel execution across real devices, distributed cloud runners, and regression suites.
Analytics & Optimization
Real-time quality dashboards, defect root-cause analysis, and continuous feedback.
Bring structured evaluation to your AI products and QA workflows
Tell us about your LLM application, machine learning pipeline or test automation goals. We will outline a practical evaluation plan and toolset.