You already have automation
Functional, API and regression tests exist, but they cannot tell you whether an AI response is correct, grounded or safe.
PARIMI · AI QUALITY ASSESSMENT
Can you prove your AI system is ready for production?
A focused 90-minute working session to identify where your AI system can fail, determine what should be tested and evaluated, and define the evidence your team needs for release decisions.
90 minutes1:1RemoteEngineering / QA / Product
THE OUTCOME
Not a generic AI checklist. A prioritised quality plan.
AI Quality Risk Map
Test & Evaluation Strategy
Priority Test Journeys
Automation & Regression Opportunities
IS THIS WHERE YOU ARE?
Your existing QA is strong. AI has expanded the quality surface.
AI has introduced new uncertainty
Models, prompts, retrieval, tools, agents and conversations create failure modes that conventional assertions do not fully capture.
Production is getting closer
You need to decide what to test, what to evaluate, what to automate and what evidence is enough before release.
THE SESSION
90 minutes, in 3 movements.
01
Assess
Understand the architecture, AI components, current QA, automation, evaluation and release process.
02
Evaluate
Identify the quality risks that matter across functional behaviour, APIs, UX, security, accessibility, performance, LLMs, RAG, agents and conversations.
03
Assure
Define what should be tested, evaluated, automated, monitored and evidenced before production.
YOU LEAVE WITH
Decisions, priorities and evidence — not another generic report.
AI Quality Risk Map
Test & Evaluation Strategy
Priority Test Journeys
Automation & Regression Opportunities
AI Evaluation Priorities
Release Evidence Plan
WHY SURESH
AI is the newest layer of a quality engineering career.
I bring a broad QA and quality engineering perspective across functional testing, automation, APIs, web and mobile, accessibility, security, performance, regression and release assurance — extended into LLM, RAG, agentic and conversational AI testing.
The goal is not to replace your QA approach with one AI methodology. It is to apply the right evidence to each quality risk.
START HERE
Bring the real system. Leave with a practical quality plan.
You do not need to prepare a perfect presentation. Bring your architecture, current QA approach, known pain points and the release question you are trying to answer.