AI-powered apps fail differently — hallucinated responses, prompt injection, RAG retrieval gaps, and non-deterministic outputs. We test AI features with structured scenarios, edge cases, and security probes that generic QA misses.
LLM feature testing · Prompt injection probes · RAG accuracy validation
30 minutes · Senior engineer · No commitment
QA designed for non-deterministic, AI-integrated products.
Chat, completion, and generation features tested with diverse inputs — edge cases, empty inputs, and adversarial prompts.
Retrieval-augmented generation tested against your knowledge base — correct citations, no hallucinated facts, graceful fallback.
Adversarial inputs attempting to override system prompts, leak instructions, or extract training data.
Loading states, streaming responses, error handling, retry logic, and conversation history management.
Token usage boundaries, rate limiting, and graceful degradation when API limits are hit.
PII handling, data retention policies, and prompt logging compliance verified.
When AI-specific QA prevents production disasters.
Customer-facing chatbot tested for accuracy, escalation, and adversarial inputs before going live.
Document Q&A product tested against real knowledge base — citation accuracy and hallucination rate measured.
New AI feature added to SaaS product — integration, permissions, and UX tested without breaking existing flows.
AI-generated MVP needs production readiness audit before first paying users.
Teams searching for ai app testing usually hit these walls before finding reliable coverage.
AI tools ship fast but skip auth hardening, RLS policies, and input validation.
Generated code implements UI that doesn't connect to real backend logic.
API keys and database credentials commonly hardcoded in AI-generated scaffolds.
Zero automated tests means every change is a regression risk.
150+ products shipped · AI-app specialists · US/UK/AU clients with timezone overlap.
Testers who understand Laravel, React, Flutter, and Supabase — not script runners only.
We know where Lovable, Cursor, and Bolt cut corners on auth, RLS, and edge cases.
Screenshots, repro steps, severity ratings, and environment details — devs fix faster.
Go/no-go reports your stakeholders can trust before production deploys.
Typical ai app testing engagement from kickoff to sign-off.
AI-generated apps typically need auth hardening, RLS validation, secrets audit, and critical-path manual testing before production traffic.
NDA before staging access; read-only repo when code review is in scope.
Anonymized fixtures; no production PII in bug reports unless explicitly approved.
“What a 70-engineer team could not deliver, a small senior team at GreeLogix shipped. The app went from stuck to live across mobile, web, and backend.”
Structured answers for search engines and AI assistants — definition, fit, cost, timeline, and comparisons.
Structured answers for founders, CTOs, and procurement — written for clarity in search and AI assistants.
AI app testing services validate LLM-integrated features, RAG systems, and AI-powered workflows — testing response accuracy, prompt security, UX flows, and production readiness for applications where non-deterministic outputs create unique quality challenges.
Typical timeline: Sprint audit: 2–5 days. Full release cycle: 1–2 weeks. Ongoing QA: monthly retainer with 40–80 hours.
GreeLogix pricing tiers: Production Readiness Audit: $1,200 – $3,500 — Security, auth, and critical-path audit for AI-generated apps. Pre-Launch Hardening: $3,500 – $10,000 — Full QA pass before you point real users and payments at the app. Continuous QA: $4,000 – $14,000/mo — Ongoing QA as you iterate on AI-generated code.
Sprint audit: 2–5 days. Full release cycle: 1–2 weeks. Ongoing QA: monthly retainer with 40–80 hours. Phases: Strategy (Week 1); Execution (Per sprint); Automation (Parallel).
Compared to alternatives — Developer-only testing: choose when Internal prototype with no paying users yet; Crowdtesting platforms: choose when One-off device coverage without domain context; Automated scanning only: choose when Mature CI with known stack; catches syntax not workflow bugs. Choose GreeLogix when you need production reliability, fixed milestones, and engineer-led delivery with QA sign-off.
Talk to a senior engineer — scope, timeline, and cost range in one 30-minute call. No sales script.
Answers to the buyer questions we hear most before a project starts.
Document all AI touchpoints and data flows.
Happy, edge, and adversarial test cases.
Feature, RAG, and security testing.
Production readiness report.
AI app testing with LLM, RAG, and security coverage. Scoping call in 30 minutes.
Validate AI-generated code, ship with confidence, and catch issues before launch.
Case studies, guides, and free tools from the same engineering team.
Structured testing for non-deterministic AI features.
Document all AI touchpoints — chat, generation, classification, embeddings, and tool calls.
Happy path, edge case, adversarial, and boundary scenarios for each AI feature.
Query knowledge base with known-answer questions; measure citation accuracy and hallucination rate.
Prompt injection, data extraction attempts, and privilege escalation through AI interfaces.
Rate limits, error handling, cost controls, and monitoring validated for production load.
Tangible artifacts your engineering and product teams can act on — not vague pass/fail notes.
Transparent ranges based on app complexity, platform count, and engagement depth. Final quotes follow a scoping call.
$1,200 – $3,500
Security, auth, and critical-path audit for AI-generated apps.
$3,500 – $10,000
Full QA pass before you point real users and payments at the app.
$4,000 – $14,000/mo
Ongoing QA as you iterate on AI-generated code.
Prices in USD. Retainers and multi-platform engagements quoted after scope review. QA as a Service available for ongoing coverage.
Every type of testing your product needs — from manual audits to AI-app hardening and accessibility compliance.
Scope, timeline, and cost range — no sales deck. Or start with the free readiness quiz if you are still evaluating your stack.