AI Quality Engineering for AI Systems | Innocito

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United States

Innocito Technologies llc,

511 E John Carpenter Fwy,

Suite 500, Irving, TX 75062, USA.

India

Visakhapatnam

Innocito Private Limited,

Tech Mahindra Campus, Vizag City Center,

Tower 2, Survey No. 44P, Old Resapuvani Palem,

Visakhapatnam, Andhra Pradesh,

India–530013

India

Hyderabad

Innocito Private Limited,

3rd Floor, The Business Park by Pranava Group,

Landmark Residency, Kondapur,

Hyderabad, Telangana

India-500084

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AI Quality Engineering (Testing AI)

AI Quality Engineering (Testing AI)

Quality engineering for AI you can trust in production.

Traditional test automation assumes software behaves the same way every time. AI agents don't. Innocito validates agentic and ML systems end to end accuracy, safety, resilience, and performance through a structured, seven phase testing framework purpose built for non deterministic systems.

Book an AI Quality Assessment

Read AI Testing Playbook

Five gaps that break conventional testing.

Why AI Needs a Different Approach

The same input can produce different outputs, agents choose their own tools, and multi agent workflows create behaviour no single test can predict. These five gaps define where assurance must evolve.

01

Non Determinism

One prompt, many valid answers. Assert equals fails quality must be measured by semantic similarity, rubric scoring, and statistics across many runs.

02

Tool Use Decisions

Agents autonomously pick tools, APIs, and parameters. A wrong choice causes silent failures that propagate quietly downstream.

03

Multi Agent Emergence

Collaboration creates behaviour no single agent shows alone. Orchestration bugs, handoff failures, and goal drift are invisible to unit tests.

04

Context & Memory Drift

Token limits, stale vector entries, and context window errors degrade quality gradually hard to catch without specialised tests.

05

Safety Surface Area

Prompt injection, PII leakage, hallucination, and guardrail bypass are entirely new attack vectors with no equivalent in traditional software.

01

Non Determinism

One prompt, many valid answers. Assert equals fails quality must be measured by semantic similarity, rubric scoring, and statistics across many runs.

02

Tool Use Decisions

Agents autonomously pick tools, APIs, and parameters. A wrong choice causes silent failures that propagate quietly downstream.

03

Multi Agent Emergence

Collaboration creates behaviour no single agent shows alone. Orchestration bugs, handoff failures, and goal drift are invisible to unit tests.

04

Context & Memory Drift

Token limits, stale vector entries, and context window errors degrade quality gradually hard to catch without specialised tests.

05

Safety Surface Area

Prompt injection, PII leakage, hallucination, and guardrail bypass are entirely new attack vectors with no equivalent in traditional software.

The Innocito View

A Purpose-built practice

"Closing these gaps takes AI native evaluation combined with enterprise grade process rigour. Our framework addresses every one of them, systematically."

Our AI Testing Framework

Seven phases. Defects caught early, where they're cheapest to fix.

A layered framework that progresses from individual agent correctness through to full safety and guardrail assurance each phase building on the last.

01

Agent Unit & Prompt Testing

Validate individual agents in isolation correctness, prompt stability, and schema compliance before they ever join a workflow.

02

Workflow & Integration Testing

Verify multi agent handoffs, orchestration, and planner executor loops with trace based assertions and end to end macro validation.

03

Context & Memory Validation

Benchmark RAG retrieval accuracy, context window handling, memory staleness, and recall across long conversations.

04

Adversarial & Scenario Fuzzing

Probe with injection, jailbreaks, encoded attacks, and property based fuzzing then codify every finding as a regression test.

05

Resilience & Chaos Testing

Inject network faults and kill dependencies to confirm retries, circuit breakers, failover, and graceful degradation hold up.

06

Performance, Load & Soak Testing

Validate latency, throughput, scalability, and long run stability across baseline, peak, stress, spike, and soak scenarios.

07

Guardrail & Safety Validation

Enforce PII protection, toxicity limits, faithfulness, and production grade injection resistance across every output.

What We Deliver

From strategy to measurable assurance.

Practical, integrated deliverables your teams can adopt immediately engineered into your sprints and CI/CD, not bolted on at the end.

Agent & model evaluation datasets

Prompt regression suites in CI/CD

Multi agent integration test harnesses

RAG & retrieval accuracy benchmarking

Adversarial red team campaigns

PII, toxicity & hallucination guardrails

Resilience & chaos test suites

Load, stress & soak performance testing

Release readiness gates & quality reporting

The Bar we engineer to

≥95%

Task success rate on end to end workflows

<2%

Hallucination rate on evaluation datasets

100%

Known injection patterns blocked

P95<8s

Latency at projected peak load

Engagement Models

Flexible ways to work with us.

Whether you need a maturity assessment or a fully embedded quality team, we meet you where your AI program is today.

Assessment & Roadmap

A 2 to 4 week evaluation of your AI testing maturity. You receive a gap analysis, prioritised roadmap, tool recommendations, and effort estimate.

Framework Implementation

End to end build of the seven phase framework tool setup, CI/CD integration, evaluation datasets, and full knowledge transfer.

Managed QE Service

A dedicated Innocito QE team embedded in your sprints: continuous testing, red teaming, load testing, and quality reporting.

Red Team as a Service

Periodic adversarial campaigns where our security focused AI testers probe your system with the latest attack techniques.

Training & Enablement

HHands on workshops for your QA, Dev, and BA teams covering AI testing fundamentals, tooling, and framework adoption.

Not sure where to start?

Begin with a focused AI quality assessment. We'll map your risks and recommend the fastest path to production grade assurance.

Ship AI that's accurate, safe, and resilient.

Talk to the Innocito AI Quality Engineering team to schedule an initial consultation and a tailored assessment of your AI systems.

Schedule a consultation