AI Reliability Audit
Adversarial assessment across hallucinations, prompt injection, consistency, and product-specific edge cases. Delivered as a ranked failure-mode report and prioritized fix plan.
From demo to dependable production
Operationalize AI. Ship with confidence.
DevOpsify Cloud turns promising AI features into production systems that run reliably — with failure-mode testing, meaningful evaluations, and quality gates around every release.
Release quality gate
Evaluation complete
Groundedness
Unsupported claims and citation quality
Adversarial behavior
Injection, jailbreaks, and boundary cases
Regression coverage
Model, prompt, and retrieval changes
Release decisionEvidence attached to every quality signal
Ready for review
LLM evaluations
Adversarial testing
Release governance
The launch gap
Demos impress. Production reveals.
An AI feature can look polished in a demo and still fail the first real customer who phrases a question differently.
01
Silent regressions
A model, prompt, or retrieval change improves one path and quietly breaks another.
02
Unmapped edge cases
Teams test happy paths while adversarial, ambiguous, and high-impact inputs go unexamined.
03
Subjective launch decisions
Without shared thresholds and evidence, “good enough” changes from meeting to meeting.
Services
Start with evidence. Build toward confidence.
Focused engagements for teams with an AI feature in development, in pilot, or already facing production pressure.
Eval & Guardrail Build
A practical evaluation harness with regression scenarios, red-team cases, guardrails, and release checks your team can run as the product changes.
AI Quality Retainer
Ongoing evaluation maintenance, pre-release quality review, failure analysis, and quality operations for teams shipping AI continuously.
The quality system
Every gate traces back to user impact.
No generic scorecard. The work starts with how your product can fail, who it affects, and which signals should block a release.
Map the risk
Define critical user journeys, business constraints, and the failure modes that matter most.
Stress the system
Run adversarial, edge-case, and consistency testing against the current experience.
Build the evidence
Turn discoveries into repeatable scenarios, evaluation criteria, and regression checks.
Gate the release
Set clear thresholds and a review rhythm the product and engineering teams can sustain.
Why DevOpsify Cloud
Operationalizing AI is a quality discipline.
DevOpsify means we operationalize AI: closing the gap between a promising demo and a feature that runs reliably in production. Founded in 2023 by a Quality Engineering and Product Operations specialist from Meta, the practice treats AI quality not as a benchmark at the end of development, but as an operating system for better release decisions.
That brings engineering rigor and product clarity together, so teams can move from experimentation to dependable operation.
Built around your productTest the journeys and risks your users actually encounter.
Actionable by designEvery finding has an owner, impact, and recommended next step.
Designed to keep workingLeave with a repeatable quality practice, not a one-time report.
A practical first step
Put your AI feature under real pressure.
Begin with a one-week reliability audit. Share the product context, the critical user journeys, and the release questions your team needs answered.