Business fit
Is the system improving a material workflow and can the baseline be measured?
AI Systems Review
Your AI system exists. Now determine whether it deserves trust.
PlanckCyber reviews existing AI applications, agents and automations to determine what is working, what is creating risk or friction, and what should change next. The review connects business outcomes to technical evidence across workflow fit, model behavior, retrieval, integrations, permissions, evaluation, cost and operating ownership.
When It Fits
Use a review when the system is live—or close to live—but the evidence is weak.
- Users do not trust the output consistently
- An AI agent completes some tasks but fails unpredictably
- Retrieval answers are stale, unsupported or difficult to verify
- Costs or latency are higher than expected
- Permissions and human authority are unclear
- A pilot works in demos but production readiness is disputed
- A vendor or internal team proposes expansion before the current system is measured
What It Is Not
An engineering review, not a badge.
The standard engagement does not claim regulatory certification, legal compliance, a penetration test or a guarantee of performance. It produces an evidence-based technical and operating assessment, with deeper security or specialist review scoped separately when needed.
Review Areas
Evaluate the system from outcome to operation.
Quality & evaluation
Are representative cases, acceptance thresholds and failure modes recorded?
Architecture
Are model, retrieval, orchestration and state choices appropriate to the requirement?
Data & knowledge
Are sources authorized, current, retrievable and access-controlled?
Tools & integrations
Do API behavior, identity, rate limits and transactions match production reality?
Authority & controls
Are allowed actions, approvals, escalation, logging and rollback explicit?
Cost & latency
Are model, infrastructure and tool costs measured against the value of the workflow?
Operations
Who owns monitoring, incidents, model changes, user feedback and controlled improvement?
Vendor & exit risk
Can the organization change providers or architecture without losing essential data or control?
Typical Inputs
Review real evidence where authorization permits.
- Business objective and current workflow
- Architecture diagrams and system inventory
- Representative test cases and output samples
- Prompt, tool and retrieval configurations where in scope
- Model/provider settings and cost records
- Integration and permission design
- Incident, error and user-feedback history
- Existing runbooks, policies and acceptance criteria
Possible Outputs
A prioritized decision package.
- Current-state findings with supporting evidence
- Risk and reliability issues by impact and urgency
- Evaluation gaps and recommended test coverage
- Architecture or workflow changes
- Cost and operational observations
- Build, repair, expand, replace or stop recommendations by issue—not a forced overall verdict
- Implementation sequence and dependencies when remediation is justified
Review Method
Trace claims back to evidence.
01
Define the intended outcome
02
Collect authorized evidence
03
Test representative cases
04
Trace failures to the system
05
Prioritize changes
06
Re-test material fixes
The purpose is to make the next investment decision more defensible. A review can identify work that should be done, work that should wait, and areas where the current system is already adequate.
FAQ
AI systems review questions.
What is an AI systems review?
An AI systems review is a structured evaluation of an existing AI application, agent, automation or retrieval system against its intended business outcome, workflow fit, architecture, data, integrations, evaluation evidence, controls, cost and operating ownership.
Is this a security penetration test or compliance certification?
No. The standard review is an engineering and operating assessment. Security testing, legal conclusions, audits or certification work require separate scope and the appropriate qualified specialists.
Do we have to replace our current system?
No. The review can lead to repair, tighter controls, better evaluation, architecture changes, expansion, replacement or a recommendation to leave the system as it is.
Can PlanckCyber review a system built by another vendor?
Yes, when the client can provide the authorized technical and operating evidence needed for a useful review. The goal is to evaluate the system, not to criticize the original builder.
Start with the problem
Need a clearer read on an existing AI system?
Share a non-confidential description of the system, the workflow it supports and what is not meeting expectations. PlanckCyber can determine whether a focused review is the right next step.