Buyer's Guide

AI MLR Software Evaluation Checklist

Choosing an AI-powered MLR review platform is a significant decision. This checklist helps pharmaceutical marketing, medical, and regulatory teams ask the right questions before they buy, so they can compare vendors on accuracy, security, implementation, agency efficiency, and measurable business value.

Accuracy and Hallucination Control

  • Does the platform check content only against your approved claims, references, and labeling rather than open internet data?
  • Can every flagged issue and suggestion be traced back to a specific approved source?
  • How does the vendor prevent, detect, and measure AI hallucinations?
  • What accuracy rates has the platform demonstrated on real pharmaceutical promotional content?
  • Does a human remain in control of every final decision?

Security and Privacy

  • Is your content kept private and never used to train shared or public AI models?
  • What security certifications and independent audits does the vendor maintain?
  • How is data encrypted in transit and at rest, and where is it hosted?
  • Are access controls, user permissions, and audit trails built in?
  • Will the vendor complete your IT and information security review without delays?

Implementation and Time to Value

  • How quickly can the platform be deployed, and what is required from your internal teams?
  • Does it integrate with your existing systems, such as Veeva PromoMats or Aprimo?
  • Can you start with a low-risk pilot on a single brand before scaling?
  • How long does it take to build your first claims library?
  • What training, onboarding, and ongoing support are included?

Automated Annotations and Agency Efficiency

  • Does the platform automatically link and anchor claims to their supporting references?
  • Can agencies precheck content before it is submitted for MLR review?
  • Does it flag unsubstantiated claims, inconsistent language, and missing fair balance?
  • How much manual annotation and reference-checking time does it remove?
  • Can agencies and brand teams work from the same approved claims library?

Measurable Business Value

  • What reductions in review cycle time and resubmissions can you expect?
  • How will the vendor help you measure ROI during and after the pilot?
  • Does the platform reduce rework for agencies, brand teams, and reviewers?
  • Can it help you get content to market faster and respond quickly to new data?
  • Is pricing transparent and aligned with the value delivered?

Why SecureCHEK AI Deserves a Closer Look

Grounded in your approved content

SecureCHEK AI checks new content against your own validated claims library, so every result is traceable to approved evidence.

Builds claims libraries with ease

Automatically creates structured, validated claims libraries from your existing approved materials and references.

Prechecks content before MLR

Identifies preventable issues before submission so reviewers can focus on the strategic points that matter.

Security-first by design

Your data stays private and is never used to train public models, backed by rigorous security practices.

See Where You Stand

MLR Bottleneck Assessment

Find out whether your claims management approach is slowing down MLR review, and where it is costing you time.

Take the Assessment

Precheck Readiness Assessment

See whether your pre-MLR quality checking process is catching preventable issues before content reaches review.

Take the Assessment