For AI Companies
Enterprise AI readiness audits
PreMetric audits the system evidence, vendor claims, deployment assumptions, controls, governance posture, and buyer approval logic behind AI products before enterprise procurement, regulated-sector scrutiny, investor diligence, strategic review, or acquisition.
AI product
Claims · system evidence · deployment assumptions
Readiness audit
Technical + institutional evidence review
Buyer-ready evidence record
AI Decision Record · claim-to-evidence summary
Enterprise scrutiny
Procurement · diligence · regulated buyers
Point-in-time audit. Produces an AI Decision Record.
The questions enterprise buyers now ask
Enterprise buyers are no longer assessing AI companies only through demos, pilots, or product claims. They increasingly ask whether the system evidence is credible, whether deployment assumptions are realistic, whether controls and accountability are defined, and whether the product can withstand procurement, security, legal, compliance, risk, and executive review.
Procurement teams, legal functions, risk committees, and boards are applying structured scrutiny to AI products before committing. Many AI companies are unprepared for this layer of questioning — not because their product lacks merit, but because the governance evidence chain has not been built.
Governance
Who is accountable if this goes wrong? Is there a documented governance structure?
Deployment risk
What are the realistic failure modes and how are they bounded?
Procurement approval
Does this product meet the approval criteria of legal, procurement, risk, and compliance?
ROI evidence
Are the value claims credible, measurable, and verifiable?
Accountability
Is it clear who owns each decision in the deployment chain?
Data exposure
What data is used, retained, or shared, and what does that mean for the buyer?
Regulatory defensibility
Can this deployment be defended under the regulations applicable to the buyer?
What PreMetric provides
PreMetric conducts an AI Vendor Enterprise Readiness Audit to examine the technical and institutional evidence behind an AI company's enterprise proposition. The audit assesses product claims, model or system evidence, benchmark results, validation materials, deployment assumptions, data dependencies, governance posture, risk boundaries, human oversight expectations, and buyer approval logic.
This is not AI consulting, product feedback, or market positioning advice. The audit produces documented outputs that enterprise buyers, procurement teams, regulated-sector customers, and investors can use to evaluate and approve deployment.
The audit examines
- —Product claims, model or system evidence, and benchmark results
- —Validation materials, deployment assumptions, and data dependencies
- —Governance posture, human oversight expectations, and accountability model
- —Risk boundaries and how they are defined and communicated
- —Buyer approval logic and procurement readiness
- —Investor and strategic diligence readiness
- —The evidence chain a buyer, board, or investor needs to approve the decision
When to use it
The AI Vendor Enterprise Readiness Audit is most valuable before a high-stakes decision point — when governance readiness and institutional defensibility will be evaluated by an external party.
- —Before entering enterprise procurement or a formal RFP process
- —Before selling into regulated industries — financial services, healthcare, insurance, public sector
- —Before finalising a strategic partnership or OEM arrangement
- —Before a follow-on financing round where diligence will include product governance
- —Before an acquisition process or investor diligence review
- —When customers ask for AI governance, risk, assurance, validation, or accountability evidence
This applies across software AI, agentic systems, AI-enabled workflows, and physical AI or robotics products where deployment assumptions, controls, safety, liability, or operational context affect buyer approval.
Defined outputs
Every AI Vendor Enterprise Readiness Audit concludes with a defined set of documented outputs. These are structured records, not slide decks or advisory summaries.
Enterprise readiness audit findings
A structured evaluation of where the product is ready and where gaps exist across technical, governance, and institutional dimensions.
AI Decision Record
A board-ready record documenting the audit basis, findings, and determination.
Claim-to-evidence summary
The documented chain mapping product claims to system evidence, benchmark results, and validation materials.
Deployment assumption review
Audit of the assumptions underlying deployment claims and where they may fail under buyer or operational scrutiny.
Governance and accountability gap analysis
Identification of gaps in governance structure, accountability assignment, and documentation.
Procurement readiness summary
An assessment of readiness for enterprise procurement scrutiny and approval workflows.
Proceed / modify / pause recommendation
A documented determination on whether to proceed to market, modify the proposition, or pause.
Preparing for enterprise scrutiny
The time to build institutional defensibility is before procurement, before diligence, and before regulated-sector buyers ask questions you are not prepared to answer.
PreMetric works with AI companies that are approaching those moments — companies that have a credible product and need the governance evidence chain to support enterprise approval, regulatory-sector entry, strategic review, or investor diligence.
This is not continuous monitoring or an ongoing relationship. It is a structured, bounded audit triggered by a defined decision moment — producing documented outputs you can use with buyers, boards, and investors.
The audit is designed to strengthen enterprise defensibility before buyer scrutiny begins. It does not replace formal certification, legal compliance review, or independent technical validation where those processes are required.