Investor overview

A decision-assurance workflow, starting with hiring.

The thesis is a repeatable review process between an AI recommendation and consequential action. This page separates that thesis from verified commercial or technical evidence.

Business thesis

What must be proven.

Initial customer hypothesis

HR and talent teams reviewing AI-assisted screening, with a named HR owner and participation from security, IT, and procurement.

Alternatives to evaluate

Existing manual review, single-model checks, vendor-native controls, and internally built orchestration. Compare against the customer's actual baseline.

Economic hypothesis

A platform-and-usage structure can be explored in commercial discussions. Actual pricing, gross margin, retention, and recurring revenue must be demonstrated.

Potential differentiation

Evaluation controls, evidence review, workflow adoption, and supported integrations may differentiate the product. None is treated as an established moat merely because several models are connected.

Expansion thesis

Prove usefulness and purchasing behaviour in hiring before extending the same controls to adjacent workflows. Other industries are not represented as current customers.

Next evidence milestones

A working end-to-end pilot, paid-customer evidence where available, baseline evaluation, repeatable implementation, and measured unit economics.

Evidence

Separate results from plans.

Paid pilots, unpaid design partners, contracts, and exploratory conversations are not represented as interchangeable traction. No quantities are published without approved supporting information. No accuracy lift, valuation, market-share figure, or validated assurance score is asserted here.

Any market model should start with an identified customer segment, addressable decision volume, realistic pricing assumptions, and purchasing evidence. Confidential candidate information is not a proprietary-data advantage without the necessary rights.

Request an investor discussion See the workflow example