Methodology
QuanTies combines self-described profile information with clearly labelled reference material. The goal is to make creative ties more concrete, while keeping people—not a model or score—in control of any decision.
The workspace asks participants to describe interests, strengths, experience and ways of working in their own words and through structured prompts. These responses are not a diagnosis or a definitive account of capability. They represent what a person chooses to share for the purpose of a project conversation.
Where role vocabulary is useful, QuanTies draws on occupational reference data to help people name related skills, knowledge areas and work preferences. Reference material is a guide, not a claim that any individual belongs in a particular role.
The Occupational Information Network (O*NET) is one public source used for this vocabulary. Its role and content are documented by the O*NET Resource Center. QuanTies adds its own interface, prompts and project-focused context; it does not reproduce a generic occupational directory as its public content.
A profile should never be used as the sole basis for selecting, excluding or labelling a person. Results can reflect incomplete information, changing priorities and the limits of any structured prompt. Participants should be able to challenge assumptions, update their information and choose whether to pursue a connection.
For practical ways to keep these conversations grounded, read the project teams guide.
QuanTies separates member-provided information, reference material, algorithmic processing and human judgement. Incomplete or outdated information can affect any suggestion, so members can update their profile and should challenge an assumption that does not fit.
Quant is optional where enabled. It provides suggestions in response to a member’s request; it does not infer protected characteristics or make a decision about a person.
Owner: QuanTies Ltd · Last reviewed: 27 August 2026 · Change note: clarified the distinction between self-description, reference data, algorithmic processing and human judgement for the public-site upgrade.
