The method behind

The methodology rests on one thesis: hiring is data-rich and interpretation-poor. CVs compress real capability into titles and keywords, and parental leaves, health breaks and sector pivots read as defects to systems trained on straight-line careers; non-linear careers are the method's proving ground, because a system that reads them fairly reads every career more accurately; job descriptions stack several jobs under one title; and generative AI amplifies both, making every document sound more competent without making the evidence stronger. ReLoop.me's method is built to interpret through that distortion, consistently and on both sides.

The solution isn't another tool that polishes really well.
It's a fixed interpretive layer that reads both sides the same way.

How a ReLoop.me read is produced, in five steps. One, independent layered passes: the document is read several times from different analytical angles, and the layers are allowed to disagree with the document and with each other — disagreement between layers is signal, not error. Two, a separate judge: inflation and claim strength are scored by a pass that did not write the summary, because a model is the wrong auditor of its own output. Three, a fixed vocabulary: every read is classified on a closed set of profile types, career patterns, capability levels and role archetypes, which is what makes one read comparable to thousands of others. Four, structured data underneath: every report is prose on top of structured capability data — the data is the durable output, the report is its interface. Five, calibrated quality control: every read is checked against the system's own rules by an automated quality-control pass before delivery, so nothing ships as raw model output. Human calibration runs separately, as a feedback loop on the system itself — real reads are reviewed, misjudgements are found, and each correction is encoded back into the rules the engine runs on, so every read is shaped by the corrections learned from the reads before it. Underneath the report sits a structured data layer with typed fields such as profile type, career pattern, capability levels with their evidence basis, misread risk, and a pay signal framed by EU Pay Transparency Directive 2023/970; the published fragment is simplified and the real schema stays closed.

{
  "profile_type":    "Operator × Connector",
  "career_pattern":  "Crossover",
  "capability":      { "name": "Cross-functional Delivery", "level": "Expert", "basis": "evidenced" },
  "misread_risk":    { "type": "non-recency", "counter_signal": "named" },
  "pay_signal":      { "frame": "EU 2023/970", "assumptions": "named" }
}

One boundary applies across all of it: Profile Intel and Role Intel are strictly separate analytical objects. A profile read never judges a role; a role read never judges a person.
Only Role × Profile — the dedicated comparison layer — brings the two together, deliberately and with both datasets in hand.

The method applied as two products. Role Intel, seven euros, reads one job description and returns the role stated plainly, true seniority and scope versus the title, an inflation read with reasons, the capability demand the role genuinely carries, transferability across backgrounds, and a calibrated net-aware pay signal aligned to EU Pay Transparency Directive 2023/970. Example: a Head of Digital posting promising strategic leadership and a competitive salary reads as director-scope breadth carried in a senior individual-contributor seat with no team, no budget, and no pay range. Profile Intel, nineteen euros, reads one CV or career history and returns how the profile lands in the market, evidence-levelled capabilities, claims weighed against evidence, where the profile is likely misread and how to counter it, and a pay context. Example: a CV listing marketing, communications, product work, consulting and a career break reads as a cross-functional growth and translation profile with senior signal and broad transferability, likely misread because of its non-linearity. Role times Profile, the direct structured comparison of one profile against one role, is in development; it is the only layer that compares the two sides.

Fragments are illustrative. The calibration and the complete read sit in the paid products.

ReLoop.me publicly shows what each report contains, what each signal means, how evidence, claims, ambiguity and risk are kept separate, what a report can and cannot decide, and the beta status, the QC pass, and the calibration process. It keeps proprietary the prompt chains and orchestration, the ontology-assignment rules, the weighting and threshold logic, the pay-derivation tables, the calibration corpus and QA rules, and the Role times Profile matching logic.

Transparency earns the trust. The boundary protects the engine.

ReLoop.me is in private beta. Solid now: role and profile interpretation, evidence anchoring, claim calibration, misread detection, non-linear-career reading, and an automated quality-control pass on every delivery, with human calibration improving the system. Still calibrating: pay bands by market, cross-border compensation, hybrid seats, edge-case seniority, and Role times Profile matching. The hard boundary: ReLoop.me is decision support, not a decision-maker — not career advice, not a hiring decision, not a salary guarantee, not personality scoring, and never an apply-or-pass verdict on a person. Vague inputs produce more inferred output, and inferred ratings are labelled as inferred.