— Talent & Leadership
Benchmarks employer reviews and public reputation signals against hiring competitors, revealing strengths and weaknesses in the employee offer and evidence-backed priorities for employer positioning.
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
— USE CASES
Identifies which elements of the employee offer have credible support in reviews and which attract criticism. Provides evidence-backed recommendations for an employer value proposition that reflects the reported experience.
Compares the organisation with two or three hiring competitors across seven aspects of the employee offer. Highlights relative advantages and weaknesses that can inform recruitment positioning.
Surfaces the strongest negative reviews and their key phrases alongside public signals such as redundancy announcements, news coverage and employer awards. Gives talent leaders a clearer view of concerns candidates may encounter.
Provides a comparative heatmap, sentiment summaries and a written benchmark that make differences between employers easier to discuss. Grounds decisions about employer positioning in a shared body of evidence.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Current external sources on the talent population are researched during the run rather than recalled from training data, and every source travels with the output.
Each row of your export is processed on the same basis, so no person is skipped however long the table is.
Movement across the talent population is visualised from the computed data, so the trend is legible at a glance.
Findings on the talent population are written up as a document that reads like professional output, with each claim tied back to a person.
Detail rows are summarised into the grouped view of the talent population without losing the underlying people.
Employer reputation is shaped by employee experience and the public signals candidates encounter, but scattered reviews make competitive differences difficult to judge. Refreshing the employer proposition is the typical trigger — identifies which elements of the employee offer have credible support in reviews and which attract criticism. Provides evidence-backed recommendations for an employer value proposition that reflects the reported experience. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 2–3 weeks of experienced attention.
Skillsize turns that work into a Skill: you supply the material, and what comes back is employer brand and competitor benchmark report, with comparative sentiment heatmap across seven employee offer themes. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. The practical effect: 2–3 weeks of manual work becomes a ~40 minutes run, held to an identical standard on the tenth engagement as on the first.
Employer brand & competitor benchmark exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
Builds evidence-backed employee skills profiles within HR systems, mapping documented experience to the organisation’s skills catalogue and assigning proficiency levels using consistent rules.
Assesses how clearly people explain their technical work against a consistent four-level rubric, producing a human-approved report with quoted evidence behind every score.
Reveals how organisational culture is lived through anonymous employee accounts, producing an evidence-backed diagnostic of shared themes, differences between functions and gaps between values and practice.
Turns a high-potential's assessment evidence into an acceleration plan weighted to real experience and exposure, naming the stretch assignments, mentors and programmes that will compound fastest.
Builds a calibrated, reusable benchmark from source material so every later screen, shortlist or review is graded against the same bar.
Screens candidate evidence against a saved Blueprint, applying the same calibrated bar and traceable reasoning to every applicant.
