Overall relevance score
The overall score is a percentage (0–100%) representing how well the candidate matches across all the criteria you searched for. It is the average of the individual criterion scores, weighted equally.Color coding (overall score)
The color appears on the candidate card, the score pill, and the avatar ring — giving you instant visual feedback.
Search criteria
AI Scout evaluates candidates on up to five criteria, depending on what you searched for. Only criteria you actually include in your search produce scores and summary sentences.Per-criterion scoring and colors
Each criterion has its own thresholds. The per-criterion colors appear as highlighted chips in the candidate summary and as colored cells in the table view.Job title
Skills
Location
Years of experience
Industry
How the summary works
Each candidate card includes a natural-language summary that explains the match. The summary is fully explainable — every claim is grounded in the score data.Structure
- Opening line — Overall relevance percentage and number of criteria evaluated. Example: “Excellent match across 4 criteria (87%).”
- Per-criterion sentences — One sentence per active criterion, ordered by score (strengths first). Each sentence names the specific data point and explains the match. Example: “Currently a Senior Backend Engineer — strong match with your search.”
- Highlighted chips — Key data points inside the summary are highlighted with color-coded chips. The chip color matches the criterion’s score tier (green, orange, or red).
Example summary
Excellent match across 4 criteria (87%). Currently a Senior Backend Engineer — strong match with your search. Has all 4 required skills: Python, Django, AWS, and PostgreSQL. Based in Berlin — matches your location criteria. Has 6 years of experience, meeting your 5+ years requirement.In the summary above:
- “Senior Backend Engineer” would be a green chip (job title match)
- “all 4 required skills…” would be a green chip (skills match)
- “Berlin” would be a green chip (location match)
- “6 years of experience” would be a green chip (experience match)
Polished summaries
After the initial deterministic summary is generated, Lope sends it to an LLM for natural-language polishing. The polished version reads more naturally while preserving all the same data points and color-coded chips. The polishing happens in the background — you see the template summary first, then it’s replaced with the polished version.Score contribution
Each criterion contributes equally to the overall score. The contribution is calculated as:Source links
Each highlighted chip in the summary links back to the source data. Hover over a chip to see:- Job title / Years — Links to the candidate’s LinkedIn experience section
- Skills — Links to the LinkedIn skills section
- Location — Links to the candidate’s LinkedIn profile
- Industry — Links to the company’s LinkedIn page and website
Table view vs. list view
AI Scout results can be viewed in two layouts:- List view (cards) — Shows the full summary with color-coded chips and avatar ring
- Table view — Shows individual score columns (Job title, Skills, Location, Experience, Industry) with colored pills