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)
| Score range | Color | Label |
|---|---|---|
| 85%+ | Green | Excellent match |
| 70–84% | Yellow | Good match |
| 60–69% | Orange | Partial match |
| Below 60% | Red | Weak match |
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.| Criterion | What it measures | Source data |
|---|---|---|
| Job title | How closely the candidate’s current role matches the title you searched for | LinkedIn experience |
| Skills | How many of the required skills the candidate has | LinkedIn skills, experience, CV |
| Location | How close the candidate’s location is to your target | LinkedIn profile location |
| Years of experience | Whether the candidate’s total experience meets your requirement | LinkedIn experience history |
| Industry | Whether the candidate’s current company operates in the target industry | Company data |
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
| Score | Color | Meaning |
|---|---|---|
| 75%+ | Green | Strong match — current role closely aligns |
| 50–74% | Orange | Partial — role is related but not identical |
| Below 50% | Red | Weak — current role differs significantly |
Skills
| Match rate | Color | Meaning |
|---|---|---|
| 70%+ of required skills matched | Green | Strong — has most or all required skills |
| 40–69% matched | Orange | Partial — some key skills present, others missing |
| Below 40% matched | Red | Weak — few or none of the required skills |
Location
| Score | Color | Meaning |
|---|---|---|
| 50%+ | Green | Matches — candidate is in or near target location |
| 30–49% | Orange | Nearby — within the broader region |
| Below 30% | Red | Distant — outside target location |
Years of experience
| Score | Color | Meaning |
|---|---|---|
| 80%+ | Green | Meets the experience requirement |
| 50–79% | Orange | Close — slightly under or over |
| Below 50% | Red | Gap — significantly below requirement |
Industry
| Score | Color | Meaning |
|---|---|---|
| 50%+ | Green | Works in the target industry |
| 30–49% | Orange | Related industry |
| Below 30% | Red | Different 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