Saral Fit Score
Rankings you can check.
Every candidate tagged Excellent, Good, Partial or Weak fit on live proof of work, with the evidence linked to its source.
Arjun MehtaSenior Backend Engineer · TechFlowExcellent fit- Maintains a Node.js payments librarySource
- Steady commits to production repos in the last 90 daysSource
- Two promotions in four yearsSource
Saral reads GitHub activity, LinkedIn trajectory and technical discussion on X and the open web.
What is the Saral Fit Score?
Saral Fit Score is a fit rating (Excellent, Good, Partial or Weak fit) that ranks candidates on public signals of real work, with each signal linked to its source.
How it works
How does AI candidate ranking work in Saral?
- 001
Read public signals
Saral reads GitHub activity, LinkedIn trajectory and technical discussion on X and the open web.
- 002
Weigh them against your brief
Each signal is weighted by how much it matters for the role you described.
- 003
Show the evidence
The score arrives with the signals that earned it, each linked to its source.
Questions about Saral Fit Score.
What do the fit tags mean?
Each candidate is tagged Excellent fit, Good fit, Partial fit or Weak fit. Open any card to see the signals behind the tag. The evidence matters more than the label.
Which signals feed the score?
Code activity and repository depth on GitHub, career trajectory and tenure on LinkedIn, and technical discussion on X and the open web. The full list is on the Signal Index page.
Can I change how candidates are ranked?
Yes. You can adjust what matters for a role and override any ranking. AI ranks. You decide.
Does the Fit Score use personal attributes?
No. Photos, gender, age, religion, caste and marital status are never used in scoring.