Methodology

How HRSuggest shortlists work

Deterministic (no ML). Built for India payroll buyers where the biggest failures show up at month-end — in statutory outputs, edge cases, and audits.

Scoring weights

What drives a shortlist (example)

Your output is a weighted blend of compliance depth, evidence quality, freshness, integration visibility — minus penalties for missing data.

Compliance depth

30%

Evidence depth (source links)

25%

Freshness (verification recency)

20%

Integration depth

15%

Missing data penalty

−10%

If evidence/integration/compliance signals are unclear, we treat them as risk and explicitly penalize the score.

Note: This is a deterministic scoring layer — not a “review” site. If a claim can’t be tied to evidence, it becomes a demo validation item.

Verified vs Validate

Designed for decision risk

Verified

Key metadata checked against evidence links. Freshness date shown.

Validate

Missing/unclear items are treated as demo checks — so you test edge cases before buying.

Freshness logic

Recency is explicit

Freshness is driven by the newest lastVerifiedAt date on published tools. If no verification date is present, we treat it as “needs validation”.

Vendor “last updated” claims are informational only — they do not automatically improve rankings.

No vendor bias

Rankings aren’t for sale

HRSuggest does not accept payments to change rankings.

If a vendor has missing signals, we penalize it. The goal is fewer surprises during implementation and audits.

Apply this methodology to live buyer pages

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