The most accurate company enrichment API
“Most accurate” hides two different questions, and the measured answers differ: People Data Labs returns the most correct data end to end (86.7% correct field yield), while PredictLeads writes the fewest wrong values among what it returns (88.7% accuracy when present). Pick by which failure mode costs you more: missing data, or wrong data.
Every provider on both definitions of accurate
Judged field-by-field by GPT-5.6 against identity-verified LinkedIn reference data, on identical inputs. Higher is better on both:
| # | Provider | Correct field yield (end to end) | Accuracy when present (right when returned) |
|---|---|---|---|
| 1 | People Data Labs | 86.7% | 88.6% |
| 2 | Fiber | 86.1% | 87.1% |
| 3 | Ocean.io | 85.3% | 86.3% |
| 4 | Apollo | 84.2% | 87.8% |
| 5 | PredictLeads | 83.8% | 88.7% |
| 6 | Explorium | 74.1% | 87.6% |
| 7 | CompanyEnrich | 71.1% | 73.7% |
Fields with no reference data don't count against anyone. Per-company, per-field judgments are public. Full leaderboard and evidence matrix →
There is no single “best” company enrichment API — the independent benchmark points to a different provider depending on the axis your workflow is constrained by. Every leader below is measured on 300 identity-verified companies:
- CRM data you can trust end to endPeople Data Labshighest correct field yield · 86.7%
- Few wrong values written — right when returnedPredictLeadshighest accuracy when present · 88.7%
- Fill the most fields per companyFiberhighest attribute coverage · 97.2%
- Long-tail / international companies must resolveOcean.iohighest resolution rate · 100.0%
- Real-time enrichment in product or agent flowsPeople Data Labslowest median latency · 274 ms
- Budget-constrained backfillsOcean.iolowest estimated cost · $1.92 cohort
Full leaderboard, methodology, and per-company evidence: the company enrichment benchmark →
Enrichment accuracy — common questions
What is the most accurate company enrichment API?
Two honest answers, because "accurate" means two things. Most correct data end to end (correct field yield): People Data Labs at 86.7%. Fewest wrong values among returned fields (accuracy when present): PredictLeads at 88.7%. If you're auto-writing to a CRM, the second number is the one that protects your data; if you want the fullest correct picture per company, the first.
Why do coverage and accuracy disagree?
A provider can fill every field (high coverage) and still be wrong often (low accuracy when present), or return few fields but be right about them. That's why a single 'accuracy' number is usually vendor marketing — the benchmark separates yield, accuracy-when-present, and coverage, and reports all three per provider on identical inputs.
How is accuracy judged?
GPT-5.6 compares each provider field against identity-verified LinkedIn reference data with a strict yes/no structured verdict (industry uses a separate judge that accepts taxonomy synonyms). Fields with no reference data are excluded from the denominator, and final judgments are spot-checked by a human. Every judgment is public and reproducible.
Which enrichment provider is most accurate for hard cases — subsidiaries and rebrands?
The cohort deliberately includes 80 subsidiaries (parent-company confusion) and 53 verified rebrands (stale identities) alongside stable-large and long-tail slices. Per-company results for every provider on every slice are on the benchmark's evidence matrix — that's where providers diverge most.