The most accurate company data enrichment API
Eight APIs—People Data Labs, Apollo, Parallel, Predict Leads, Explorium, CompanyEnrich, ZoomInfo, and Exa—receive the same company domain and enrich it by API calls from their official docs.
“Most accurate” hides two different questions, and the measured answers differ: People Data Labs returns the most correct data end to end (89.0% correct field yield), while Apollo writes the fewest wrong values among what it returns (92.6% accuracy when present). Pick by which failure mode costs you more: missing data, or wrong data.
Which company enrichment API is best?
There isn't one best enrichment API. The right choice depends on what happens next: filling CRM records, avoiding bad overwrites, matching a prospect list, returning complete company profiles, or powering a latency-sensitive product flow. Choose the job below; each recommendation is based on the metric that matters for it.
Which provider wins depends on the use case — CRM writeback, prospecting and matching, complete company profiles, or real-time product flows. The workflows below are ranked separately for each.
| Workflow | Recommended API | Measured evidence |
|---|---|---|
| Best API for keeping CRM records current | People Data Labs | 89.0% enrichment success rate |
| Best API for safe CRM writeback | Apollo | 92.6% field accuracy |
| Best API for the most complete company data | Parallel | 94.7% field coverage |
| Best API for matching a company list | Predict Leads | 100.0% company match rate |
| Best API for real-time product or agent enrichment | People Data Labs | 274 ms median latency |
Compare every measured company enrichment API.
This is the overall provider table from the current 282-company benchmark: resolution, enrichment success, field accuracy, coverage, latency, cost, and the exact endpoint tested. Read the complete benchmark methodology for cohort design, Ground Truth, field judging, pricing assumptions, and public evidence.
| Provider | Official docs | Resolution | Enrichment success rate | Field accuracy | Field coverage | Median latency | Est. cost | Endpoint & configuration |
|---|---|---|---|---|---|---|---|---|
| Apollo | Apollo official docs | 96.8%273/282 domains | 88.6% | 92.6% | 93.0% | 654 ms | $10.88$0.0196 / credit | POST /api/v1/mixed_companies/search → GET /api/v1/organizations/{id}page=1; per_page=1 |
| CompanyEnrich | CompanyEnrich official docs | 98.6%278/282 domains | 69.7% | 72.5% | 93.7% | 309 ms | $2.76$0.0098 / credit | GET /companies/enrichdomain={company domain} |
| Exa | Exa official docs | 100.0%282/282 domains | 60.4% | 76.2% | 77.1% | 8,143 ms | $4.23$0.015 / deep-reasoning request | POST /searchtype=deep-reasoning; category=company; numResults=10; highlights + structured output |
| Explorium | Explorium official docs | 95.0%268/282 domains | 70.5% | 87.6% | 76.0% | 616 ms | $22.00$0.04 / credit | POST /v1/businesses/match → POST /v1/businessesmode=full; page=1; page_size=10; size=10 |
| Parallel | Parallel official docs | 99.7%281/282 domains | 86.0% | 89.4% | 94.7% | 21,317 ms | $14.10$0.050 / Responses request (medium reasoning) | POST /v1/responsesmodel=parallel; reasoning.effort=medium; JSON schema output |
| People Data Labs | People Data Labs official docs | 98.2%277/282 domains | 89.0% | 91.2% | 94.3% | 274 ms | $27.70$0.10 / match | GET /v5/company/enrichwebsite={company domain} |
| Predict Leads | Predict Leads official docs | 100.0%282/282 domains | 80.6% | 86.5% | 87.9% | 556 ms | $40.00$0.04 / API credit · 100 free; $40 monthly minimum | GET /api/v3/extended_companies/{domain}1 company domain per request; follows API redirect target when returned |
| ZoomInfo | ZoomInfo official docs | 94.7%267/282 domains | 63.9% | 70.4% | 88.7% | 1,132 ms | $93.45$0.35 / new company record | gtm companies enrich--domain {company domain} --fields name website domainList city state country primaryIndustry industries employeeCount employeeRange foundedYear socialMediaUrls |
Compare provider results field by field.
This is the main benchmark's per-field table for HQ location, industry, employee band, founded year, and LinkedIn URL. Each value is correct-field yield among companies carrying a reviewed reference for that attribute, so denominators differ by column.
| Provider | Official docs | HQ location | Industry | Employee band | Founded year | LinkedIn URL |
|---|---|---|---|---|---|---|
| Apollo | Apollo official docs | 87.3%240/275 | 95.4%269/282 | 78.2%219/280 | 91.8%191/208 | 90.8%256/282 |
| CompanyEnrich | CompanyEnrich official docs | 50.9%140/275 | 74.8%211/282 | 47.5%133/280 | 90.4%188/208 | 91.1%257/282 |
| Exa | Exa official docs | 82.9%228/275 | 96.1%271/282 | 33.2%93/280 | 97.6%203/208 | 5.3%15/282 |
| Explorium | Explorium official docs | 73.5%202/275 | 84.8%239/282 | 88.9%249/280 | 0.0%0/208 | 86.5%244/282 |
| Parallel | Parallel official docs | 90.9%250/275 | 92.9%262/282 | 80.7%226/280 | 76.4%159/208 | 86.2%243/282 |
| People Data Labs | People Data Labs official docs | 90.2%248/275 | 94.0%265/282 | 86.4%242/280 | 93.8%195/208 | 84.0%237/282 |
| Predict Leads | Predict Leads official docs | 68.4%188/275 | 88.3%249/282 | 78.2%219/280 | 89.9%187/208 | 84.8%239/282 |
| ZoomInfo | ZoomInfo official docs | 73.1%201/275 | 74.5%210/282 | 47.9%134/280 | 61.4%129/210 | 62.4%176/282 |
Every provider on both definitions of accurate
Judged field-by-field against human-reviewed Ground Truth, on identical inputs. Higher is better on both:
| Provider | Enrichment success rate (correct and complete) | Field accuracy (right when returned) |
|---|---|---|
| People Data Labs | 89.0% | 91.2% |
| Apollo | 88.6% | 92.6% |
| Parallel | 86.0% | 89.4% |
| Predict Leads | 80.6% | 86.5% |
| Explorium | 70.5% | 87.6% |
| CompanyEnrich | 69.7% | 72.5% |
| ZoomInfo | 63.9% | 70.4% |
| Exa | 60.4% | 76.2% |
Fields with no reference data don't count against anyone. Per-company, per-field judgments are public. Full provider comparison and evidence matrix →
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 (enrichment success rate): People Data Labs at 89.0%. Fewest wrong values among returned fields (field accuracy): Apollo at 92.6%. 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 field accuracy), or return few fields but be right about them. That's why a single 'accuracy' number is usually vendor marketing — the benchmark separates enrichment success rate, field accuracy, and coverage, and reports all three per provider on identical inputs.
How is accuracy judged?
GPT-5.6-terra compares HQ, industry, founded year, and LinkedIn URL against human-reviewed Ground Truth with strict structured verdicts. GPT-5.6-sol at medium reasoning applies the dedicated numeric employee-band policy. The taxonomy-aware rubric accepts consistent industry umbrella and subsector labels; fields with no reference data are excluded from the denominator. 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.