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What is Company Enrichment API?

Company enrichment APIs take a sparse identifier — usually a domain name or company name — and return a comprehensive firmographic profile containing headcount, revenue estimates, tech stack, industry taxonomy, and key personnel. For B2B data pipelines, it's the critical join layer that turns a raw list of scraped leads or CRM records into actionable, segmentable accounts. The value of an enrichment API is defined entirely by its match rate and data freshness.

FirmographicsData EngineeringEntity ResolutionB2B DataAPI Integration
// 02 — definitions

Sparse input,
dense output.

The mechanics of resolving a single domain name into a 150-field firmographic record, and why entity resolution is harder than it looks.

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TL;DR

A company enrichment API maps a domain or company name to a canonical entity profile. It aggregates signals from public registries, job boards, social networks, and web scrapes. The hardest engineering challenge isn't fetching the data — it's entity resolution: proving that "Acme Corp Ltd" in the UK is the exact same operating entity as "acme.io".

01Definition & structure
A company enrichment API is a service that accepts a sparse identifier — typically a domain name, company name, or registration number — and returns a dense, structured JSON payload containing firmographic data. The output usually includes:
  • firmographics — industry taxonomy, founded year, HQ location
  • metrics — estimated revenue, employee headcount, funding rounds
  • technographics — software frameworks, hosting providers, and tools detected on their domain
  • hierarchy — parent companies, subsidiaries, and alternative domains
It acts as a force multiplier for B2B data pipelines, turning raw scraped leads into segmentable accounts.
02How it works in practice
When you submit a domain, the API doesn't go out and scrape the web in real-time. Instead, it queries a massive, pre-computed entity graph. The system normalises your input, performs entity resolution to find the canonical company ID, and retrieves the associated profile from a distributed database. High-end enrichment providers constantly update this graph in the background by ingesting government registries, crawling corporate websites, and buying third-party datasets.
03The entity resolution bottleneck
The hardest part of building an enrichment API is entity resolution. If a user searches for "Amazon", do they mean Amazon.com Inc, Amazon Web Services, or Amazon Logistics? If they search for "amazon.co.uk", should it return the UK subsidiary's specific headcount, or the global parent's metrics? Resolving ambiguous inputs to the correct canonical entity is what separates enterprise-grade enrichment APIs from cheap data dumps.
04How DataFlirt handles it
We treat enrichment as a dynamic pipeline, not just a static database lookup. When our enrichment gateway receives a request, it checks the cache TTL. If the firmographic data is older than our freshness threshold, we trigger a synchronous micro-scrape to validate the target's current tech stack and active job postings before returning the payload. This ensures our clients aren't making routing decisions based on six-month-old data.
05Did you know?
B2B data decays at an average rate of 3% per month. Within a year, roughly a third of your CRM or database is factually incorrect — companies rebrand, merge, go bankrupt, or migrate their tech stacks. This is why one-off data purchases are rarely effective; enrichment must be run as a continuous, scheduled pipeline to maintain data integrity.
// 03 — enrichment metrics

How to measure
enrichment quality.

Evaluating an enrichment vendor requires looking beyond the marketing claims. A high match rate is useless if the fill rate is sparse or the data is stale. We track these three metrics continuously across our internal enrichment cascades.

Match Rate = resolved_entities / total_inputs
A 40% match rate on SMBs is typical; >80% requires multi-vendor cascading. Standard pipeline metric
Fill Rate (Completeness) = Σ populated_fields / (expected_fields × resolved_entities)
Measures depth. A matched record with only a name and domain is functionally useless. DataFlirt schema validation
Data Decay Rate = 1 − e−λt
B2B data decays at ~3% per month. Headcount and tech stack decay fastest. B2B data lifecycle model
// 04 — api trace

Hydrating a domain
in 400 milliseconds.

A live trace of a synchronous enrichment request hitting the DataFlirt enrichment gateway. The input is a bare domain; the output is a fully resolved firmographic entity.

REST APIJSONEntity Resolution
edge.dataflirt.io — live
CAPTURED
// POST /v1/enrich/company
payload: { "domain": "dataflirt.com" }

// 1. entity resolution phase
resolve.domain_status: active "200 OK"
resolve.canonical_id: "ent_8f92a1b"
resolve.confidence: 0.99

// 2. hydration phase (parallel fetch)
fetch.firmographics: cache hit // age: 12 hours
fetch.tech_stack: cache miss // triggering live micro-scrape
micro_scrape.status: success "detected: Astro, React, Cloudflare"

// 3. response payload assembly
company.name: "DataFlirt"
company.industry: "Data Infrastructure"
company.headcount_range: "11-50"
company.founded_year: 2023
company.location: "Bengaluru, Karnataka, IN"
tech.frameworks: ["Astro", "React"]

// metrics
latency_ms: 412 fill_rate: 0.94 status: 200 OK
// 05 — failure modes

Where enrichment
falls short.

Enrichment APIs are probabilistic systems. Ranked by frequency, these are the primary reasons an enrichment request fails to deliver actionable business value.

SAMPLE SIZE ·  ·  ·  ·    10M+ API calls
TARGET TIER ·  ·  ·  ·    Global B2B
UPDATED ·  ·  ·  ·  ·  ·  2026-05-19
01

Entity resolution mismatch

Wrong company · Mapping 'Apple' to Apple Records instead of Apple Inc.
02

Stale firmographics

Decayed data · Returning 2022 headcount for a company that doubled in size.
03

Domain parking / redirects

Dead ends · Holding company domains that don't reflect operating reality.
04

Sparse fill rate

Empty fields · Matched the domain, but returned null for revenue and tech stack.
05

API rate limiting

Infrastructure · Synchronous bottlenecks when processing bulk CRM uploads.
// 06 — our architecture

Resolve the entity,

then hydrate the fields.

DataFlirt's enrichment pipeline doesn't rely on a single static database. We use a cascading resolution engine. When a domain is submitted, we first resolve the canonical entity ID, then hydrate the profile by querying live web signals, historical scrape archives, and partner registries in parallel. If the data is older than 30 days, we trigger an asynchronous micro-scrape to verify the tech stack and employee count before returning the payload. Freshness is a feature, not a nice-to-have.

Enrichment Job Trace

Live telemetry from a bulk enrichment job processing 50,000 domains.

job.id enrich-batch-092
input.records 50,000 domains
match_rate 84.2%above baseline
fill_rate.revenue 61.4%sparse
fill_rate.tech_stack 92.8%dense
live_scrapes_fired 4,120cache miss fallback
pipeline.status completed · 14m 22s

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// 07 — FAQ

Common
questions.

Common questions about enrichment accuracy, entity resolution, data decay, and how DataFlirt integrates enrichment into broader scraping pipelines.

Ask us directly →
What is the difference between web scraping and data enrichment? +
Web scraping extracts raw data from specific URLs. Data enrichment takes an identifier (like a domain) and returns a structured profile by aggregating data from multiple underlying sources — which often includes previously scraped data. Scraping is the collection mechanism; enrichment is the structured delivery mechanism.
How do you handle holding companies versus subsidiaries? +
This is the core challenge of entity resolution. A good enrichment API maintains an entity graph that maps parent-child relationships. If you query a subsidiary domain, the API should return the subsidiary's specific headcount and location, while explicitly linking to the parent company's canonical ID.
Is company enrichment data subject to GDPR or CCPA? +
Firmographic data (company name, revenue, HQ address) is generally not considered personal data and falls outside GDPR/CCPA scope. However, if the enrichment API also returns contact data for specific employees (names, direct emails, LinkedIn profiles), that portion of the payload is strictly regulated and requires compliance frameworks.
Why is the match rate so low for local SMBs? +
SMBs often lack a robust digital footprint. They might use a Gmail address instead of a custom domain, have no LinkedIn company page, and lack public revenue filings. Enrichment APIs rely on digital exhaust; where there is no exhaust, there is no data. Match rates for enterprise targets are typically >95%, while local SMBs often hover around 30-40%.
How does DataFlirt ensure data freshness? +
We use a time-to-live (TTL) threshold on our entity cache. If a requested profile hasn't been updated in the last 30 days, our gateway triggers a targeted, asynchronous micro-scrape of the target's corporate site and public registries to refresh the tech stack and headcount before serving the final payload.
Can I enrich a company using just its name instead of a domain? +
Yes, but the confidence score drops significantly. "Acme" could refer to 500 different registered businesses globally. When enriching by name, you must provide secondary signals — like country, city, or industry — to allow the entity resolution engine to disambiguate the target accurately.
$ dataflirt scope --new-project --target=company-enrichment-api READY

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hello@dataflirt.com  ·  Bengaluru  ·  IST  ·  typical reply < 4h
// 08 — keep reading

Related glossary terms

Data Cleaning

Entity Resolution

The algorithmic process of proving that two different records refer to the exact same real-world business.

Read →
Data Business

Data Enrichment API

The broader category of APIs that append missing attributes to incomplete records.

Read →
Data Business

Alternative Data

Non-traditional data sources, like web scrapes and tech stack signals, used to evaluate company performance.

Read →
Web Scraping

Data Freshness

Why stale firmographics are a liability, and how pipeline cadence dictates data value.

Read →