Harvin Data Building

Find the companies and decision-makers that fit your business best.

Harvin learns what you sell, who converts and who doesn’t, then discovers, researches and enriches a market around that context.

How it works
B2B profiles
650M+
B2B profiles
contact-data sources
30+
contact-data sources
signal types
20+
signal types

Your business

What you sellCustomersLost dealsBuyer roles

Harvin

Building market
  1. Understand
  2. Discover
  3. Research
  4. Match

Sales-ready market

1,284 companies

High-fit companies

Company Fit

Right decision-makers

CFO · VP Finance

Verified contacts

Email + phone

Signals

20+ signal types

The problem

A database can give you millions of records. It still needs you to know exactly what to search for.

1Company search

Industry: SoftwareEmployees: 200–2,000Revenue: $10M–$100MIndiaSeries B+

Relevant companies can still be missed when they:

  • Sit under a broad or wrong category
  • Describe what they do differently
  • Are missing revenue or funding data
  • Need research before fit is clear

2Decision-maker search

Title contains “Finance”2,418 results

Every result matches the keyword. Few are the buyer.

  • Finance ManagerToo junior
  • VP Finance TransformationAdjacent role
  • Finance Operations LeadWrong buying role
  • VP FinanceLikely buyer

More records don’t automatically mean better targeting.

How Harvin works

Harvin adds business context before it adds more data.

It doesn’t start with which filters you want. It starts with what a good customer looks like for you.

  1. 01

    Understand

    Your business

    • What you sell
    • Who buys
    • Who converted
    • Who didn’t
  2. 02

    Build

    Your market

    • Search themes
    • 650M+ B2B profiles
    • Company discovery
    • Company research
  3. 03

    Match

    Fit and decision-makers

    • Company Fit
    • Target roles
    • Seniority
    • Function
  4. 04

    Act

    Contacts and timing

    • Verified email
    • Direct/mobile phone
    • Signals

Company Profile

Teach Harvin your business once.

Harvin uses this context every time it discovers, researches or scores a company.

  • Tell Harvin

    What you sell, buyers, markets

  • Upload

    Converted, lost, customers

  • Set targeting

    Titles, functions, seniority

Your team doesn’t rebuild targeting logic every time it creates a list.

Company Profile

Saved

Your business

What does your company do?
B2B payments for global companies
What do you sell?
Cross-border payment infrastructure
Who do you sell to?
Finance teams at global businesses

Your customers

Companies that converted

TripwiseOrbit LearnNomad Stays

Companies that didn’t convert

CornerMartCityClinic

Existing customers

customers.csv

Target Profile

Target job titles

CFOVP FinanceHead of Payments

Buyer functions

FinanceTreasuryPayments

Seniority

Head and above

Your market

Geographies

IndiaSoutheast AsiaUAE

Exclusions

CompetitorsDNCOpen deals

Company discovery

Harvin turns your business context into the language your market actually uses.

What you sell

Cross-border payment infrastructure

Use case

Global payouts · International collections

Business model

Marketplace payments · Overseas subscriptions

Customer type

Cross-border sellers · Global employers

Product language

FX workflows · Global payroll

650M+B2B profiles

Companies found

  • Northstar SoftwareOff-category
  • Vela Commerce
  • Brightpath Learning
  • Atlas Digital
  • Kitecart

30%

A relevant company doesn’t always sit in the category you expect. 30% of approved companies are discovered outside the original filter set.

Illustrative example

A company can look wrong in the database, and still be exactly right for you.

Database view

Northstar Software

Industry
Software
Employees
350
Location
India
Revenue
Unknown

Nothing here says “cross-border payments prospect.”

Harvin research

  • Subscription business
  • Customers in 18 countries
  • Recurring foreign-currency payments
  • Uses Stripe and AWS
  • 300K monthly active users

Harvin looks past the category label to understand the business.

Company Fit

91/ 100

Strong fit

Matches patterns from companies that converted

Two ways to build

Build your list from scratch, or improve the one you already have.

A

Create from scratch

Starts from your saved Company Profile

  1. 1Company Profile
  2. 2Find companies
  3. 3Research
  4. 4Company Fit
  5. 5Decision-makers
B

Enrich existing data

Upload a CSV or XLSX of companies, leads or both

  1. 1Upload
  2. 2Normalize
  3. 3Fill gaps
  4. 4Company Fit
  5. 5Missing decision-makers

Automatic normalization

ACME TECHNOLOGIES PVT LTDAcme Technologies

SARAHSarah

Clean data before the SDR sees it.

Sales-ready dataResearched companiesCompany FitDecision-makersVerified email and phone

Premium company research

Add the company data that actually decides fit.

Harvin can research and refresh the information your targeting depends on, instead of relying only on what’s already in the company record.

  • Standard company data
  • Researched by Harvin

Illustrative values. Field availability varies by company.

V

Vela Commerce

Illustrative company record

88 · Strong fit

Industry

Internet

Company size

600

HQ

Singapore

Category

Online marketplace

Revenue

$41M

Funding

Series C

Monthly active users

1.2M

Location count

17 locations

Technologies

Stripe, Shopify, AWS

Enrich Column

Need another field? Add a column and let Harvin research it.

Add the information you need without rebuilding the list. Choose a field and Harvin researches missing values across the selected companies.

List · Cross-border payments

5 companies selected5/5 researched
CompanyCategoryRevenueFundingMonthly active usersEnrich column
Vela CommerceOnline marketplace$41MSeries C1.2M
Brightpath LearningEdTech$14MSeries A420K
KitecartSocial commerce$9MSeed310K
Lumora HealthDigital health$22MSeries B95K
Atlas DigitalIT services$12MUnknown18K

Choose a field

  • Revenue
  • Funding
  • Category
  • Monthly active users
  • Location count
  • Technologies

More research fields

Decision-maker matching

Finding the right company is only half the job.

Finding the right person is where title searches get noisy.

Keyword title search

Without Harvin
Title contains “Finance”
  • Finance ManagerToo junior
  • VP Finance TransformationAdjacent role
  • Finance Operations LeadNot the buyer
  • Head of Commercial FinanceAdjacent role
  • VP Strategic FinancePossible match

Harvin · Vela Commerce

Target Profile

Searches your roles in priority order

  1. 1CFOMatched
  2. 2VP FinanceMatched
  3. 3Head of FinanceNo match
  4. 4Head of PaymentsMatched
  5. 5Treasury DirectorNo match

Matches on title, seniority and function, in your priority order.

40% fewer irrelevant decision-maker matches. Less manual title checking.

Email and phone

One person. One verified result.

Harvin runs separate email and phone waterfalls and stops each one at the first qualifying result, so your team doesn’t jump between contact-data tools.

Decision-maker

PN

Priya Nair

Head of Payments

Vela Commerce

Email waterfall

  1. Source ANo match
  2. Source BNo match
  3. Source C · MatchStops here
  4. More sourcesNot needed

Verified work email

priya@velacommerce.co

Phone waterfall

  1. Source ANo match
  2. Source B · MatchStops here
  3. Source CNot needed
  4. More sourcesNot needed

Direct/mobile phone

+65 9XXX X412

30+ sources across Harvin’s contact-data waterfall

Signals

Find the right companies. Keep watching for the right moment.

Company Fit

Should we sell to them?

88

Strong fit

Vela Commerce

A high-fit company may not have a signal today.

Signals

Is there a reason to act now?

20+

signal types monitored

  • Leadership change

    New VP Finance joined

    Vela Commerce · 4 days ago

    New

  • Expansion

    Opened operations in UAE

    Vela Commerce · 11 days ago

A strong signal doesn’t make a poor-fit company a good target.

Where Harvin removes work

Less manual work. Faster to a sales-ready market.

80%

less time building and researching lists

3×

faster from targeting brief to sales-ready data

40%

fewer irrelevant decision-maker matches

30%

of approved companies discovered outside the original filter set

Discovery

Manual filter experimentation

Search themes Harvin generates

Research

Opening websites and data tools

Structured company research

People

Checking job titles by hand

Matching to your Target Profile

Contact data

Switching between providers

One email and phone waterfall

Monitoring

Revisiting accounts manually

Signal monitoring

Start with one market

Give Harvin one market.

Share your targeting criteria, your best customers, or the spreadsheet your SDR team is working today. We’ll show you what Harvin finds.

  • Your targeting criteria
  • Your best customers
  • Your current spreadsheet