A phone number is enough to know what to offer.
Demographics, profession, income and financial health, resolved from a single identifier — so a cold list gets ranked before anyone dials, and the offer fits the person receiving it.
Who already runs on it.
Consumer businesses using ZipLabs to decide who to call first, what to offer, and when.
A name and a number is not a customer.
Four layers on one person, from a single identifier. Demographics and profession are global; financial health is India.
Who they are
Age, gender, location and household context — the base every other layer hangs off.
What they do
Employer, title, seniority and education, resolved against the same 1B+ person graph the Build line runs on.
What they can afford
Income band, credit health and existing obligations — the layer that separates a lead worth a senior rep from one worth an email.
What just changed
Promotions, job changes, relocation and life events — the moment that turns a dormant record into a reason to call.
Take the profile, or the profile plus the finances.
Demographics are always included. The choice is whether financial health comes with them.
Demographic + professional
Who the person is and what they do. Enough to segment, personalise and route — and the only package that runs everywhere we hold people.
- RoleSenior Product Manager
- Experience6 years
- EducationMBA
- Recent changeRelocated, 4 mo ago
Demographic + professional + credit
The same profile with income band, credit health and obligations on top. For lending, insurance and wealth, where the offer depends on the answer.
- RoleSenior Product Manager
- Experience6 years
- EducationMBA
- Recent changeRelocated, 4 mo ago
- Income band₹35–50L/yr
- Credit bracket800–900
- Obligations3 cards · 1 active loan
One request. One person back.
What you send and the shape of what returns — with the block the credit package adds marked, so the difference between the two is a thing you can see rather than a sentence you have to trust.
Send01
POST /enrichsvc/job/create
{
"inputs": [
{ "phone": "9876543210" }
]
}
Get back02
{
"identity": {
"name": "Priya Raghavan",
"age": "32",
"gender": "female",
"location": { "city": "Bengaluru", … }
},
"professional": {
"headline": "Senior Product Manager",
"experience": [ role, company, dates ],
"education": [ school, degree, dates ],
"total_experience": "6 years"
},
"career_signal": {
"signal": "relocation",
"confidence": "high",
"current": { "company": "Northwind", … },
"previous": { "company": "Vertex Labs" }
},
"compensation": {
"bucket": "35L-50L",
"currency": "INR"
},
── added by the credit package · India ──
"financial_health": {
"credit": { "score_bracket": "800-900" },
"cards": { "active": 3 },
"loans": { "active": 1, "secured": 1 },
"home_loan": { "active": true },
"payments": { "monthly": "low" },
"defaults": { "active": false }
},
"identity_checks": { "validated": true }
}
One identifier, three steps.
The same path whether it is one lead at capture or a list of four hundred thousand.
Send what you already have
A phone number or an email. Nothing else is required, and hashed identifiers work the same way.
We resolve it to a person
The identifier is matched into the graph and the fragments behind it — social, professional, financial — are joined into one record.
The profile comes back
Up to 150+ attributes, confidence-scored. Re-run on a cadence and the changes come with it.
Value at every stage, from one graph.
Acquire cheaper, close faster, engage at the right moment, then keep and grow. The same profile does all four.
-
Acquire
Target on who people actually are, and drop the junk before a rep is paid to dial it.
CAC↓
-
Close
Rank the list by income band and credit health, so the best closer gets the best lead.
Closure time↓
-
Engage
A promotion, a move, a marriage — the campaign fires on the event, not the calendar.
Conversion↑
-
Retain and grow
Your own base keeps changing — income, job, life stage. You see it, and act on it.
LTV↑
Three ways in, and none of them need a data team.
Same profile through every route. The difference is who has to do the work.
Enrichment API
One call at the moment of capture, so a lead is a person before it reaches a rep. Webhooks push the changes afterwards.
Send us the list
Share the file, we enrich it and hand it back. No integration, no engineering ticket — the fastest way to see what your own base looks like.
Inside the tool you already use
Zoho CRM and TeleCRM, enriching leads as they arrive or on a manual trigger. Nobody has to leave the CRM.
Enrich the list without exposing it.
Matching runs on hashed identifiers, so a customer list can be enriched without the raw contact details leaving your side. The graph is ours and owned rather than resold, which is why we can say where a field came from.
Security & Trust Centre
EU & UK
California
Audited
Consumer questions,
answered
What do we send, and what comes back?
A phone number or an email address. Back comes one person record — demographics, profession and education, life-stage signals, and financial health where we hold it. Up to 150+ attributes, each confidence-scored rather than asserted flat.
Where does the credit and income data apply?
India. Demographics and professional data run globally on the same 1B+ person graph, but income band, credit health and obligations are India coverage today. If your base sits outside India, the demographic and professional package is the one that applies.
Do we have to send you our customers in the clear?
No. Matching works on hashed identifiers, so a list can be enriched without the raw phone numbers or emails leaving your side. It is the same match either way.
We have no engineering time. Can we still use it?
Yes, two ways. Send us the list and we enrich it offline and hand it back, or turn on the CRM integration and let it enrich leads where your team already works. Neither needs a developer.
How current is a profile?
People change jobs, move and change income, so a profile is a snapshot unless you keep it fresh. Re-enrich on a cadence, or take the changes as they happen over a webhook — over a million observed changes a month is what makes the moment-based use cases work at all.
Is this resold from another provider?
No. The graph is ours, curated from 25+ consumer data sources and reconciled into one record rather than passed through. That is why a field can be traced to where it came from and scored for confidence.
See the real match rate on your own base.
Send a sample — hashed if you prefer — and we will show you what comes back before anything is signed.