Open web, court index, registers · on approval

Seller Background Check API

Yes. The LandLens seller background check researches the person, firm or company selling a property across four sources: the open web (enforcement by ED, CBI and income tax, benami attachments, SEBI and RBI orders, bank recovery and auction notices, insolvency, wilful default, corporate standing, election affidavits, sanctions lists and serious press), the national court and tribunal index searched by name, our CMDA and real estate registers, and reports about the property's area. It follows the leads it finds (directors, group companies, aliases, case numbers). Every finding carries its source link, a date, a severity and how confident the match to this person is. The first call returns a quote; once approved the check runs for 8 to 15 minutes and costs 7 credits, returned if it fails.

Postman collection Try it in the browser

POSThttps://verified.realestate/api/v1/tools/seller_background

Price: 7 credits on approval
Source: Open web, court index and registers
Coverage: Persons and companies across India
Answers in: Quote at once; the check in 8 to 15 minutes

Example

Request

curl -X POST https://verified.realestate/api/v1/tools/seller_background \
  -H "Authorization: Bearer $LANDLENS_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name":"S Karthik","kind":"person","locality":"Sholinganallur, Chennai"}'

Responsereal answer

{
  "ok": true,
  "status": "proposal",
  "data": {
    "proposal": true,
    "action": "seller_check",
    "label": "Background check on S Karthik",
    "params": { "name": "S Karthik", "kind": "person", "locality": "Sholinganallur, Chennai" },
    "details": [
      { "k": "Subject", "v": "S Karthik" },
      { "k": "Type", "v": "Person" },
      { "k": "Property area", "v": "Sholinganallur, Chennai" },
      { "k": "Sources", "v": "Open web (three parallel passes), court and tribunal index, RERA and CMDA registers, the area, then the leads it finds" },
      { "k": "Time", "v": "8 to 15 minutes, posts into this chat" }
    ],
    "cost": 7,
    "source": "Seller background check",
    "note": "Check the name is spelt exactly as on the documents and add anything that tells this seller apart from a namesake. Charged 7 credits, taken when it starts and returned automatically if the check fails. ..."
  },
  "credits_quoted": 0,
  "credits_charged": 0,
  "next": { "tool": "approve_action", "arguments": { "action": "seller_check", "params": { "name": "S Karthik", "kind": "person", "locality": "Sholinganallur, Chennai" } } }
}

A real quote from the API, with intake, balance_after and request_id left out. It was not approved.

What it returns

The first call returns a quote, with status proposal. approve_action starts the check and answers queued with a check_id; poll seller_check_status about once a minute until it is done.

Field	What it holds
`check_id, status (approve_action)`	The check started, its id, and a poll block for seller_check_status.
`status, stage (seller_check_status)`	queued, running, done or failed, and while it runs the stage it is on.
`result.verdict, summary`	A short verdict and the written summary of what was found.
`result.findings[]`	The findings, most severe first: headline, severity, category, date, source_url, source_domain, source_tier, identity_confidence and how it was reached (via_lead).
`result.findings_count, high_count`	How many findings in all, and how many are high severity.
`result.court`	The court search: status, pending matters that look like this person, how many hits were set aside as namesakes or out of area, and the open matters.
`result.leads`	The leads the check followed: directors, group companies, aliases, case numbers.
`link, credits_charged`	The report page in LandLens, and what the check cost.

Inputs

The JSON body of the POST is the arguments. Only name is required, but every identity detail you add makes the court triage sharper.

Argument	Type	What to send
`name`required	string	The seller's name exactly as it appears on the deed, agreement or patta.
`kind`	string: auto, person, company	"person", "company" or "auto".
`locality`	string	Where the property is. Used to tell namesakes apart and to research the area, never to narrow the search.
`note`	string	Only a fact about the seller that tells them apart: a trade, a company they run, a relation to a previous owner.
`relative_name`	string	The spouse's or father's name as the deed writes it. Court records naming a different relative are set aside.
`relation`	string: husband, wife, father	Who relative_name is: husband, wife or father.
`address`	string	The seller's own address, not the property's.
`age`	integer	The seller's age, if the deed states it.

Code

Set LANDLENS_KEY to your key. In an AI app, connect https://verified.realestate/mcp once and ask in plain words instead. In Postman, download this API's collection, import it (File, Import) and set apiKey.

curl -X POST https://verified.realestate/api/v1/tools/seller_background \
  -H "Authorization: Bearer $LANDLENS_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name":"S Karthik","kind":"person","locality":"Sholinganallur, Chennai"}'

import os, requests

r = requests.post(
    "https://verified.realestate/api/v1/tools/seller_background",
    headers={"Authorization": f"Bearer {os.environ['LANDLENS_KEY']}"},
    json={
        "name": "S Karthik",
        "kind": "person",
        "locality": "Sholinganallur, Chennai"
    },
    timeout=60,
)
body = r.json()
print(body["status"], body["credits_charged"], body["data"])

const res = await fetch("https://verified.realestate/api/v1/tools/seller_background", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.LANDLENS_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "name": "S Karthik",
    "kind": "person",
    "locality": "Sholinganallur, Chennai"
  }),
});
const body = await res.json();
console.log(body.status, body.credits_charged, body.data);

Run a background check on the seller, S Karthik, son of R Subramanian, who is selling a plot in Sholinganallur. Any court cases or enforcement action?

Your AI shows the quote with the details it will search on, and starts the check only after you say yes.

Responses

Every answer has the same envelope. What status can say for this call:

status	What it means
`proposal`	The quote. Nothing is charged. Approve it by sending next.arguments to approve_action within 30 minutes, unchanged.
`queued`	From approve_action: the check is running, with check_id and a poll block for seller_check_status.
`ok`	From seller_check_status: the check's state, and its result once done.
`not_found`	At the quote: no usable name. From seller_check_status: no check with that id on this account.
`insufficient_credits`	HTTP 402 at approval, with the credits needed, your balance and where to top up.

Where the data comes from

Public sources on the open web, the national court and tribunal index (including debt recovery tribunals and consumer commissions), the CMDA and real estate registers LandLens holds, and reports on the property's area, read and weighed by an AI research pass.

Coverage

Any person, firm, LLP, company or trust in India. Court results outside the property's state and district are set aside as likely namesakes and counted.

How current

Live. Each check reads its sources when it runs.

How we read it

Three parallel passes over the open web, the court index by name, the registers and the area, then the leads they surface. Court hits are triaged against the identity you give (a spouse's or father's name, an address, an age), so a case recorded against a namesake is set aside rather than counted.

Who uses it

Buyers

Know who you are paying an advance to: enforcement action, recovery notices or suits against the seller show up before the money moves.

Banks and NBFCs

Screen the vendor in a home loan, or a builder in a project loan, with every finding sourced.

Law firms

Add a sourced counterparty check to a title opinion, with namesakes set aside and counted.

Limits and good practice

A name is not a person. Findings carry an identity confidence; read it before treating one as your seller's.
Public sources only. A matter that was never published, or a case not on the court index, will not be found.
The summary and the weighing of findings are written by an AI from the sources found. The source links are the evidence.
Runs 8 to 15 minutes. Poll seller_check_status; do not approve the quote again.

Pricing

7 credits on approval

A check costs 7 credits once approved, taken when it starts and returned automatically if it fails. The quote and seller_check_status are free. Credits come from the same LandLens wallet as the chat and the minis. How billing works.

Calling at volume?

For thousands of calls a month, a portfolio sweep or a platform built on these registers, we quote a customised bulk price, with invoicing to match.

WhatsApp 99961 29996

Questions

Is there an API for a background check on a property seller in India?

Yes. LandLens runs a sourced background check on a person or company across the open web, the national court and tribunal index and the property registers, after you approve a quote, and returns the findings as JSON.

How long does it take?

8 to 15 minutes after approval. Poll seller_check_status with the check_id about once a minute.

How much does it cost?

It costs 7 credits, taken when it starts and returned if the check fails.

How does it handle common names?

Court hits are triaged against the identity you give and the property's area, and a likely namesake is set aside and counted rather than reported. Give the father's or spouse's name for the sharpest result.

Can I check a builder or a company?

Yes. Set kind to company; the check follows directors and group companies it finds.

Updated 22 Oct 2026 · OpenAPI description