How a Litigation Check API Works

Explainer · Litigation Data

Updated for 2026 · Claw Editorial

Any team that has tried to check whether a vendor, borrower, or employee has pending litigation knows the manual version of this job well: open a court website, search a name, wait, get five possible matches with no way to tell which one is real, and repeat this for every court that entity could plausibly be involved in. A litigation check API is meant to remove that manual work by letting your own system send a name or identifier and get structured, matched litigation data back automatically. This explainer covers how that actually works, from the request you send to the response you get, so you know what to look for when evaluating one.

01Why teams reach for a litigation check API

A litigation check, in the compliance and risk sense, means finding out whether a person or company is named as a party in any pending or past court case. Teams need this for onboarding vendors, screening borrowers, verifying employees, and periodically re-checking existing relationships. Doing it by hand does not scale, for three reasons.

Court data is scattered

India does not have one single portal that reliably covers every court and tribunal. A thorough manual check means searching multiple court websites separately, each with its own search interface, and hoping none of them are down or slow that day.

The volume breaks manual workflows

Checking one name by hand takes a few minutes. Checking five hundred vendors, or re-screening your entire borrower book every quarter, is not something a person can do reliably at that scale. This is the exact problem an API is built to solve: it turns a manual lookup into a programmatic call your own system can run in bulk.

Manual results are hard to trust

Common Indian names return many results on a court search. Without a way to tell which "Ramesh Kumar" is actually your vendor, a manual search either wastes time chasing false leads or, worse, misses a real match because it looked like noise.

02How a litigation check API works, step by step

Strip away the marketing and a litigation check API is a fairly simple request and response cycle, repeated at whatever scale you need. It has four moving parts.

1. You send an identifier. Your system sends a request containing what you know about the entity: usually a name, and ideally a stronger identifier like a PAN, CIN, LLPIN, or an existing case number if you already have one.

2. The provider searches its underlying database. Behind the API sits a database of court records the provider has already ingested and indexed. The API itself does not go and search a court website live; it queries data that has already been collected and structured.

3. A matching layer decides what counts as a hit. This is the step that separates a useful API from a noisy one. Raw name search returns everything that looks similar. A matching layer has to work out which of those results are actually the entity you asked about, given spelling variations, transliteration differences, and common names.

4. You get back structured data, not a webpage. Instead of a page you would have had to read and interpret yourself, the response is structured data: fields like case status, court, filing date, and party names, that your own system can store, flag, or act on automatically.

A litigation check API does not remove the hard part of court search. It moves the hard part, matching the right person to the right case, into a system that can do it consistently and at scale.

03The building blocks under the hood

Every litigation check API is built from the same few components. Knowing them helps you ask better questions when you are evaluating a vendor.

Inputs and identifiers

The query almost always starts with a name. A well-built API also accepts stronger identifiers, PAN or CIN for companies, or a known case number, because those cut down false matches sharply compared to a name alone. The more specific the identifier you can send, the less matching ambiguity there is on the other end.

Entity matching and name resolution

This is the part that decides whether "R. Kumar", "Ramesh Kumar", and "Ramesh K." in three different case filings are the same person. Good matching handles spelling variations, initials, transliteration, and common Indian name patterns, and it is genuinely one of the harder problems in this space. For a deeper look at exactly why this goes wrong and what to check for, see our explainer on entity matching and name collision in litigation search.

Court and data coverage

Coverage varies a lot between providers: some cover only High Courts, some add the Supreme Court, and some extend to tribunals and district courts. Coverage is also a question of history, how far back the records go, not just which courts are included. This is one of the first things to confirm before you rely on a "clean" result meaning no litigation actually exists.

The response shape

A useful response is structured, typically including case status (pending, disposed, or similar), the court and bench, filing date, party names as recorded, and a citation or reference you can trace back to the source. The response should let you distinguish an active case from a closed one, since a closed matter is a very different risk signal than a pending one.

Delivery model: a single call, or ongoing monitoring

Some checks are a single request and response, useful at the moment you onboard someone. Others need to run repeatedly, for example re-checking your whole vendor list every month to catch a new case that was filed after onboarding. That second pattern usually relies on scheduled or webhook-based updates rather than you polling the API yourself every time.

04How teams actually integrate it

In practice, most teams plug a litigation check API into one of two moments.

At onboarding. When a new vendor, borrower, or employee is being brought on, the check runs as part of that workflow, and the result feeds into whatever approval or risk-scoring process already exists. The point is that no one has to remember to run a manual search; it happens automatically as a step in the process.

As ongoing monitoring. A clean check at onboarding does not stay true forever; a case can be filed against a vendor two years into the relationship. Teams that take this seriously re-run checks periodically, or use update alerts, so a new case surfaces without anyone having to re-search manually.

Either way, the API result usually lands inside a system your team already uses, an internal risk dashboard, a KYC tool, or a case management system, rather than being read as a standalone report each time. That is the real value: it removes the manual, repetitive part of the job and leaves your team to review only the matches that actually need a human decision.

05Why matching quality matters more than speed

It is tempting to judge a litigation check API on how fast it responds. Speed matters for user experience, but it is not the number that determines whether the check is actually useful. Two failure modes matter far more.

A false negative, where a real case exists but the API does not surface it, is the expensive one. It gives your team false confidence that an entity is clean when it is not, which is exactly the risk a litigation check was meant to catch in the first place.

A false positive, where the API flags someone else’s case against a person with a similar name, wastes review time and can unfairly delay onboarding for the wrong person.

Both failure modes trace back to the same thing: matching quality and data coverage, not response speed. Before you take a vendor’s coverage or accuracy claim at face value, it is worth knowing how to test it properly. See our guide on how to evaluate vendor litigation data claims for the specific questions to ask. If you are comparing options for this category more broadly, our roundup of the best court record check APIs in India walks through how the main providers stack up.

06Where Claw fits

Claw is an all-in-one legaltech platform for Indian advocates, law firms, and corporate legal teams, combining AI-based case search, an AI legal assistant (Legal GPT), case management, and compliance automation across all Indian courts and tribunals.

Claw has a live public and partner API for programmatic access to court and litigation data, so the mechanics described above, sending an identifier, getting matched case data back, are the same shape a team would use to pull Claw’s case data into its own onboarding or risk workflow. Claw’s underlying search covers 25 High Courts (1980 to 2026) and the Supreme Court (1950 to 2026), across more than 1.5 billion case records and 30 crore judgements, with results typically returned in under 5 seconds and name-tolerant matching built to handle spelling and phonetic variations in Indian names. Teams that want litigation data alongside case tracking and AI-based case search in one place, rather than stitching together separate tools, are the ones for whom this fits best.

07Sources and further reading

References for this explainer:

• Claw: clawlaw.in
• eCourts (National Judicial Data Grid / official court services): ecourts.gov.in

This explainer describes how litigation check APIs work as a category. It is not a guide to any single vendor’s specific endpoints, authentication method, or pricing.

08Frequently asked questions