VIPRO Annotation is a managed data annotation company. We turn messy source files into training labels a model team can inspect, argue with, and trust — because every decision stays attached to the example that produced it.
VIPRO Annotation is not Wipro and is not VIPRO Inc.
Clarity before volume. Evidence before assumptions. People throughout.
Our storyMost annotation projects fail the same way. The first week looks productive — then the model team finds three readings of the same object and a guide that never mentioned the hard case.
VIPRO starts with the opposite order: agree the decision, calibrate on the scenes that will argue, and only then scale the work.
Give AI teams a dependable path from raw data to structured training signals — without sacrificing the nuance that makes labels useful.
Build annotation systems where context, questions, and review stay visible — because the best models are trained on the most carefully considered data.
The studio is run by people who still sit with the first contested file. Process, review, and how the work is explained to a client are not separate jobs from the labels themselves.
How a project runs
Scale is useful only after the hard cases have names. Every VIPRO project follows the same order, whether the source is a street scene, a broadcast clip, a claims packet, or a call recording.
We start with the decision you want the model to make, not with a catalogue of tools. Detection, tracking, field extraction, speaker turns, and event timing each imply a different geometry and a different review bar. If that task is still fuzzy, we help you write it down before anyone opens a file.
A pretty sample set is a trap. We ask for the rain, the blur, the overlapping speech, the handwritten margin, the class that only appears once in a hundred files. Those items become the calibration batch, because that is where two readers will disagree.
Annotators and reviewers label the same items, compare, and write the missing rule. Your team sees the contested cases, not a dashboard of green ticks. When the guide stops changing on every review, the project is allowed to grow.
Production work follows the locked guide. Automated checks catch empty fields, illegal classes, broken geometry, and missing timestamps. Reviewers sample and inspect the rest. Only accepted items enter the export your engineers named at the start.
Who this is for
You have an ontology, or the start of one, and you need a partner who will pressure-test it on real files instead of translating a slide into ten thousand rushed boxes.
The work lives in a factory, a clinic, a warehouse, a roadside, or a support queue. Domain language matters, and a generic class list will not survive the first week.
You need a dataset you can show an engineer, a reviewer, and later an auditor — not a zip file whose decisions vanished into chat.
Confidentiality is part of the process, not an afterthought. This is what we put in place before your files arrive — and what you can hold us to afterwards.
We sign your NDA, or share ours, before a single file changes hands. Confidentiality terms are agreed in writing — scope, parties, duration — before the work begins.
Your data is worked on only by the annotators and reviewers assigned to your engagement, under the transfer method and access restrictions agreed for that project.
Files are used solely to produce your annotations. They are never reused, published, or applied to work outside your engagement.
Where files are held, how long they are kept, and when they are removed are all agreed per project. When the engagement ends, the data goes with it.
Need confidentiality terms in place first? Tick the NDA box in the contact form and we will send the agreement before anything else.
Discuss an NDAStart a conversation
Bring the files that will argue back. We will tell you whether the task is ready to label, or what still needs a written rule.
