Data annotation company: Training data. Made dependable.

VIPRO turns complex image, video, text, and audio into carefully reviewed data your AI team can use with confidence.

Answer first

VIPRO at a glance

VIPRO Annotation is a managed data annotation company that prepares AI training data for computer vision, machine learning and robotics teams. We label images, video, egocentric (first-person) footage, LiDAR and 3D point clouds, text and audio. Every project starts with a calibration pilot on your own data, runs on written labelling rules and passes two-stage QA: an annotator self-check, then an independent reviewer audit. Work runs under NDA with confidential data handling, and you can reach the team at hello@viproannotation.com.

New to the topic? Read what data annotation is, with types and examples.

Compatible workflows

Tools we can work with

Roboflow
SuperAnnotate
Darkhorse
Roboflow
SuperAnnotate
Darkhorse

Systems built for absolute precision.

01 / Calibrate

Calibrate before scale

02 / Visibility

Keep review visible

03 / Workflow

Deliver to your workflow

Precision in every label.

01 / Image Annotation

01 / 04 · Managed annotation

Make every pixel pull its weight.

Turn visual data into precise, model-ready labels.

Explore Image Annotation

02 / Video Annotation

02 / 04 · Managed annotation

When the story is in the movement, frame by frame matters.

Keep objects and events consistent from frame to frame.

Explore Video Annotation

03 / Text Annotation

03 / 04 · Managed annotation

Give your language data a clearer point of view.

Give language data consistent meaning and structure.

Explore Text Annotation

04 / Audio Annotation

04 / 04 · Managed annotation

Turn sound into signals your model can use.

Translate speech and sound into useful training signals.

Explore Audio Annotation

Calibration before volume

A representative sample exposes unclear classes, edge cases, and conflicting instructions before production expands.

You receive · Reviewed pilot batch

Decisions become guidance

Resolved questions are added to the working guide so annotators and reviewers apply the same interpretation.

You receive · Updated decision guide

Review stays traceable

Exceptions, corrections, and feedback remain connected to the affected work instead of disappearing into side conversations.

You receive · Visible review trail

Delivery is verifiable

Approved labels are checked against the agreed structure and packaged with the batch and version context your team needs.

You receive · Validated export package

Annotation operations

How raw data becomes reviewed output.

Four stages, from preparing inputs to versioned delivery. Select a stage to see what happens and what we check.

Files, metadata, existing labels, and project goals are checked before work enters a queue. Invalid or incomplete items are separated instead of silently passing downstream.
- File and metadata integrity
- Representative batch selection
- Ontology and output requirements
Output: Ready dataset
Annotators and reviewers work through the same representative examples. Disagreements become documented guidance, examples, and escalation rules before volume increases.
- Shared example review
- Edge-case decisions
- Guide and taxonomy alignment
Output: Locked guide
Schema and consistency checks catch preventable issues. Reviewers then inspect labels and exceptions, returning unclear work through a visible feedback loop.
- Schema and completeness
- Label and geometry consistency
- Exception and escalation review
Output: Accepted output
Accepted labels, source references, decisions, and agreed metadata are versioned in the required structure so the model team can trace what changed.
- Export validation
- Version and batch manifest
- Source-to-label traceability
Output: Versioned delivery

Industry context

Every domain needs its own annotation judgment.

Scroll through six industry-specific datasets and see how the annotation focus changes with the decision each model must make.

Explore industries

Automotive data with annotation overlays

Annotated dataset · 01

01 / 10

Automotive

Track road users, lanes, signs, and movement across complex scenes.

Data types: Image · Video
Annotation focus: Boxes, lanes, tracks

Explore Automotive

Annotated dataset · 02

02 / 10

Aerial

Label terrain, assets, and change across overhead and drone footage.

Data types: Image · Video
Annotation focus: Regions, objects, change

Explore Aerial

Agriculture data with annotation overlays

Annotated dataset · 03

03 / 10

Agriculture

Identify crops, conditions, growth stages, and field anomalies.

Data types: Image · Video
Annotation focus: Masks, classes, keypoints

Explore Agriculture

Annotated dataset · 04

04 / 10

Documents

Mark fields, tables, and reading order so a page becomes structured data.

Data types: Image · Text
Annotation focus: Fields, entities, layout

Explore Documents

Manufacturing data with annotation overlays

Annotated dataset · 05

05 / 10

Manufacturing

Spot defects, verify assembly, and classify production events.

Data types: Image · Video
Annotation focus: Defects, parts, events

Explore Manufacturing

Annotated dataset · 06

06 / 10

Medical

Structure approved clinical imagery, documents, and domain language.

Data types: Image · Text
Annotation focus: Regions, entities, classes

Explore Medical

Annotated dataset · 07

07 / 10

Sport

Follow players, ball movement, actions, and match events.

Data types: Video
Annotation focus: Tracks, poses, events

Explore Sport

Waste management data with annotation overlays

Annotated dataset · 08

08 / 10

Waste management

Separate materials, bins, and contamination so sorting models can act on what they see.

Data types: Image · Video
Annotation focus: Classes, masks, objects

Explore Waste management

LiDAR & 3D Annotation data with annotation overlays

Annotated dataset · 09

09 / 10

LiDAR & 3D Annotation

Turn point clouds and 3D scans into spatial labels that preserve geometry, depth, and object boundaries.

Data types: Point cloud · 3D mesh
Annotation focus: 3D cuboids, segmentation, landmarks

Explore LiDAR & 3D Annotation

Egocentric Video data with annotation overlays

Annotated dataset · 10

10 / 10

Egocentric Video

Label first-person footage from head-mounted and wearable cameras so models understand hands, objects, and actions from the wearer's view.

Data types: Video · Image
Annotation focus: Hand keypoints, objects, actions

Explore Egocentric Video

Run it small

We start with a pilot batch of your real data, so you can see the process and the labels before the work scales.

Agree the process

We settle how data is shared, who can access it, how questions are handled, and how completed work is delivered.

Scale what worked

Once the pilot output meets your standard, the same reviewed process carries the full workload.

Case Study

Case Study 1

ManufacturingVideo · Activity recognition

Factory Egocentric Tag Annotation

Egocentric video annotation for worker activity recognition and process optimization.

See how we tagged worker activity in factory video

Case Study 2

3D Data3D scanning · Point clouds · Facial landmarks

FacePoint AI — Facial Landmark Dataset

Turning captured facial geometry into a processed, annotated 3D dataset.

See how we built the FacePoint AI facial landmark dataset

Case Study 3

Urban PerceptionImage · Polygons · Bounding boxes

CityScope AI — Urban Perception Dataset

Turning complex urban environments into structured data for autonomous perception and 3D mapping.

See how we built the CityScope AI urban perception dataset

Have a challenge in mind?

Tell us what you're working on. Our team can help you identify the right approach, capabilities, and path to delivery.

Let's talk

013D Scan

“The final 3D scan output was detailed and well put together. Shreyash and Vinit were attentive to the requirements and easy to communicate with whenever a clarification was needed. Their team worked in a coordinated way, took feedback seriously, and stayed focused on quality throughout the project.”

Jatin PatelVendor

02CityScope AI — Urban Perception Dataset

“Working with Vinit and Shreyash on the CityScope AI urban perception dataset was a smooth experience. They understood the labeling requirements quickly and kept the annotations consistent across the dataset. I especially appreciated how well the team coordinated, resolved questions, and maintained a careful approach to the details.”

Gunjan PatelVendor

03Tag Annotation

“One thing that stood out during the tag annotation project was the team's willingness to be thorough rather than rush through the frames. Team were cooperative, consistent, and receptive to corrections. Their organized workflow and shared commitment to dependable annotations made collaboration easy.”

Karan KankariaVendor

02CityScope AI — Urban Perception Dataset

“Working with Vinit and Shreyash on the CityScope AI urban perception dataset was a smooth experience. They understood the labeling requirements quickly and kept the annotations consistent across the dataset. I especially appreciated how well the team coordinated, resolved questions, and maintained a careful approach to the details.”

Gunjan PatelVendor

Comparison

Why AI Teams Choose VIPRO

Stated VIPRO policies and quality targets, set beside general descriptions of other common annotation models.

How VIPRO managed calibration pods compare with crowdsourced platforms and traditional data-entry BPOs
Criterion	VIPRO Managed Calibration Pods	Crowdsourced Platforms (MTurk/Generic)	Traditional Data-Entry BPOs
Mandatory pilot calibration before scale	YesA free calibration pilot on your data before any volume work.	Not typicalUsually not required.	Not typicalUsually not required.
Post-calibration precision	YesTargets 98.5% or higher inter-annotator agreement (a measured target).	VariesVaries by task and worker.	VariesVaries, often sampled spot checks.
Edge-case rubric iteration speed	YesRubric updates are versioned and re-calibrated with the pod quickly.	Not typicalSlow. Instructions are hard to push to a changing crowd.	Not typicalSlow. Change requests go through account management.
Annotator turnover	YesDedicated pods with a zero-turnover policy.	Not typicalAnonymous and rotating workers.	Not typicalHigh attrition is common.
Native tooling	YesWorks natively in CVAT, Roboflow, SuperAnnotate and Labelbox.	VariesGeneric task interfaces.	VariesSpreadsheets or proprietary tools.
Security	YesStrict NDA and role-based access.	Not typicalOpen worker pool, limited control.	VariesStandard office policies, varies.

FAQ

Questions AI teams ask before the first batch.

Straight answers about pilots, security, quality, formats, and how changes are handled once production is running.

How much data do we need for a pilot?

A pilot usually needs a few hundred representative items—not your full dataset. We ask for a sample that includes your common cases and your hardest edge cases, so the calibration batch reflects the real work.

How do you keep our data secure?

Access is limited to the team assigned to your project, data is shared through agreed controlled channels, and completed work is delivered back to you. We settle retention, access, and handling rules with you before any data moves.

How is annotation quality measured?

Quality is agreed before production: accuracy targets, sampling rates, and acceptance criteria are written into the brief. Automated checks catch schema and consistency issues, and reviewers inspect samples of every batch against those criteria.

Can you work with our existing guidelines and ontology?

Yes. We start from your existing guide, test it on a calibration batch, and surface conflicts or gaps with documented examples before scaling. If you have no guide yet, we help build one from your model's decisions.

Which formats and tools do you deliver in?

Delivery is shaped around the schema your team already uses—common export formats for images, video, text, and audio, or a custom structure. The exact format is confirmed and validated before annotation begins.

How long does a typical project take?

A pilot is usually turned around in days to a couple of weeks depending on task complexity. Production timelines depend on volume and review depth, and a schedule is agreed once the pilot output meets your standard.

What happens when our requirements change mid-project?

Changes go through the same calibration loop: the guide is updated, a small batch is re-checked against the new rules, and only then does the revised process carry forward. Versioned deliveries keep earlier work traceable.

Do you use AI or automation in the annotation process?

Automation handles movement, validation, and consistency checks; human annotators and reviewers make the labeling decisions. Pre-labeling can speed up production where it fits, but every accepted label passes human review.

Start a conversation

Ready to make your training data easier to trust?

Start with the data you have.

Request a Quote