Annotation services / 01—04

Bring structure to the data your model depends on.

VIPRO runs four managed annotation services — image, video, text, and audio — each with its own geometry, timing rules, and review bar. The shared habit is the same: lock the decision, then scale the labels.

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Compatible workflows

Tools we can work with

Roboflow
SuperAnnotate
Darkhorse
Roboflow
SuperAnnotate
Darkhorse

Find your data type

Four services, four different failure modes

Each card below is a doorway, not the whole argument. The longer reading for that modality lives on the service page — including the techniques, the quality checks, and the questions that belong only there.

01 — Bounding boxes

Image Annotation

A box is enough when location matters more than silhouette. A polygon or mask is required when the model must follow an irregular edge. We write inclusion and occlusion rules before the first batch.

Bounding boxes
Polygons
Segmentation
Keypoints & OCR

Read the image annotation brief

02 — Object tracking

Video Annotation

Video is not a pile of images. The useful signal is continuity: the same person after a pillar, the same vehicle after a cut, and the moment an action starts and ends.

Object tracking
Frame-by-frame labels
Action & event labels
Temporal segmentation

Read the video annotation brief

03 — Named entities

Text Annotation

Language labels fail when they flatten a page into a string. Headers, stamps, and tables change what a number means, so we keep the source document in view.

Named entities
Intent classification
Sentiment & tone
Taxonomy & relevance

Read the text annotation brief

04 — Transcription

Audio Annotation

A transcript without times is a summary. Audio work ties words, speakers, and events to ranges on the recording, with written rules for overlap and noise.

Transcription
Speaker diarization
Intent & topic
Timestamped events

Read the audio annotation brief

Specialist service: egocentric video annotation for physical AI

Specialist service: LiDAR & 3D point cloud annotation

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.

How to choose

Pick the service that matches the decision, not the file extension.

Teams often send us a folder and ask us to 'label it'. The useful question is what the model must do after training. That answer chooses the modality and the technique.

If the model must…	Start here	What we settle first
The model must find or outline things in a still	Image annotation	Boxes, polygons, masks, keypoints, or scene classes — chosen by how much shape the task needs.
The model must follow something, or notice when it happens	Video annotation	Tracks, actions, and temporal segments. Frame-level labels only when time is not the point.
The model must read, classify, or extract language	Text annotation	Entities, intent, fields, and document structure. Layout stays attached when it changes meaning.
The model must hear, transcribe, or locate a sound	Audio annotation	Timestamps, speakers, events, and file-level classes. Overlap and uncertainty are first-class rules.

01 / 10

Automotive

Road scenes need labels that stay consistent when vehicles, people, and lanes overlap, disappear, and reappear.

Data · Image · VideoLabels · Boxes, lanes, tracks

02 / 10

Aerial

Overhead and satellite views train models to recognize objects in environments that change with altitude, light, and season.

Data · Image · VideoLabels · Regions, objects, change

03 / 10

Agriculture

Field imagery is labeled so a model can tell crop, soil, growth stage, and damage apart across a changing season.

Data · Image · VideoLabels · Masks, classes, keypoints

04 / 10

Documents

Pages are marked so fields, tables, and reading order survive the move from paper into structured data.

Data · Image · TextLabels · Fields, entities, layout

05 / 10

Manufacturing

Production images are labeled tightly enough to separate a good part, a defect, and the step where the fault appears.

Data · Image · VideoLabels · Defects, parts, events

06 / 10

Medical

Approved clinical images and documents are labeled so anatomy, findings, and fields stay tied to the source.

Data · Image · TextLabels · Regions, entities, classes

07 / 10

Sport

Match footage is labeled so players, the ball, and the action stay connected from one frame to the next.

Data · VideoLabels · Tracks, poses, events

08 / 10

Waste management

Sorting scenes are labeled so materials, bins, and contamination can be separated before a model acts on them.

Data · Image · VideoLabels · Classes, masks, objects

09 / 10

LiDAR & 3D Annotation

Spatial datasets need labels that follow the actual geometry of a scene, from sparse LiDAR returns to detailed surface scans.

Data · Point cloud · 3D meshLabels · 3D cuboids, segmentation, landmarks

10 / 10

Egocentric Video

First-person footage is labeled so hands, the objects they touch, and each step of an activity stay connected as the camera moves with the wearer.

Data · Video · ImageLabels · Hand keypoints, objects, actions

What you receive

A dataset you can open, not a black box of clicks.

A written guide version

The ontology, examples, and exception rules that the batch actually followed — dated, so later work does not silently drift.

Labels in your schema

COCO-style detections, masks, track files, span offsets, or time-coded transcripts. We confirm the export before production, not after.

A review trail

Which items were escalated, what changed, and which cases remain flagged. Useful when a metric moves and someone asks why.

A calibration record

The sample that locked the guide. Keep it. It is the cheapest way to onboard a new reviewer or to test a guide change later.

FAQ

Questions that belong on this page, not copied onto every service

These answers cover how VIPRO scopes work across modalities. Image, video, text, and audio pages each carry a different set of questions about geometry, time, language, and sound.

What is an annotation dataset?

An annotation dataset is a collection of raw data, such as images, video, text or audio, paired with the labels that describe what is in it. Machine learning models learn from these labelled examples during training.

What can VIPRO annotate?

Image, video, text, and audio. Scopes are built around a model task — detection, tracking, extraction, transcription — not around a generic 'we label everything' promise. If a file type sits outside those four, we will say so instead of stretching a workflow that does not fit.

How is this different from drawing on a platform ourselves?

A platform stores shapes. A service owns the questions that make those shapes consistent. We write the guide, calibrate people against it, review the hard cases, and package an export that matches the schema you named. You still keep the source data and the decisions.

Do you replace automated labelling?

No. Pre-labels are welcome when they save time on a stable class. We treat them as a draft. A person still accepts, corrects, or rejects them against the guide. Automatic output that never meets a reviewer is not a VIPRO delivery.

Can you work in our tools?

Yes, when access and licensing allow. We already work in common annotation platforms and can follow a workflow you already run. If a tool cannot express a rule you need — nested entities, speaker overlap, track interpolation — we say so before the project starts.

How do we share data?

We agree transfer, access, retention, and deletion before any file moves. That can be a controlled bucket, a platform project, or another path your security team accepts. We do not invent a side channel to go faster.

What does a pilot include?

A representative slice of the real data, a first guide, labelled output, a list of disagreements, and a recommendation: lock and scale, revise the ontology, or stop because the task is still undefined. A pilot is a decision, not a discount on clicking.

What changes the timeline?

Volume is only one variable. Class rarity, guideline maturity, review depth, and how often the world produces a new edge case all matter more than a headline turnaround. We estimate after we have seen the calibration set.

Will the same sentences appear on every service page?

No. The four services share a discipline and have different failure modes. Image pages talk about boundaries and occlusion. Video pages talk about identity over time. Text pages talk about spans and layout. Audio pages talk about clocks and overlap. If a paragraph could sit on any page unchanged, we rewrite it.

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Not sure which service fits?

Send a sample of the files that will be difficult, and the decision the model has to make. We will tell you which service owns that work, and what a pilot should contain.

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