Data Labeling and Annotation Services for AI

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Data Labeling and Annotation

Training Data, Engineered

Data labeling and annotation services built as a measured pipeline.

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Reliable partner

Experienced team

Smart solutions

Case Study

“34% of leaders at low-maturity organizations name data availability and quality a top AI challenge.” — Gartner

When a model stalls on label quality rather than architecture, you are in that group. A pipeline that measures every batch is the way out.

Label schema

Class definitions, edge-case rules, and worked examples written so two people label the same item the same way.

Tool configuration

Your annotation tool set up with task templates, review queues, and export formats that match your training code.

Pre-labeling layer

A model produces the first pass, so reviewers correct and adjudicate instead of starting from a blank screen.

Review workflow

Overlapping assignments, escalation of disputed items, and adjudication by a domain expert, all tracked per item.

Quality dashboard

Agreement, accuracy against gold samples, and throughput reported per batch, per annotator, and per class.

Acceptance gate

A threshold each batch must meet before release; batches below it go back for re-labeling, not forward.

Training-loop integration

Accepted labels land in versioned datasets your pipelines pull directly, with no manual reshaping or file handling.

Workforce management

Your annotators or a vendor’s, onboarded, calibrated, and monitored inside the same pipeline and the same metrics.

Images and video

Bounding boxes, polygons, segmentation masks, keypoints, and object tracks, exported in COCO or your schema, with overlap scored against gold samples.

Text and NLP

Entity spans, intent and sentiment classes, and relation links, with per-class agreement showing exactly where the label definitions are unclear.

Documents and forms

Field extraction, clause classification, and layout tagging for contracts, invoices, and compliance records, checked against extracted ground truth.

Time series and events

Labeled outcomes on event streams, such as delivery results or sensor anomalies, aligned by timestamp with the feature window the model uses.

LLM outputs

Preference pairs, response rankings, rubric scores, and flagged failures for fine-tuning and evaluation sets, with rater agreement tracked per rubric.

Multimodal pairs

Image-text and video-transcript pairs with aligned captions and region references, so a single label set serves both modalities.

“In the case of Zoolatech, it's a very tight partnership.

The team at Zoolatech is incredibly collaborative, and we work as a team despite being thousands of miles away from each other.”

Spencer Rascoff

CEO Match Group

5/5

“Zoolatech has been a key technology partner for Pandora,

enhancing our software development and deployment capabilities. They're ambitious, supportive, fast-moving, and well-skilled, with sound ethical values.”

Erika Romsics

Contract and Vendor Manager, Pandora

5/5

“The apps they’ve developed give us the opportunity to get more customers.

We’re providing more services to target big customers. We can install jobs faster and identify reduce bottlenecks, so we’re providing a better customer experience.”

Aida Youssef

Senior Director of Software Engineering, Complete Solaria

5/5

“Zoolatech has access to a deep talent pool and knows how to identify client's needs.

With the help of Zoolatech, went from a very early and incomplete prototype to the MVP release, the first production release, and the first paying customer!”

Greg Wagenhoffer

CEO, GreenVisr

5/5

“Zoolatech enabled us to build a world-class engineering team quickly and efficiently.

Zoolatech's pre-screening process and engineer training are customized for providing effective engineers that can contribute immediately to accelerating product roadmaps.”

Shariq Minhas

CTO, SVSG

5/5

“We can recommend Zoolatech

for their talent pool, attention, ability to understand our requirements, candidate screening process and constant communication.”

Chaitanya Pallapothula

SVP, Tailored Brands, Inc.

5/5

“Zoolatech’s developers quickly became an integral part of our team effort

with whom we shared daily stand up calls. Overall, Zoolatech fit well with our needs for agile development and continued to adapt as our needs evolved.”

Forrest Glick

UX Designer, Stanford University

5/5

“Working with Zoolatech has been a driving force in our business offerings.

The team utilizes it's experience and expertise meshing with our internal team creating a positive work environment. Zoolatech is by far one of the best teams to work with in the industry.”

Kris Naidu

CEO, Zeacon

5/5

Model-assisted pre-labeling

A model labels every item first, and people correct rather than start from blank. Low-confidence items route to full manual labeling, and a share stays unlabeled as a control, so reviewers do not drift toward agreeing with the model.

Agreement on overlaps

A fixed share of items goes to more than one annotator. Agreement is calculated per class, and a low score usually points to an ambiguous label definition rather than a careless person, so the schema gets fixed first.

Gold sets and blind checks

Domain experts label a reference set that annotators never see flagged. Accuracy against it is measured per annotator and per class, and calibration items are mixed into live batches without notice.

Active learning

The model selects the items it is least certain about for the next batch, so effort goes to examples that move the decision boundary instead of ones the model already handles well.

Bias and coverage checks

The labeled set is tested for class balance and for representation across protected attributes and segments, the same fairness testing applied in Zoolatech’s machine learning work, so gaps surface before training starts.

Acceptance gate

Each batch is scored against the threshold agreed at schema design. A batch below it returns for re-labeling with the failing classes named; a batch above it is versioned and released to training.

See how your labels would be scored

Bring one labeling task, and we will show you what the pipeline measures.

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Step 1

Label schema design

We define what counts as a label, where class boundaries sit, and how edge cases are handled, with examples for each rule. Output: a versioned annotation guide. Owner: Zoolatech ML engineer with your domain expert.

Step 2

Calibration batch

A small batch is labeled by everyone who will work on the program, with overlap on every item. Output: agreement scores per class and a revised guide. Owner: Zoolatech, with your reviewers taking part.

Step 3

Tooling setup

Your annotation tool, or one selected with you, is configured with task templates, review queues, and export formats that match your training code. Output: a working project with sample exports. Owner: Zoolatech engineers.

Step 4

Pre-labeling

A model produces first-pass labels for the full set, with confidence recorded per item, so routing between review and full manual labeling is automatic. Output: pre-labeled batches with confidence scores. Owner: Zoolatech.

Step 5

Annotation and adjudication

Annotators correct and confirm, disputed items escalate to adjudication, and quality metrics update per batch. Output: labeled batches with agreement and accuracy figures. Owner: your team or a workforce vendor, managed inside the pipeline.

Step 6

Acceptance and handoff

Batches meeting the threshold are versioned and released into your training dataset; those below it return with the failing classes named. Output: a versioned dataset with a quality report. Owner: Zoolatech, with your ML lead signing off.

Ready to stop guessing at label quality? Let us map the pipeline for you.

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Labels are worth what the model does with them: a delivery-promise model 67% more accurate, and text feedback analysis running 80% faster with LLM classification.

67%

More accurate delivery forecasts

80%

Faster text analysis

98%

Client Retention Rate

300+

Successful Projects

Peak load without hiring

Volume rises with a vendor or your own team, not with a hiring cycle.

Process that scales

Schema, routing, and metrics stay the same at 10,000 items and at 1 million.

No tooling purchase

The tool is configured, not bought, licensed, and maintained by your own engineers.

Quality as a system

Agreement and accuracy are reported per batch instead of checked by occasional spot review.

Faster to first model

Pre-labeling and active learning cut the volume needed before training can start.

Path back in-house

The pipeline is documented and handed over once throughput stabilizes, if you choose.

Build and hand over

We design the schema, configure tooling, and set the quality gates; your team runs it.

Build and run

We operate the program, manage annotators or vendors, and report quality per batch.

Amazon Bedrock

Anthropic Claude

Amazon Titan

Google Vertex AI

Google Gemini

Amazon SageMaker

Amazon EKS

AWS Lambda

Kubernetes

Apache Airflow

Apache Kafka

LangGraph

Google BigQuery

and other

“Only 26% of CDOs are confident their organization can turn unstructured data into business value.” — IBM Institute for Business Value

Unstructured data becomes usable only once it is labeled and measured. A pipeline that scores every batch is how that confidence gets built.

Processing location

Data stays in the environment agreed at the start, including your own infrastructure where the program requires it.

Scoped access

Annotators see only the subset and fields their task needs, with identifiers masked where masking keeps the signal intact.

Encryption throughout

Training data and model artifacts are encrypted at rest and in transit, the standard applied across Zoolatech’s machine learning work.

Rights and retention

Usage rights for labeled data, NDA coverage for everyone touching it, and deletion after delivery are written into the plan.

Production ML results

The same engineers built a delivery-promise model for a Fortune 500 US retailer that raised forecast accuracy by 67%.

Models classifying content

Content categorization and filtering already run in production on Amazon Bedrock, Anthropic Claude, and Google Vertex AI.

Ownership to the model

Responsibility runs from label schema to the trained model, so label problems are found by us, not by your users.

Fix the Labels First

If the model is stuck, the labels are the cheapest place to look.

Software and technology

Labeled event and usage data for models embedded in the product, exported straight into training.

Retail

Product, order, and delivery outcomes labeled for forecasting, search, and recommendation models.

Fintech

Transaction and document labels with reviewer trails that survive an audit of the training set.

Healthcare

Pipelines built to run inside the provider’s environment for records that cannot be moved.

Telecom

Network events and support transcripts labeled for fault, churn, and intent classification.

Energy

Sensor and inspection data labeled for anomaly and maintenance models across distributed assets.

Travel

Booking, review, and itinerary text labeled for pricing, intent, and recommendation models.

Logistics

Shipment events and delivery outcomes labeled by timestamp for ETA and exception models.

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Custom software development

Applications that consume model outputs inside the workflows where decisions are made.

Mobile app development

Mobile products that capture and use labeled signals with the latency users expect.

Legacy application modernization

Free the training data locked in aging systems without interrupting operations.

AI & ML services

Model training, evaluation, and MLOps once the labeled dataset is accepted.

Cloud development services

Cloud data platforms that store versioned datasets and serve them to training jobs.

Team extension services

ML and data engineers embedded in your team to run the labeling program.

At Zoolatech, we create engineering teams for industry leaders across the US and Europe — teams that move fast, think big, and deliver strong impact.

96%

Client Satisfaction

300+

Successful Projects

2017

Year Founded

98%

Retention Rate

At Zoolatech, we create engineering teams for industry leaders across the US and Europe — teams that move fast, think big, and deliver strong impact.

Engineering Excellence. Every Time.

At Zoolatech, we create engineering teams for industry leaders across the US and Europe — teams that move fast, think big, and deliver strong impact.

600+

Employees

Headquarters

USA

Development Centers

Stop Training on Guesswork

Tell us what you are training, and we will show you which labels are holding the model back and how to fix them.

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Questions You May Have

What is the difference between data labeling and data annotation?

None in practice; both terms describe attaching the labels a model learns from to raw data, such as categories on text, bounding boxes on images, or preference rankings on model outputs. Vendors use whichever term their buyers search for, and no industry standard enforces a distinction between the two.

What is the hybrid approach to data labeling?

Labeling work splits three ways: fully manual, fully automated, and hybrid, where a model pre-labels every item and people correct and adjudicate only the cases the model is uncertain about. The hybrid approach is what this pipeline runs, because it keeps human judgment on the items where it changes the outcome.

Do you provide annotators, or do you build the process?

We build and run the process: label schema, tool configuration, model-assisted pre-labeling, quality measurement, and integration into your training pipeline. Annotation capacity comes from your own team or a workforce vendor, and either one is managed inside the same pipeline with the same metrics.

How do you measure annotation quality?

Through agreement between annotators on overlapping samples, accuracy against a gold standard set labeled by domain experts, and an acceptance threshold below which a batch is returned rather than delivered. All three are reported per batch and per class, so a problem is traced to a label definition or a person instead of guessed at.

Does model-assisted pre-labeling hurt quality?

It can, because reviewers tend to accept what they are shown, which is why low-confidence items go to full manual labeling and a share of items stays unlabeled as a control. Handled that way, pre-labeling reduces effort without lowering the accuracy the gold set measures.

What drives the cost when we outsource data annotation services?

Schema complexity, the share of disputed items, the accuracy threshold you need, volume and delivery rhythm, security requirements, and whether tooling already exists. A calibration batch answers the question more precisely than any estimate made before it.

When is Zoolatech not the right fit?

If you need only hands for a large volume of simple labeling, with no pipeline, tooling, or quality engineering around it, a specialized workforce vendor will be cheaper. This service is for teams whose model is limited by label quality or whose program needs to scale without breaking.

How do we choose a data annotation partner?

Ask whether quality is measured or asserted, whether the provider owns the engineering or only the workforce, and whether the output lands in your training pipeline or arrives as files someone must reshape. Public case studies with numbers answer more than a capability list ever will.