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AI DATA ANNOTATION SERVICES

Your AI Is Only as Good as the Data It Learns From

Poor training data leads to unreliable models, wasted training cycles, and costly rework.
Tech.us builds high-quality datasets across text, images, video, audio, and multimodal content with clear specifications and quality checks that improve model performance.

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TRUSTED BY ORGANIZATIONS THAT DEPEND ON TECHNOLOGY

WHAT DATA ANNOTATION CHANGES

Build Training Data That Improves AI Performance

A model learns from every label it receives. Better datasets reduce rework, improve reliability, and help AI systems perform when they meet real-world cases.

Reduce Costly Retraining

Catch labeling problems early instead of discovering them after a model fails.

Improve Model Reliability

Cover the examples that matter most, including rare and difficult cases.

Make AI Performance Measurable

Use consistent labels and evaluation data to understand whether the model is actually improving.

Create Datasets That Last

Keep guidelines and decisions documented so your dataset remains useful as models and requirements change.

DATA ANNOTATION SERVICES

From Raw Data to Model-Ready Training Sets

We design annotation workflows that create reliable datasets for AI systems across text, images, video, audio, and multimodal applications.

Text and Document Annotation

Entity spans, relations, classification, intent, sentiment, clause boundaries, and layout-aware labeling across languages and document formats.

Image Annotation

Bounding boxes, polygons, semantic and instance segmentation, keypoints, and attribute labeling for detection, inspection, and recognition tasks.

Video Annotation

Frame-level labeling, object tracking across frames, action and event boundaries, and temporal segmentation.

Audio and Speech Annotation

Transcription, speaker separation, dialect and language tagging, event marking, and tone or emotion labeling.

Multimodal Annotation

Aligned labeling across text, image, audio, and video within a single dataset for tasks that depend on more than one signal.

Guideline Design and Task Definition

Converting an informal request into a specification two people can apply the same way, including edge cases and exclusions.

Sampling and Dataset Construction

Selecting what to label, balancing classes, mining hard cases, and reserving evaluation data before work begins.

Dataset Audit and Remediation

Assessing a dataset you already hold for label accuracy, class consistency, guideline drift, and duplication, then correcting what is recoverable.

Model-Assisted Pre-Labeling

Using a trained model to propose labels for human correction, which raises throughput on established tasks without shifting the decision away from a person.

QUALITY

Know What Makes Your Dataset Reliable

Annotation quality cannot be measured by volume alone. We verify that labels are consistent, explainable, and aligned with the model's purpose.

Independent Quality Checks

Samples are reviewed independently to identify inconsistencies before they affect training.

Domain-Based Review

Your experts can define standards and resolve cases where the correct label requires deeper context.

Class-Level Reporting

We identify where categories perform well and where additional work is needed.

Complete Traceability

Guidelines, decisions, and dataset versions are documented so changes can be understood later.

WHAT YOU RECEIVE

A Dataset Built for More Than One Model

The value of annotation is not only the labels delivered today. It is the documentation, decisions, and quality evidence that make the dataset reusable as your AI systems evolve.

A well-built dataset becomes an asset your team can improve, audit, and reuse as models and requirements change.

A labeled dataset ready for your training pipeline

Versioned guidelines that capture labeling decisions and edge cases

Quality reports showing agreement and areas requiring attention

Evaluation data kept separate for measuring model performance

Dataset history showing how each label was created and reviewed

Unresolved cases clearly identified instead of hidden

SELECTED WORK

Datasets Behind Production Models

Explore how we help organizations solve real business problems with practical technology solutions.

View All Case Studies

AI-Powered Takeoffs From Complex Construction Drawings

A precast concrete manufacturer partnered with Tech.us to bring AI into the estimating workflow. The system uses OCR, computer vision, and generative AI to identify, extract, and visualize structures, pipes, and components from complex construction drawings.

Read the Case Study
Wellington Hamrick Precast AI takeoff automation tool shown on laptop and tablet

Mobile Fleet Visibility Built for Field Operations

SkyHawk by TELUS needed a streamlined mobile experience for its Connect Anywhere platform. Tech.us built the core experience around real-time asset tracking, fleet activity, secure configuration, and map-based operational visibility.

Read the Case Study
SkyHawk by TELUS mobile app screens showing fleet tracking and login

Personalized Financial Technology Built to Scale

Tech.us helped bring Wealth Mastery to life as a digital platform that puts personalized financial planning tools directly in users' hands while supporting a large and growing audience.

Read the Case Study
Tony Robbins Wealth Mastery app shown on tablet and phone

WHY TECH.US

Annotation Quality You Can Measure and Trust

We combine clear specifications, expert review, and measurable agreement to build datasets that hold up beyond delivery.

We Write the Specification First

The guideline is the product. Labeling before the rules are settled produces volume that has to be redone.

We Report Disagreement Openly

Agreement rates and per-class weaknesses are handed over, including the categories not yet reliable enough to train on.

We Push Back on Bad Taxonomies

A category people cannot apply consistently will not work in a model either. We recommend changing it rather than labeling around it.

We Build for the Model, Not the Invoice

Sampling, class balance, and hard-case mining reduce how many labels you need, which reduces what we bill.

Domain Reviewers on Specialist Work

Clinical, legal, financial, and technical material is reviewed by people who read that material professionally.

We Support the Dataset Afterwards

Taxonomies change and definitions move. We version, extend, and relabel rather than treating delivery as the end.

why-choose-techus

HOW WE WORK

Settle the Rules, Then Scale the Volume

We resolve the difficult labeling decisions early so quality does not break when the dataset scales

1

Define the Task

We establish what the labels are for, what the model has to do with them, and what accuracy the downstream use case genuinely requires.

2

Pilot a Small Batch

A few hundred items labeled by multiple annotators, which surfaces the ambiguities faster and cheaper than any amount of guideline review.

3

Resolve and Rewrite

Disputes are adjudicated with your domain experts and written into the guideline before volume work begins.

4

Build the Sample

We select and balance what will be labeled, mine for hard cases, and reserve the evaluation set.

5

Label With Audit in Line

Annotation runs with overlap, ongoing audit sampling, and per-class reporting rather than a single review at the end.

6

Deliver and Support Retraining

We hand over the dataset with its guideline, agreement report, and provenance, and support relabeling as your categories change.

DATA SECURITY

Protect Sensitive Data During Human Review

Annotation requires people to see your data. We build controls around who can access it and how it is handled.

Limit Access

Annotators only see the data required for their assigned work.

Protect Confidential Information

Sensitive fields can be removed or protected before annotation begins.

Control Data Lifecycle

Retention and reuse policies are defined before work starts.

ANNOTATION TOOLING

The Platform Follows the Task

We work across annotation platforms for text, vision, video, audio, and multimodal work, model-assisted labeling, agreement and quality reporting, and dataset versioning and lineage tooling. Selection follows the label type, review workflow, volume, integration with your training pipeline, and where the data is permitted to be processed.

Agentic AI & Orchestration

Agentic AI Agentic AI
AI Agents AI Agents
Multi-Agent Systems Multi-Agent Systems
Agentic Workflow Automation Agentic Workflow Automation
Model Context Protocol (MCP) Model Context Protocol (MCP)
Agent-to-Agent Protocol (A2A) Agent-to-Agent Protocol (A2A)
Agent Memory and Reasoning Agent Memory and Reasoning

FAQ

Questions Buyers Ask About Annotation

Through blind re-annotation of a sample, gold items set by your experts rather than ours, audit sampling during delivery, and per-class reporting. Accuracy measured against a standard the vendor authored is not independent evidence.

It marks the cases the guideline has not resolved, and those are the cases a model will get wrong. Suppressing disagreement with majority vote removes the signal without fixing the cause.

Less than most teams assume for a first useful model, often a few hundred well-labeled examples per category. Coverage of hard and rare cases affects results more than total volume.

Both, with the decision staying with a person. A model can propose labels for correction on established tasks, which raises throughput, and every proposal is reviewed rather than accepted by default.

Yes, and for specialist categories it is usually the right approach. Your experts set the gold standard and adjudicate disputes while our annotators handle volume against the guideline they define.

They are recorded as unresolved and handed to you with the dataset. A category your own experts cannot apply consistently is a task definition problem, and we would rather flag it than bury it.

Yes. We assess label accuracy, class consistency, guideline drift, and duplication, then advise whether targeted correction or relabeling is the better investment.

Identifying fields are removed where the task does not require them, annotator access is scoped to assigned items, named annotators work under agreement on confidential material, and retention is defined before work starts.

No. Your data is not used for other clients, other models, or internal training sets.

Per project, driven by label complexity, volume, review depth, the number of annotators per item, and the domain expertise required. We scope it after seeing a sample and the task definition.

Ready to Build Training Data That Improves Model Performance?

Send us a sample and what you are trying to teach the model. We will tell you what the dataset has to cover and where the labeling will be contentious.

Schedule a Dataset Consultation

Task definition · Sampling plan · Agreement targets · Practical next step