Which brands AI mentions

#BrandMentioned in

1iMerit77%

=2People for AI42%

=2CloudFactory40%

=4Annotera25%

=4Centric Labs19%

Mentioned in · Jul 6 – Oct 17, 2026

ChatGPT SearchChatGPT SearchGoogle AI ModeGoogle AI Mode

Widest split: Annotera, 50% on ChatGPT Search and 0% on Google AI Mode.

1iMeritChatGPT Search 62%Google AI Mode 92%
=2People for AIChatGPT Search 58%Google AI Mode 27%
=2CloudFactoryChatGPT Search 19%Google AI Mode 62%
=4AnnoteraChatGPT Search 50%Google AI Mode 0%
=4Centric LabsChatGPT Search 38%Google AI Mode 0%

Questions and answers

ChatGPT SearchOct 13, 2026

I found several data-labeling providers that explicitly say they use dedicated, trained employees or in-house teams rather than crowdsourced workers. The key distinction is to look for a managed, non-crowdsourced workforce and then verify whether the contract guarantees employees rather than independent contractors or subcontractors.

Here are the strongest candidates based on their published workforce policies.

Best data-labeling services to evaluate

1. Sama

(In-house workforce)

Best starting point for enterprise-scale labeling with an emphasis on security and quality controls. Sama says its annotation work is performed by a full-time, in-house workforce in secure facilities rather than through crowdsourcing. Its services include image, video and 3D data annotation.

Website:

2. iMerit

A strong candidate for large or technically specialized projects, including medical imaging, computer vision, LiDAR, documents and NLP. Published company and marketplace descriptions identify a large, full-time workforce and structured delivery operations rather than a crowdsourced task marketplace.

Website:

3. Deepen AI

Particularly suitable for autonomous vehicles, robotics and physical AI. Deepen AI explicitly states that its annotation, calibration, curation and validation work is done by an in-house team it trains and manages, and is never crowdsourced.

Website:

4. People for AI

Explicitly advertises that it uses in-house labelers and does not crowdsource. Its project examples include microscopy, autonomous vehicles, infrastructure and retail data. A useful option for specialist labeling where consistent teams matter.

Website:

5. Hugo

Worth considering for nuanced human judgment tasks, including LLM evaluation, RLHF, robotics and complex image annotation. Hugo says it does not use anonymous crowdsourced click-workers and instead provides dedicated, managed teams of university-educated talent. Confirm the employment status of the assigned team.

Website:

6. Hewani Data

An option to investigate if cost matters alongside workforce continuity. Hewani says its annotation and QA are handled by a full-time, trained team hired and qualified before working on live data, rather than a crowdsourced task queue. Its workforce is based in East Africa.

Website:

Three additional providers worth comparing

Provider	Why it fits	Website
Cogito Tech	Explicitly says it does not crowdsource or use freelancers; operates staffed delivery centers in India.	cogitotech.com
Centric Labs	Advertises full-time, background-checked annotators, dedicated project managers and QA leads.	centriclabs.ai
Labelforce	Recruits and manages custom labeling teams rather than relying on generic crowdsourcing. Verify direct employment versus contractor arrangements.	labelforce.ai

How I would shortlist them

For a strict employee-only requirement: Start with Cogito Tech, iMerit and Centric Labs. Their published descriptions are particularly relevant to a managed workforce model. Note that iMerit also advertises a separate flexible expert program that includes independent contractors, so you should specify which workforce model is permitted.
For autonomous driving, robotics or LiDAR: Start with Deepen AI and iMerit.
For enterprise computer vision and multimodal labeling: Start with Sama and iMerit.
For NLP, LLM evaluation and complex human judgment: Compare Hugo and Cogito Tech.

These are vendor claims, not independent audits of every worker relationship. Before signing, ask for contractual confirmation that the project will use employees only, with no freelancers, crowd platforms or undisclosed subcontractors.

Three questions to narrow this down

The best fit depends on your data type and how strictly you define employment.

1. What kind of data needs labeling?

Computer vision, images or video

Text, NLP or LLM evaluation

Audio or speech

Medical, scientific or specialist data

LiDAR, robotics or autonomous driving

Multiple data types

2. What counts as acceptable labor?

Direct employees only (no contractors)

Paid, dedicated workers; contractors acceptable, but no crowdsourcing

3. What scale do you need?

Small: one-time dataset or pilot

Medium: ongoing project

Large: dedicated team or enterprise-scale volume

Preferred worker location (optional)

US-based workers only

US or other high-income countries

Any country, provided workers are employees

[Find my best-fit providers]

Sources cited in this answer17

Google AI ModeOct 13, 2026

If you want to avoid crowdsourced platforms (like Amazon Mechanical Turk or open-invite gig apps) in favor of a managed workforce model using dedicated, trained, and paid employees, several reputable data labeling and annotation companies operate this way.

These companies typically provide full-time or regular part-time staff, formal training, quality assurance management, and data security compliance that crowdsourced options lack.

To help narrow down the best fit, let me know:

Sources cited in this answer6

Google AI ModeOct 17, 2026

For high-compliance, high-context, or complex AI projects where crowdsourced gig-workers fall short on consistency and security, several notable data labeling providers rely instead on dedicated, fully managed, or in-house workforces:

iMerit — Operates with a full-time, professional, in-house workforce rather than an open crowd. Their annotators work out of secure global delivery centers (across the US, India, and Bhutan), making them ideal for enterprise-grade computer vision, NLP, and medical/critical AI.
Sama — An ethical AI and data annotation provider that utilizes a directly employed, dignified, full-time workforce (primarily in East Africa and South Asia). They focus heavily on high-accuracy computer vision and autonomous systems annotation with strict data security guarantees.
SuperAnnotate Managed Services — Rather than relying on public crowdsourcing marketplaces, they offer a curated, fully managed service tier utilizing vetted, professionally trained, and ID-verified annotation teams tailored to complex multimodal or domain-specific workflows.
CloudFactory — Recruits, trains, and manages "data work teams" as permanent employees rather than floating independent contractors. They specialize in pairing managed workforces with dedicated team leaders for continuous, long-term machine learning pipelines.

If you want to narrow this down, let me know:

What data modality you are working with (images, video, text, or audio/RLHF)?
Do you have security/compliance requirements (like HIPAA or SOC2) that require specific geographic delivery locations?

Sources cited in this answer1

digi-texx.com17 Data Labeling Service Providers To Consider In 2026

ChatGPT SearchOct 17, 2026

If your priority is a data labeling provider with a dedicated, managed workforce rather than an open crowdsourcing model, I’d start with Sama, Innodata, People for AI, and Centric Labs. These providers are worth evaluating because they describe their workforces in terms of full-time employees, long-term teams, or specifically recruited and trained annotators.

One important distinction: an in-house workforce is not necessarily the same as a team dedicated exclusively to your project. Some providers employ their annotators directly but allocate them across multiple clients. If workforce continuity, confidentiality, and accumulated knowledge of your labeling guidelines matter, confirm both points in the contract.

Recommended providers

1. Sama

(Strong enterprise option)

Best for computer vision, autonomous systems, and large-scale image, video, and 3D point-cloud annotation.

Sama explicitly describes its workforce as full-time, in-house staff working in secure delivery centers. It also reports an average annotator tenure of over two years and emphasizes quality controls and data security.

Website:

2. Innodata

(Best for complex, multimodal work)

Best for LLM training, preference and evaluation data, text, speech, document intelligence, and sensor data.

Innodata emphasizes specialist annotators—including linguists, engineers, and other subject-matter experts—rather than generic crowd labor. It offers managed programs with structured quality assurance and a dedicated team after the scoping and pilot phases.

Website:

3. iMerit

(Best for managed production teams)

Best for computer vision, LiDAR, medical AI, geospatial data, and specialized AI training.

iMerit describes its core annotation workforce as full-time, trained, and in-house, with dedicated project management and managed quality assurance. Its iMerit Scholars program also provides access to specialized domain experts for more demanding tasks.

Website:

4. People for AI

(Best for long-term team continuity)

Best for general-purpose labeling, NLP, computer vision, microscopy, infrastructure, and retail datasets.

People for AI explicitly states that it does not use crowdsourcing and employs long-term labelers. It reports that more than 90% of its collaborators are on permanent contracts, making it particularly relevant when employment stability is a priority. Its operations are based in Madagascar, with project management in France.

Website:

5. Centric Labs

(Best for explicitly assigned teams)

Best for companies that want a named, managed team with project-specific training and direct operational accountability.

Centric Labs says it uses full-time, background-checked annotators, with dedicated project managers and quality leads. Its stated services span image, video, text, audio, and 3D point clouds. These workforce details are vendor claims that should be verified during procurement.

Website:

6. Labelforce

Best for custom-built teams and flexible tooling.

Labelforce says it recruits and trains labelers individually for client requirements rather than relying on generic crowdsourcing. It offers dedicated teams for computer vision, generative AI training, and other custom workflows, and can work with the client's preferred labeling software.

Website:

How I would shortlist them

Your primary requirement	Start with
Secure, high-volume image/video labeling	Sama, iMerit
LLM training, RLHF, and expert evaluation	Innodata, iMerit
Long-term employment and workforce stability	People for AI, Sama
A named team trained on your taxonomy	Centric Labs, Labelforce
Complex specialist or multimodal annotation	Innodata, iMerit

These are starting points based on publicly described workforce models and capabilities, not independently verified rankings.

Questions to ask before signing

I would ask every provider the same five questions:

Employment: Are all annotators direct employees, or do you use contractors, freelancers, subcontractors, or third-party labor?
Exclusivity: Will a named team work exclusively on our project, or will annotators rotate between clients?
Continuity: What percentage of the team will remain assigned for the duration of the engagement?
Security: Where will data be processed, and can you contractually prohibit subcontracting, personal-device access, and use of our data for model training?
Quality: Can we run a paid pilot with gold-standard test items, inter-annotator agreement metrics, and a defined error-correction process?

My recommendation: Start with Sama, Innodata, and iMerit for enterprise-scale requirements, and add People for AI if long-term employment stability is your strongest criterion. Include Centric Labs if you want an explicitly named, project-specific team.

One clarification would help me narrow this to the best three: What kind of data are you labeling—LLM/RLHF, text, images/video, LiDAR, audio, or something else?

Sources cited in this answer18

Also asked as

I need to find a data annotation service that employs their own staff directly. Are there options that avoid crowdsourced labor?

In-house data labeling services

Which brands AI mentions