Defined.ai

Name: Defined.ai
Brand: Defined.ai
Rating: 4.5 (2 reviews)

Empower the builders of AI with high-quality, ethically sourced data

Est 2015|Valuation $355M|Raised $81.9M|13 Ref Customers

LinkedIn 41K|Web traffic 15K|FTEs 213|HQ Seattle, Washington, US

Published Oct 15, 2026

01Common complaints

- If procurement requires an extensive set of independent enterprise buyer reviews: public review coverage is limited and may not represent enterprise procurement experiences. - If reputational risk related to contributor treatment is a critical concern: recurring worker allegations about payment, bans, and opaque QA processes linked to the contributor channel may be a material consideration unless the vendor can demonstrate fair-pay practices, dispute resolution mechanisms, and provenance controls. - If the priority is rapid, low-cost experiments with explicit unit economics: vendors with documented self-serve per-unit pricing may be better suited for early-stage iteration. - If the primary requirement is large-scale RLHF/LLM alignment delivered by an RLHF-specialist with explicit programmatic expertise, consider vendors that position specifically for that work and compare governance and pricing models.

Deal disqualifiers

Requirement for extensive third-party enterprise reviews and independent benchmarks where public evidence is limited.
Mandate for SOC 2 reports and inability to accept ISO-only documentation unless SOC 2 reports can be provided in diligence.
Need for very low-budget pilots or transparent self-serve unit economics; alternatives with clearer self-serve models may be preferable.

Known limitations from public evidence

Limited enterprise review footprint on mainstream B2B review platforms; this constrains independent verification of enterprise buyer experience.
Contributor-platform trust issues: recurring allegations regarding payment, bans, and opaque QA on the contributor channel.
Platform usability complaints from contributor reviews, such as session or login stability problems.

Roadmap / future features

No clear public roadmap with dated commitments was found. Procurement teams should request roadmap commitments in writing if future capabilities are decision-critical.

02Why they buy

Unique selling points supported by public evidence

Published, tiered speech dataset access plan (DAP) with explicit volume and price points, which is more transparent than some competitors' publicly available information.
Public statements of ISO 27001 renewal and ISO 27701 certification, which address certain enterprise security and privacy gating criteria.
Focus on speech and multilingual datasets with both off-the-shelf catalog offerings and custom data services.

Where competitors may have advantages

Scale AI: visible documentation around self-serve workflow pricing mechanics and broad task coverage.
Appen: established brand recognition in AI data services and a long-standing operating footprint.
LXT: explicit positioning around RLHF and alignment workflows for certain buyer needs.

03Field reports

Review-platform signals (G2)

G2 coverage for Defined.ai is limited and does not provide strong independent buying signals. The reviews that are present tend to reflect contributor/worker experiences as much as enterprise buyer feedback. Customer quote (worker perspective): - "DefinedCrowd is one of the best crowdsourcing companies I have ever completed work for… The pay is much higher then other 'crowdworker' 'task based' platforms… steady stream of working available." Customer quote (platform usability): - "Their App… For many times, we had to keep logging out then logging in again." Interpretation: Public platform reviews skew toward the contributor experience rather than enterprise procurement experiences; this is common where a company operates both a B2B data business and a crowd labor platform.

Community and worker sentiment (third-party review sites)

Worker reviews on third-party sites for the contributor platform historically linked to the vendor show polarized sentiment with a number of negative reports concerning payments and account actions. Negative worker paraphrases (allegations of non-payment or bans): - Reports from contributors describe completing many tasks without receiving promised payments. - Accounts being suspended or closed with limited explanation and delayed or withheld compensation. - Contributors describing long delays in receiving payments after project completion. More mixed worker feedback also references payment delays and project closure timing for payments. Why this matters to enterprise buyers: If a supplier's data supply chain depends on crowd contributors, persistent allegations about contributor treatment can create reputational risk, supply risk, or data provenance risk, even if the enterprise delivery organization is contractually separated.

Community forums

Searches for discussion on forums returned limited high-signal threads directly about the vendor. Prospective buyers may still wish to review forum discussions for deeper worker sentiment and operational anecdotes.

Why customers choose Defined.ai (evidence available)

From vendor-published case studies and public positioning, customers appear to select Defined.ai for: - Multilingual coverage and access to global contributors. - Speech/ASR specialization and available off-the-shelf speech datasets. - Documented security/privacy certifications (ISO 27001/27701) and vendor messaging around responsible sourcing. Independent, enterprise-scale third-party buyer reviews are sparse, which limits external verification of these selection rationales.

What Defined.ai publishes

The vendor publishes multiple case studies across industries such as finance, telecom, payments, energy, and retail.

Examples of vendor-published outcomes include reductions in speech recognition error rates and bilingual chatbot implementations. These case studies generally highlight outcomes but omit detailed methodological disclosures.

Critical evaluation of these claims

Metrics in case studies are vendor-published and typically do not disclose baselines, data volumes, annotation guidelines, QA acceptance thresholds, or independent audits.
Procurement teams should request project scopes, measurement methodologies, and any third-party validation during reference checks.

Projects that struggle or fail

No failed-implementation case studies were found in public sources (vendors seldom publish failures). Prospective buyers should request reference calls that include at least one project that had issues and should obtain SLAs and dispute remediation terms for data quality disagreements.

04Best fit

Company sizes

Mid-market to enterprise AI teams with substantial dataset budgets.
Published DAP tiers suggest larger dataset commitments in the tens to hundreds of thousands of euros for speech dataset access, plus additional spend for custom work.

Industries / verticals (examples)

Vendor-published case studies and materials indicate use in finance (ASR), telecommunications/contact centers, payments (bilingual chatbots), and energy/utilities (computer vision for operations).

Buyer personas

Head of ML / Applied AI
Data engineering leadership responsible for data pipelines
Product leaders for voice assistants and customer support automation
Compliance and security stakeholders for regulated datasets

Fit qualifiers

Need for multilingual and accent-diverse speech data.
Requirement for documented security/privacy certifications such as ISO 27001 and ISO 27701.
A supplier capable of delivering custom collection and annotation at scale.

05The pitch

What the product does (objective value proposition)

Defined.ai is a B2B provider and marketplace that sources, curates, and delivers datasets used to train and evaluate machine-learning systems, notably in speech/ASR, NLP, and computer vision. Offerings include: - Off-the-shelf datasets (particularly speech) available via a marketplace/catalog model. - Custom data collection (audio, image, video, text) and data annotation/transcription delivered as services and workflows. - A contributor/crowd channel used to scale data creation and labeling. Defined.ai emphasizes responsible data sourcing practices such as bias mitigation, consent handling, and regulatory compliance in vendor materials.

Target market and positioning

- Primary buyers: Enterprises and scale-ups building ASR, conversational AI, NLP, computer vision, and LLM fine-tuning pipelines. - Use-case concentration: Speech data (languages, accents, call-center audio), multilingual NLP, and image annotation for computer vision. - Positioning statement: The vendor describes itself as a global ethical AI marketplace and positions its offering around high-quality, ethically sourced data; these are vendor assertions and are difficult to verify using public, standardized measures.

Get a comprehensive analysis of Defined.ai including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Oct 15, 2026

01Common complaints

If procurement requires an extensive set of independent enterprise buyer reviews: public review coverage is limited and may not represent enterprise procurement experiences.
If reputational risk related to contributor treatment is a critical concern: recurring worker allegations about payment, bans, and opaque QA processes linked to the contributor channel may be a material consideration unless the vendor can demonstrate fair-pay practices, dispute resolution mechanisms, and provenance controls.
If the priority is rapid, low-cost experiments with explicit unit economics: vendors with documented self-serve per-unit pricing may be better suited for early-stage iteration.
If the primary requirement is large-scale RLHF/LLM alignment delivered by an RLHF-specialist with explicit programmatic expertise, consider vendors that position specifically for that work and compare governance and pricing models.

Deal disqualifiers

Requirement for extensive third-party enterprise reviews and independent benchmarks where public evidence is limited.
Mandate for SOC 2 reports and inability to accept ISO-only documentation unless SOC 2 reports can be provided in diligence.
Need for very low-budget pilots or transparent self-serve unit economics; alternatives with clearer self-serve models may be preferable.

Known limitations from public evidence

Limited enterprise review footprint on mainstream B2B review platforms; this constrains independent verification of enterprise buyer experience.
Contributor-platform trust issues: recurring allegations regarding payment, bans, and opaque QA on the contributor channel.
Platform usability complaints from contributor reviews, such as session or login stability problems.

Roadmap / future features

No clear public roadmap with dated commitments was found. Procurement teams should request roadmap commitments in writing if future capabilities are decision-critical.

02Why they buy

Unique selling points supported by public evidence

Published, tiered speech dataset access plan (DAP) with explicit volume and price points, which is more transparent than some competitors' publicly available information.
Public statements of ISO 27001 renewal and ISO 27701 certification, which address certain enterprise security and privacy gating criteria.
Focus on speech and multilingual datasets with both off-the-shelf catalog offerings and custom data services.

Where competitors may have advantages

Scale AI: visible documentation around self-serve workflow pricing mechanics and broad task coverage.
Appen: established brand recognition in AI data services and a long-standing operating footprint.
LXT: explicit positioning around RLHF and alignment workflows for certain buyer needs.

03Field reports

Review-platform signals (G2)

Customer quote (worker perspective):

"DefinedCrowd is one of the best crowdsourcing companies I have ever completed work for… The pay is much higher then other 'crowdworker' 'task based' platforms… steady stream of working available."

Customer quote (platform usability):

"Their App… For many times, we had to keep logging out then logging in again."

Interpretation: Public platform reviews skew toward the contributor experience rather than enterprise procurement experiences; this is common where a company operates both a B2B data business and a crowd labor platform.

Community and worker sentiment (third-party review sites)

Worker reviews on third-party sites for the contributor platform historically linked to the vendor show polarized sentiment with a number of negative reports concerning payments and account actions.

Negative worker paraphrases (allegations of non-payment or bans):

Reports from contributors describe completing many tasks without receiving promised payments.
Accounts being suspended or closed with limited explanation and delayed or withheld compensation.
Contributors describing long delays in receiving payments after project completion.

More mixed worker feedback also references payment delays and project closure timing for payments.

Why this matters to enterprise buyers: If a supplier's data supply chain depends on crowd contributors, persistent allegations about contributor treatment can create reputational risk, supply risk, or data provenance risk, even if the enterprise delivery organization is contractually separated.

Community forums

Why customers choose Defined.ai (evidence available)

From vendor-published case studies and public positioning, customers appear to select Defined.ai for:

Multilingual coverage and access to global contributors.
Speech/ASR specialization and available off-the-shelf speech datasets.
Documented security/privacy certifications (ISO 27001/27701) and vendor messaging around responsible sourcing.

Independent, enterprise-scale third-party buyer reviews are sparse, which limits external verification of these selection rationales.

What Defined.ai publishes

The vendor publishes multiple case studies across industries such as finance, telecom, payments, energy, and retail.

Critical evaluation of these claims

Metrics in case studies are vendor-published and typically do not disclose baselines, data volumes, annotation guidelines, QA acceptance thresholds, or independent audits.
Procurement teams should request project scopes, measurement methodologies, and any third-party validation during reference checks.

Projects that struggle or fail

04Best fit

Company sizes

Mid-market to enterprise AI teams with substantial dataset budgets.
Published DAP tiers suggest larger dataset commitments in the tens to hundreds of thousands of euros for speech dataset access, plus additional spend for custom work.

Industries / verticals (examples)

Vendor-published case studies and materials indicate use in finance (ASR), telecommunications/contact centers, payments (bilingual chatbots), and energy/utilities (computer vision for operations).

Buyer personas

Head of ML / Applied AI
Data engineering leadership responsible for data pipelines
Product leaders for voice assistants and customer support automation
Compliance and security stakeholders for regulated datasets

Fit qualifiers

Need for multilingual and accent-diverse speech data.
Requirement for documented security/privacy certifications such as ISO 27001 and ISO 27701.
A supplier capable of delivering custom collection and annotation at scale.

05The pitch

What the product does (objective value proposition)

Off-the-shelf datasets (particularly speech) available via a marketplace/catalog model.
Custom data collection (audio, image, video, text) and data annotation/transcription delivered as services and workflows.
A contributor/crowd channel used to scale data creation and labeling.

Defined.ai emphasizes responsible data sourcing practices such as bias mitigation, consent handling, and regulatory compliance in vendor materials.

Target market and positioning

Primary buyers: Enterprises and scale-ups building ASR, conversational AI, NLP, computer vision, and LLM fine-tuning pipelines.
Use-case concentration: Speech data (languages, accents, call-center audio), multilingual NLP, and image annotation for computer vision.
Positioning statement: The vendor describes itself as a global ethical AI marketplace and positions its offering around high-quality, ethically sourced data; these are vendor assertions and are difficult to verify using public, standardized measures.

Get a comprehensive analysis of Defined.ai including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →