Tasq.ai

Tasq.ai provides a data processing and management layer for AI that offers annotation, evaluation, and validation services across text, video, audio, and images. It serves AI teams that need trusted model training data and production‑grade output verification for use cases such as physical AI, advertising, commerce, and defense. The platform routes decisions to models, a 100M+ contributor network, or verified domain experts based on confidence levels, delivering a neutral trust layer for production AI.

Tasq.ai is profiled on the APIs.io network. Tagged areas include Annotation, Evaluation, Validation, AI Infrastructure, and Production AI.

Tasq.ai’s developer surface includes engineering blog and 6 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

9.3/100 minimal Agent 6/100 agent aware Full breakdown ↓
scored 2026-10-31 · rubric v0.25.0 · Show this score on your site →

0 APIs

Annotation Evaluation Validation AI InfrastructureProduction AI

Kin Score

Kin Score How this is scored →
scored 2026-10-31 · rubric v0.25.0

Composite quality — 9.3/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 3.6 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.1 / 9

Regulatory Posture 2.1 / 15

Agent readiness — 6/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 4 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

11 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Developer Ergonomics scores 2.4 — up to +19.5 composite points
Access Clarity scores 21.1 — up to +15.8 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/tasq/apis.yml: open an issue on github.com/api-evangelist/tasq to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Tasq Domain Security

TLSv1.3 · DMARC

SECURITY

Resources

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Blog

Blog

Website

Website

Other 1

Properties that don't map to a standard resource type

Hosts

Hosts

Source (apis.yml)

aid: tasq
name: Tasq.ai
image: https://www.tasq.ai/wp-content/uploads/2026/10/tasq_hero_desktop_poster.jpg
description: Tasq.ai provides a data processing and management layer for AI that offers annotation, evaluation, and validation
  services across text, video, audio, and images. It serves AI teams that need trusted model training data and production‑grade
  output verification for use cases such as physical AI, advertising, commerce, and defense. The platform routes decisions
  to models, a 100M+ contributor network, or verified domain experts based on confidence levels, delivering a neutral trust
  layer for production AI.
url: https://raw.githubusercontent.com/api-evangelist/tasq/refs/heads/main/apis.yml
x-type: company
x-source: harvest:g2-catalog
x-tier: profiled
x-tier-reason: enrichment pass 2026-10-21 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-10-21'
modified: '2026-10-21'
tags:
- Annotation
- Evaluation
- Validation
- AI Infrastructure
- Production AI
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: LLMsTxt
  url: llms/tasq-llms.txt
- type: Hosts
  url: hosts/tasq-hosts.yml
- type: TermsOfService
  url: https://www.tasq.ai/terms-of-use/
- type: PrivacyPolicy
  url: https://www.tasq.ai/privacy-policy/
- type: Blog
  url: https://www.tasq.ai/blog/
- type: DomainSecurity
  url: security/tasq-domain-security.yml
- type: Website
  url: https://tasq.ai/
apis: []
x-coverage:
  state: none
  reason: no-developer-program
  method: derived
  generated: '2026-10-21'
  generator: derive-coverage.py (pre-flight evidence)
  detail: the company publishes no developer documentation host and no machine-readable contract on its own domain
  evidence:
  - url: https://console.tasq.ai/mcp
    status: 403
  - url: https://tasq.ai/
    status: 200
x-enrichment:
  date: '2026-10-21'
  status: enriched
  artifacts_added: 9
  pass: modular-v1
  engine: identity=local,contract=local,surfaces=local,docs=local,regulatory=local

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