DataNeuron

DataNeuron provides a no-code platform for LLM customization, data annotation, and agentic workflows. It offers tools for data curation, fine-tuning, evaluation, model distillation, and classic NLP pipelines, enabling enterprises to build high-quality datasets and tailor large language models efficiently. The platform includes a knowledge hub, security features, and integrations for rapid deployment.

DataNeuron is profiled on the APIs.io network. Tagged areas include Artificial Intelligence, No-Code, LLM, Data Annotation, and Enterprise.

DataNeuron’s developer surface includes getting-started guide, support, engineering blog, and 5 more developer resources.

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6.3/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs

Artificial Intelligence No-Code LLM Data Annotation Enterprise

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 6.3/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 1.6 / 17

Access Clarity 0.9 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 3.8 / 9

Regulatory Posture 1.1 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 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 0 / 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

MCP Tool Quality 6 / 6

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
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
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)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

13 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
Access Clarity scores 5.3 — up to +18.9 composite points
Developer Ergonomics scores 9.5 — up to +18.1 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/dataneuron/apis.yml: open an issue on github.com/api-evangelist/dataneuron 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.

Dataneuron Domain Security

TLSv1.3 · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Access & Security 2

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 1

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Other 1

Properties that don't map to a standard resource type

Hosts

Hosts

Source (apis.yml)

aid: dataneuron
name: DataNeuron
description: DataNeuron provides a no-code platform for LLM customization, data annotation, and agentic workflows. It offers
  tools for data curation, fine-tuning, evaluation, model distillation, and classic NLP pipelines, enabling enterprises to
  build high-quality datasets and tailor large language models efficiently. The platform includes a knowledge hub, security
  features, and integrations for rapid deployment.
url: https://raw.githubusercontent.com/api-evangelist/dataneuron/refs/heads/main/apis.yml
x-type: company
x-source: harvest:g2-catalog
x-tier: profiled
x-tier-reason: enrichment pass 2026-10-16 (modular-v1; was harvest)
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  probe_state: unreadable
  source:
  - security
  generated: '2026-11-06'
  method: derived
specificationVersion: '0.20'
created: '2026-10-16'
modified: '2026-10-16'
tags:
- Artificial Intelligence
- No-Code
- LLM
- Data Annotation
- Enterprise
tags_raw:
- AI
- No-Code
- LLM
- Data Annotation
- Enterprise
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Hosts
  url: hosts/dataneuron-hosts.yml
- type: DomainSecurity
  url: security/dataneuron-domain-security.yml
- type: DomainSecurity
  url: security/dataneuron-domain-security.yml
- type: Website
  url: https://www.dataneuron.ai/
- type: GettingStarted
  url: https://www.dataneuron.ai/book-demo
- type: Support
  url: https://www.dataneuron.ai/contact
- type: Blog
  url: https://www.dataneuron.ai/blog
- type: PrivacyPolicy
  url: https://www.dataneuron.ai/privacy_policy
apis: []
x-coverage:
  state: unreadable
  reason: js-rendered-docs
  detail: The provider’s documentation site renders via JavaScript and provides no machine‑readable API specification.
  evidence:
  - url: https://dataneuron.ai/
    status: 200
  checked: '2026-10-16'
x-enrichment:
  date: '2026-10-16'
  status: enriched
  artifacts_added: 5
  pass: modular-v1
  engine: identity=local,contract=local,surfaces=local,docs=local,regulatory=local

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