Dognosis

Dognosis is a Bengaluru, India deeptech company building olfaction AI that pairs trained detection dogs with machine learning to find disease from a single breath sample. Its product, BreathEasy, is a non-invasive breath test that produces a VOC-based cancer risk score for clinicians; a Phase 2 study published in the Journal of Clinical Oncology reported over 90% accuracy across seven cancer types, including at early and treatable stages. Dognosis is a portfolio company of Prosus Ventures. No public developer API is published today; this API Evangelist profile tracks the company's digital, discovery, and security surface (an llms.txt is served; no /.well-known discovery documents are published).

Dognosis is profiled on the APIs.io network. Tagged areas include Company, Health, Healthcare, Diagnostics, and Cancer Detection.

🌐 Visit website 📡 Source on GitHub

4.1/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs

Company Health Healthcare Diagnostics Cancer DetectionBreath AnalysisOlfaction AIDeep Tech

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 4.1/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.0 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.9 / 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

Regulatory Posture applies to this provider. Its tags matched the Health 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/.

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 github.com/api-evangelist/dognosis: open an issue to ask a question, or submit a pull request to add artifacts. 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.

Dognosis Domain Security

TLSv1.3 · HSTS

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

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: dognosis
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/dognosis.png
name: Dognosis
description: Dognosis is a Bengaluru, India deeptech company building olfaction AI that pairs trained detection dogs with
  machine learning to find disease from a single breath sample. Its product, BreathEasy, is a non-invasive breath test that
  produces a VOC-based cancer risk score for clinicians; a Phase 2 study published in the Journal of Clinical Oncology reported
  over 90% accuracy across seven cancer types, including at early and treatable stages. Dognosis is a portfolio company of
  Prosus Ventures. No public developer API is published today; this API Evangelist profile tracks the company's digital, discovery,
  and security surface (an llms.txt is served; no /.well-known discovery documents are published).
url: https://raw.githubusercontent.com/api-evangelist/dognosis/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- prosus-ventures
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-18'
tags:
- Company
- Health
- Healthcare
- Diagnostics
- Cancer Detection
- Breath Analysis
- Olfaction AI
- Deep Tech
tags_raw:
- Company
- Health
- Healthcare
- Diagnostics
- Cancer Detection
- Breath Analysis
- Olfaction AI
- Deeptech
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/dognosis-domain-security.yml
- type: LLMsTxt
  name: llms.txt
  url: llms/dognosis-llms.txt
- type: Website
  url: https://dognosis.tech
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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