Quris website screenshot

Quris

Quris-AI is a Bio-AI company building what it describes as pharma's first AI safety-prediction platform, aiming to predict which drug candidates will be safe in humans before they reach clinical trials. Its approach couples machine learning with "Patients-on-a-Chip" technology — miniaturized, organ-representative human tissue models that generate automatically-tagged biological data used to train classification algorithms for drug-safety prediction. The company targets the roughly 89% of drugs that fail in clinical trials, seeking to de-risk and accelerate pharmaceutical development. Based in Boston and Tel Aviv, Quris was co-founded with pioneers including Moderna co-founder Robert Langer and Nobel Laureate Aaron Ciechanover. Quris is backed by SoftBank Vision Fund and is profiled in the API Evangelist network as a company lead.

Quris is profiled on the APIs.io network. Tagged areas include Company, Health Tech, Bio-AI, Drug Discovery, and Drug Safety.

Quris’ developer surface includes engineering blog, support, and 4 more developer resources.

7.6/100 minimal ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →
0 APIs
CompanyHealth TechBio-AIDrug DiscoveryDrug SafetyClinical TrialsPharmaceuticalsArtificial IntelligenceBiotechnology

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0
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/.
Show this score on your site. Kin Score badge preview for Quris It updates itself when the score changes. Paste this where you link to your API docs:
<a href="https://apis.io/providers/quris/"><img src="https://apis.io/badge/quris/card.svg" alt="Quris on APIs.io — Kin Score by API Evangelist"></a>
Other styles, a README pill and a shields.io endpoint are on the badge page.
Raise your score.
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):
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/quris/apis.yml: open an issue on github.com/api-evangelist/quris 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 →

Standards implemented 1

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

measured — observed by APIs.io (wix)

Security Posture 1

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

Quris Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Access & Security 1

Authentication, authorization, and security posture

Operate 1

Status, limits, changes, and where to get help

Commercial 1

Pricing, plans, and the legal terms of use

Company 3

The organization behind the API

Source (apis.yml)

apis.yml Raw ↑
aid: quris
name: Quris
description: Quris-AI is a Bio-AI company building what it describes as pharma's first AI safety-prediction platform, aiming
  to predict which drug candidates will be safe in humans before they reach clinical trials. Its approach couples machine
  learning with "Patients-on-a-Chip" technology — miniaturized, organ-representative human tissue models that generate automatically-tagged
  biological data used to train classification algorithms for drug-safety prediction. The company targets the roughly 89%
  of drugs that fail in clinical trials, seeking to de-risk and accelerate pharmaceutical development. Based in Boston and
  Tel Aviv, Quris was co-founded with pioneers including Moderna co-founder Robert Langer and Nobel Laureate Aaron Ciechanover.
  Quris is backed by SoftBank Vision Fund and is profiled in the API Evangelist network as a company lead.
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:
  - security
  generated: '2026-11-06'
  method: derived
image: https://www.quris.ai/favicon.ico
url: https://raw.githubusercontent.com/api-evangelist/quris/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- softbank-vision-fund
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Health Tech
- Bio-AI
- Drug Discovery
- Drug Safety
- Clinical Trials
- Pharmaceuticals
- Artificial Intelligence
- Biotechnology
apis: []
common:
- type: DomainSecurity
  url: security/quris-domain-security.yml
- type: Website
  url: https://www.quris.ai
- type: About
  url: https://www.quris.ai/aboutus
- type: Blog
  url: https://www.quris.ai/news
- type: PrivacyPolicy
  url: https://www.quris.ai/privacypolicy
- type: Support
  url: https://www.quris.ai/contact
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-20'
  status: minimal
  artifacts_added: 1
  pass: local-v1

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