83 Sciences

83 Sciences is an AI-native materials-discovery company (Y Combinator Summer 2026) that helps research labs unlock value from unpublished and discarded experimental data. Roughly 90% of experimental data never reaches publication; 83 Sciences works on-site with researchers to capture and structure that hidden data into a queryable database, then applies AI-powered analysis and generative models to learn from a lab's full experimental history — including failed experiments — to surface novel materials, co-authored manuscripts, and patents. Founded by researchers from MIT, Stanford, Harvard, and Columbia and based in San Francisco. The company currently ships no public API or developer surface; this profile tracks its identity for the API Evangelist network.

83 Sciences is profiled on the APIs.io network. Tagged areas include Company, Materials Science, Artificial Intelligence, Research Data, and Scientific Discovery.

🌐 Visit website 📡 Source on GitHub

4.6/100 minimal ▬ flat Agent 0/100 human only saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs

Company Materials Science Artificial Intelligence Research Data Scientific Discovery Life Sciences Machine-Learning

Kin Score

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

Composite quality — 4.6/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.0 / 17

Access Clarity 1.1 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.3 / 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/83-sciences: 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.

83 Sciences Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

Login

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Company 3

The organization behind the API

Website

Website

LinkedIn

Twitter

Twitter

Source (apis.yml)

aid: 83-sciences
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  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/83-sciences.png
name: 83 Sciences
description: 83 Sciences is an AI-native materials-discovery company (Y Combinator Summer 2026) that helps research labs unlock
  value from unpublished and discarded experimental data. Roughly 90% of experimental data never reaches publication; 83 Sciences
  works on-site with researchers to capture and structure that hidden data into a queryable database, then applies AI-powered
  analysis and generative models to learn from a lab's full experimental history — including failed experiments — to surface
  novel materials, co-authored manuscripts, and patents. Founded by researchers from MIT, Stanford, Harvard, and Columbia
  and based in San Francisco. The company currently ships no public API or developer surface; this profile tracks its identity
  for the API Evangelist network.
url: https://raw.githubusercontent.com/api-evangelist/83-sciences/refs/heads/main/apis.yml
x-api-posture: no-product-api
x-api-posture-basis: observed
x-api-posture-note: The company currently ships no public API or developer surface; this profile tracks its identity for the
  API Evangelist network.
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-17'
tags:
- Company
- Materials Science
- Artificial Intelligence
- Research Data
- Scientific Discovery
- Life Sciences
- Machine-Learning
tags_raw:
- Company
- Materials Science
- Artificial Intelligence
- Research Data
- Scientific Discovery
- Life Sciences
- Machine Learning
apis: []
common:
- type: DomainSecurity
  url: security/83-sciences-domain-security.yml
- type: Website
  url: https://83sciences.ai
- type: Login
  url: https://platform.83sciences.ai/
- type: LinkedIn
  url: https://www.linkedin.com/company/83sciences.ai
- type: Twitter
  url: https://x.com/83sciences
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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