Canopy Labs

Canopy Labs was a customer analytics and predictive marketing SaaS company founded in 2012 in the Toronto, Ontario area (Thornhill) by Wojciech Gryc and Jorge Escobedo, with an additional office in San Francisco. A Y Combinator alumnus, it built machine-learning software that helped businesses model and predict customer behavior, optimize purchase funnels, and personalize marketing across the customer journey. The company raised seed funding from investors including BDC Venture Capital's IT Fund and Valar Ventures. In 2018 Canopy Labs was acquired by Drop Technologies (the customer-loyalty app Drop), after which the independent product, website (canopylabs.com), and any developer/API surface were wound down. This profile is retained as a historical company record; the entity no longer operates independently and publishes no live API.

Canopy Labs is profiled on the APIs.io network. Tagged areas include Company, Analytics, Customer Analytics, Predictive Analytics, and Machine Learning.

📡 Profile data on GitHub

0.0/100 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

Company Analytics Customer Analytics Predictive Analytics Machine Learning Marketing Software-as-a-Service Acquired Defunct

Kin Score

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

Composite quality — 0.0/100 · 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 0.0 / 9

Regulatory Posture 0.0 / 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
Developer Ergonomics scores 0.0 — up to +20.0 composite points
Access Clarity scores 0.0 — up to +20.0 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/canopy-labs/apis.yml: open an issue on github.com/api-evangelist/canopy-labs 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 →

Source (apis.yml)

aid: canopy-labs
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: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/canopy-labs.png
name: Canopy Labs
description: Canopy Labs was a customer analytics and predictive marketing SaaS company founded in 2012 in the Toronto, Ontario
  area (Thornhill) by Wojciech Gryc and Jorge Escobedo, with an additional office in San Francisco. A Y Combinator alumnus,
  it built machine-learning software that helped businesses model and predict customer behavior, optimize purchase funnels,
  and personalize marketing across the customer journey. The company raised seed funding from investors including BDC Venture
  Capital's IT Fund and Valar Ventures. In 2018 Canopy Labs was acquired by Drop Technologies (the customer-loyalty app Drop),
  after which the independent product, website (canopylabs.com), and any developer/API surface were wound down. This profile
  is retained as a historical company record; the entity no longer operates independently and publishes no live API.
url: https://raw.githubusercontent.com/api-evangelist/canopy-labs/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- a16z
x-tier: stub
x-tier-reason: portfolio-lead
x-status: defunct
x-status-detail: Acquired by Drop Technologies in 2018; independent operations and website (canopylabs.com) discontinued.
x-acquired-by: Drop Technologies
x-acquired-year: 2018
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-18'
tags:
- Company
- Analytics
- Customer Analytics
- Predictive Analytics
- Machine Learning
- Marketing
- Software-as-a-Service
- Acquired
- Defunct
tags_raw:
- Company
- Analytics
- Customer Analytics
- Predictive Analytics
- Machine Learning
- Marketing
- SaaS
- Acquired
- Defunct
- Machine-Learning
apis: []
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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