Pecan

Pecan AI is a predictive analytics platform that lets business teams generate machine-learning predictions without data-science expertise. Users describe a business question in natural language and Pecan builds, trains, and deploys predictive models for outcomes such as customer churn, lifetime value (LTV), lead scoring, demand forecasting, upsell and cross-sell, campaign ROAS, and fraud prevention. Predictions connect back to CRMs, marketing platforms, and BI dashboards through native integrations or an API. The company markets itself as a predictive AI agent built for business outcomes rather than raw models, and runs on AWS with ISO 27001 and SOC 2 Type II security certifications.

Pecan is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Predictive Analytics, Machine Learning, and Data Science.

Pecan’s developer surface includes documentation, engineering blog, getting-started guide, pricing, support, and 12 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

31.9/100 thin ▬ flat Agent 0/100 human only Front door AI 6/6 dominant Heartbeat 50/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessPaid

0 APIs

Company Artificial Intelligence Predictive Analytics Machine Learning Data Science Forecasting Business Intelligence

Kin Score

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

Composite quality — 31.9/100 · thin

Contract Quality 0.0 / 21

Developer Ergonomics 4.9 / 17

Access Clarity 14.5 / 17

Operational Transparency 0.3 / 11

Contract Governance 1.9 / 10

Discoverability 4.7 / 9

Regulatory Posture 3.3 / 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 28.6 — up to +14.3 composite points
Operational Transparency scores 2.6 — up to +12.7 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/pecan/apis.yml: open an issue on github.com/api-evangelist/pecan 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 →

Pecan API pricing

Prices published by Pecan, read 2026-10-07 from www.pecan.ai/pricing/.

Plan	Price
Starter	Talk to sales Includes: 2 monthly prediction batches; 500M rows storage; In-app support
Team	Talk to sales Includes: 10 monthly prediction batches; 2Bn rows storage; In-app + Essential enablement
Business	Talk to sales Includes: Custom monthly prediction batches; 5Bn rows storage; In-app + Pro enablement

Plan details, limits and sources →

Pricing Plans 1

Published pricing tiers and plan structures.

Pecan Plans Pricing

3 plans

PLANS

Security Posture 2

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

Pecan Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Pecan Trust Center

ISO 27001, SOC 2 Type II

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Login

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Conformance

Conformance

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 3

Authentication, authorization, and security posture

Compliance

Compliance

TrustCenter

TrustCenter

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

HelpCenter

HelpCenter

Support

Support

Commercial 4

Pricing, plans, and the legal terms of use

Plans

Pricing

TermsOfService

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: pecan
name: Pecan
description: Pecan AI is a predictive analytics platform that lets business teams generate machine-learning predictions without
  data-science expertise. Users describe a business question in natural language and Pecan builds, trains, and deploys predictive
  models for outcomes such as customer churn, lifetime value (LTV), lead scoring, demand forecasting, upsell and cross-sell,
  campaign ROAS, and fraud prevention. Predictions connect back to CRMs, marketing platforms, and BI dashboards through native
  integrations or an API. The company markets itself as a predictive AI agent built for business outcomes rather than raw
  models, and runs on AWS with ISO 27001 and SOC 2 Type II security certifications.
url: https://raw.githubusercontent.com/api-evangelist/pecan/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- ggv-capital
x-tier: stub
x-tier-reason: portfolio-lead
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: paid
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Paid
  confidence: low
  source:
  - plans
  - security
  generated: '2026-11-06'
  method: derived
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-08-08'
image: https://d11l08mk9rlgne.cloudfront.net/wp-content/uploads/2026/01/featured-image.png
tags:
- Company
- Artificial Intelligence
- Predictive Analytics
- Machine Learning
- Data Science
- Forecasting
- Business Intelligence
tags_raw:
- Company
- Ai
- Predictive Analytics
- Machine Learning
- Data Science
- Forecasting
- Business Intelligence
- Machine-Learning
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Plans
  url: plans/pecan-plans-pricing.yml
- type: Website
  url: https://www.pecan.ai/
- type: Documentation
  url: https://help.pecan.ai/en/
- type: HelpCenter
  url: https://help.pecan.ai/en/
- type: Blog
  url: https://www.pecan.ai/blog/
- type: GettingStarted
  url: https://help.pecan.ai/en/collections/3580335-getting-started
- type: GitHubOrganization
  url: https://github.com/pecan-ai
- type: Pricing
  url: https://www.pecan.ai/pricing/
- type: Login
  url: https://pecan.ai/login
- type: TermsOfService
  url: https://www.pecan.ai/license-agreement/
- type: PrivacyPolicy
  url: https://www.pecan.ai/privacy/
- type: LLMsTxt
  url: llms/pecan-llms.txt
- type: Conformance
  url: conformance/pecan-conformance.yml
- type: Compliance
  url: https://www.pecan.ai/platform/data-security/
- type: TrustCenter
  url: security/pecan-trust-center.yml
- type: DomainSecurity
  url: security/pecan-domain-security.yml
- type: Support
  url: https://www.pecan.ai/contact/
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 0
  pass: local-v1
x-merged-from:
- pecan-ai

Pecan API — frequently asked questions

How much does the Pecan API cost?

Pecan publishes its prices; its Starter plan is Talk to sales as of 2026-10-07. APIs.io lists 3 pricing plans for Pecan, with each plan's price, limits and inclusions under Pricing on this page.

Is the Pecan API ready for AI agents?

Pecan has a Kin Score of 31.9 out of 100 (thin) and an Agent Readiness score of 0.0 (human-only), scored by API Evangelist on 2026-11-06.

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Every operation they expose

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How their score was established

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