Shotwellai

Shotwell AI is a Y Combinator-backed startup building an observability and annotation layer for robotics training data. Its pipeline ingests raw teleoperation video, robot logs, and multimodal sensor streams, then uses models to watch every frame, segment continuous motion into discrete actions, and label each action against a customer's task definition and SOP rubric. Every label is quality-scored and returned in hours rather than weeks, producing dense, frame-accurate, training-ready datasets for robot manipulation, deformable/folding tasks, teleoperation episode QA, and vision-language-action (VLA) foundation-model post-training. As of enrichment the company exposes only a marketing website and a sales contact (hello@shotwell.ai); no public API, developer portal, documentation, or SDKs were found.

Shotwellai is profiled on the APIs.io network. Tagged areas include Company, Robotics, Data Annotation, Training Data, and Machine Learning.

Shotwellai’s developer surface includes support and 2 more developer resources.

🌐 Visit website 📡 Source on GitHub

8.3/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5

0 APIs

CompanyRoboticsData AnnotationTraining DataMachine LearningComputer VisionArtificial IntelligenceY Combinator

Kin Score

Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5

Composite quality — 8.3/100 · minimal

Contract Quality 0.0 / 25

Developer Ergonomics 0.9 / 20

Commercial Clarity 0.0 / 20

Operational Transparency 0.7 / 13

Governance 0.0 / 12

Discoverability 6.8 / 10

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 15

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

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Resources

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Operate 1

Status, limits, changes, and where to get help

Support

Support

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: shotwellai
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/shotwellai.png
name: Shotwellai
description: Shotwell AI is a Y Combinator-backed startup building an observability and annotation layer for robotics training
  data. Its pipeline ingests raw teleoperation video, robot logs, and multimodal sensor streams, then uses models to watch
  every frame, segment continuous motion into discrete actions, and label each action against a customer's task definition
  and SOP rubric. Every label is quality-scored and returned in hours rather than weeks, producing dense, frame-accurate,
  training-ready datasets for robot manipulation, deformable/folding tasks, teleoperation episode QA, and vision-language-action
  (VLA) foundation-model post-training. As of enrichment the company exposes only a marketing website and a sales contact
  (hello@shotwell.ai); no public API, developer portal, documentation, or SDKs were found.
url: https://raw.githubusercontent.com/api-evangelist/shotwellai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Robotics
- Data Annotation
- Training Data
- Machine Learning
- Computer Vision
- Artificial Intelligence
- Y Combinator
apis: []
common:
- type: Website
  url: https://shotwell.ai
- type: GitHubOrganization
  url: https://github.com/shotwellai
- type: Support
  url: mailto:hello@shotwell.ai
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-21'
  status: minimal
  artifacts_added: 0
  pass: local-v1