Datacurve is a San Francisco-based AI infrastructure company that positions itself as "the data engine for frontier AI," building high-quality, expert-annotated datasets, reinforcement-learning environments, long-horizon agent tasks, benchmarks, and supervised fine-tuning demonstrations used to train and evaluate frontier AI models. Its offerings include off-the-shelf and custom datasets, agent-trajectory capture (tool calls, pivots, recovery attempts), and domain-sensitive evaluations across software engineering, data science, cybersecurity, machine learning, and research. Datacurve is backed by Homebrew and publishes research such as the DeepSWE long-horizon coding benchmark. The company sells through direct engagement (contact@datacurve.ai) and currently exposes no public API, developer portal, or SDKs.
Datacurve is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine-Learning, Datasets, and Reinforcement Learning.
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.
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aid: datacurve
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: '2026-07-22'
method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/datacurve.png
name: Datacurve
description: Datacurve is a San Francisco-based AI infrastructure company that positions itself as "the data engine for frontier
AI," building high-quality, expert-annotated datasets, reinforcement-learning environments, long-horizon agent tasks, benchmarks,
and supervised fine-tuning demonstrations used to train and evaluate frontier AI models. Its offerings include off-the-shelf
and custom datasets, agent-trajectory capture (tool calls, pivots, recovery attempts), and domain-sensitive evaluations
across software engineering, data science, cybersecurity, machine learning, and research. Datacurve is backed by Homebrew
and publishes research such as the DeepSWE long-horizon coding benchmark. The company sells through direct engagement (contact@datacurve.ai)
and currently exposes no public API, developer portal, or SDKs.
url: https://raw.githubusercontent.com/api-evangelist/datacurve/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- homebrew
x-tier: stub
x-tier-reason: portfolio-lead
x-enrichment:
last_run: '2026-07-18'
status: no-public-api-surface
notes: Enrichment probed the site plus the well-known and package-registry surface. Datacurve has no public API, developer
portal, docs, SDKs/packages, or event surface, so most artifact types are N/A (not penalized). Only the domain-security
probe and an identity/discoverability pass yielded real data.
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-18'
tags:
- Company
- Artificial Intelligence
- Machine-Learning
- Datasets
- Reinforcement Learning
- Benchmarks
- LLM Training
- AI Infrastructure
- Data Annotation
tags_raw:
- Company
- Ai
- Machine Learning
- Datasets
- Reinforcement Learning
- Benchmarks
- LLM Training
- AI Infrastructure
- Data Annotation
apis: []
maintainers:
- FN: Kin Lane
email: kin@apievangelist.com
- FN: APIs.json
email: info@apis.io
common:
- type: Website
url: https://datacurve.ai
- type: Products
url: https://datacurve.ai/products
- type: Research
url: https://datacurve.ai/research
- type: Careers
url: https://datacurve.ai/careers
- type: DomainSecurity
url: security/datacurve-domain-security.yml
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