Datacurve

Freemium

Datacurve는 장기 추론, 소프트웨어 엔지니어링, 데이터 과학을 위한 프런티어 AI 평가 인프라와 데이터셋을 구축합니다.

Datacurve란 무엇인가요?

Datacurve describes itself as the data engine for frontier AI and as infrastructure for research and data collection that helps teach models to handle long-horizon work. Its site focuses on the kinds of data needed for reasoning, software engineering, and data science, especially where judgment, iteration, and partial progress matter.

The product page presents Datacurve as a provider of datasets, benchmarks, evaluations, reinforcement learning environments, and expert trajectories. The public research page adds Deep SWE, a benchmark for long-horizon software engineering, which shows the company applying that approach to evaluating and measuring agent performance on more realistic engineering tasks.

Datacurve(으)로 무엇을 할 수 있나요?

Reinforcement learning environments

Construct durable environments for measuring agentic capability in work settings with long horizons, natural instructions, realistic tools, and domain-specific judgment.

Long-horizon tasks

Collect tasks that stretch over hours or days and preserve ambiguity, partial progress, and recovery so model training reflects real work instead of simplified prompts.

OTS datasets

Provide prebuilt datasets that are curated for signal, reviewed for quality, and structured to fit directly into a training stack.

Benchmarks and evals

Build benchmarks that aim to capture task-faithful, domain-sensitive lift rather than only optimizing a single numeric score.

Agent trajectories

Capture full expert execution traces, including tool calls, checks, pivots, and recoveries, so agents can learn process as well as outcome.

Supervised fine-tuning data

Create demonstrations for supervised fine-tuning using bespoke tooling that lets experts work naturally while preserving the operating judgment behind the work.

사용 사례

“Long-horizon software engineering”

Train or evaluate coding agents on longer engineering jobs where a correct outcome depends on many intermediate steps, tool uses, and recoveries.

“Data science and research workflows”

Assemble datasets and evaluations for model development in scientific or analytical settings where task structure and judgment matter.

“Agent training on realistic work”

Create realistic reinforcement learning environments that reflect the complexity of real systems, including partial progress and domain-specific decisions.

“Behavioral supervision for model tuning”

Use expert execution traces and demonstrations to teach models how experts work through tasks, not just what final answers look like.

자주 묻는 질문

What does Datacurve provide?

Datacurve positions itself as infrastructure for research and data collection for frontier AI. Its products page focuses on reinforcement learning environments, long-horizon tasks, OTS datasets, benchmarks, agent trajectories, and supervised fine-tuning data.

Which teams or domains is it aimed at?

The site highlights software engineering, data science, cyber security, machine learning, and research as domains it works in, with a stated emphasis on long-horizon reasoning and verifiable work.

Does Datacurve publish benchmarks and research?

The research page introduces Deep SWE, a benchmark for long-horizon software engineering, and says Datacurve publishes benchmarks, datasets, and evaluations from its own research.

Is pricing available on the site?

The pricing page in the collected evidence returns a 404, so the site does not currently expose a stable pricing page in the provided sources.

주요 정보

Category: AI data platform
Primary focus: Custom data for long-horizon reasoning, software engineering, and data science
Source domain: datacurve.ai
Public research: Deep SWE benchmark
Main product types: Environments, long-horizon tasks, OTS datasets, benchmarks, agent trajectories, SFT data

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