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What is evalship?

Evalship provides engineering teams with a robust framework for evaluating LLM-driven software by performing thorough audits of evaluation suites and scrutinizing pull requests. It carefully investigates scorers and judges for possible inaccuracies, identifies gaps in coverage, and points out evaluations that could lead to erroneous results, all while delivering insights complete with code references and suggestions for enhancements. The initial setup streamlines a coding agent's task of configuring GitHub Actions and creating a setup pull request. For future pull requests, Evalship assesses the results against the default branch, detecting regressions, flaky evaluations, and any changes to prompts or model calls that lack sufficient evaluation, while also providing a status check for merge gating. It accommodates results from a variety of supported test runners and LLM evaluation tools. Evaluations occur within the customer's GitHub Actions framework using their designated model API keys, with Evalship concentrating on reviewing the results rather than executing the evaluations. Furthermore, teams have the ability to review their evaluation history and receive weekly performance reports to monitor advancements and enhancements over time. This thorough methodology guarantees that maintaining software quality is prioritized at every stage of the development process, ultimately fostering a culture of continuous improvement.

What is doteval?

Doteval functions as a comprehensive AI-powered evaluation workspace that simplifies the creation of effective assessments, aligns judges utilizing large language models, and implements reinforcement learning rewards, all within a single platform. This innovative tool offers a user experience akin to Cursor, allowing for the editing of evaluations-as-code through a YAML schema, enabling the versioning of evaluations at various checkpoints, and replacing manual tasks with AI-generated modifications while evaluating runs in swift execution cycles to ensure compatibility with proprietary datasets. Furthermore, doteval supports the development of intricate rubrics and coordinated graders, fostering rapid iterations and the production of high-quality evaluation datasets. Users are equipped to make well-informed choices regarding updates to models or enhancements to prompts, alongside the ability to export specifications for reinforcement learning training. By significantly accelerating the evaluation and reward generation process by a factor of 10 to 100, doteval emerges as an indispensable asset for sophisticated AI teams tackling complex model challenges. Ultimately, doteval not only boosts productivity but also enables teams to consistently achieve exceptional evaluation results with greater simplicity and efficiency. With its robust features, doteval sets a new standard in the realm of AI evaluation tools, ensuring that teams can focus on innovation rather than logistical hurdles.

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Integrations Supported

Braintrust
GitHub Actions
Jest
LangSmith
Langfuse
Vitest
promptfoo
pytest

Integrations Supported

YAML

API Availability

Has API

API Availability

Pricing Information

$7.60/month
The Team plan is $7.60 per month during the launch offer, 60% off its $19 monthly price for the next 9 accounts. It includes 3 users and 2 private repositories. Additional users cost $2 per month each, and additional repositories cost $10 per month each. A 7-day free trial is offered. Public repositories are free.
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Garm Tech B.V.

Date Founded

2026

Company Location

Netherlands

Company Website

evalship.com

Company Facts

Organization Name

doteval

Company Website

www.doteval.com

Categories and Features

LLM Evaluation

Not specified

Categories and Features

LLM Evaluation

Not specified

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