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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 Pull Sense?

Pull Sense is an AI-powered tool designed for code review that seeks to enhance development workflows by automating the scrutiny of pull requests on GitHub. It provides prompt and intelligent feedback on code changes by identifying potential bugs, security vulnerabilities, and optimization suggestions, thereby streamlining the review process while maintaining coding standards. Users can customize their experience by integrating their preferred AI models, such as Anthropic, OpenAI, or Deepseek, via API keys, granting them greater flexibility and control over the review activities. The application generates relevant inline comments directly within pull requests, offering practical advice without disrupting the current workflow. Teams can define and maintain their own coding standards through adaptable configuration options, promoting uniformity across their codebases. With an easy-to-follow setup, Pull Sense seamlessly connects with GitHub, allowing users to start reviewing code in a matter of minutes. Furthermore, its intuitive interface makes it accessible to both experienced developers and those new to the coding environment, ensuring everyone can leverage its capabilities efficiently. This accessibility not only enhances productivity but also cultivates a collaborative atmosphere within development teams.

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Integrations Supported

Braintrust
GitHub Actions
Jest
LangSmith
Langfuse
Vitest
promptfoo
pytest

Integrations Supported

Azure DevOps
Claude
Claude Duet
Claude for Legal
DeepSeek
GitHub
Jira
Linear
Microsoft Teams
OpenAI
Slack

API Availability

Has API

API Availability

Has API

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

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Garm Tech B.V.

Date Founded

2026

Company Location

Netherlands

Company Website

evalship.com

Company Facts

Organization Name

Pull Sense

Company Location

United States

Company Website

www.pullsense.com

Categories and Features

LLM Evaluation

Not specified

Categories and Features

AI Code Review

Not specified

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