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Description

Weave is an engineering intelligence platform that helps organizations measure software delivery performance, developer output, code quality, AI adoption, and the economics of AI-assisted development. It analyzes engineering activity from AI prompts and token consumption through commits, pull requests, reviews, deployments, and production outcomes. The platform combines AI-specific measurements with established engineering frameworks such as DORA and SPACE to provide a unified view of software development performance. Token intelligence shows how spending is distributed across AI models, providers, engineers, and development tools while evaluating efficiency and quality alongside raw consumption. Organizations can use these metrics to compare AI-assisted output with historical baselines and identify where AI is contributing to measurable improvements or unnecessary costs. Weave Router automatically classifies prompts and sends them to models selected for an appropriate balance of cost, latency, and output quality. The router can work with AI providers including Anthropic, OpenAI, and Google while learning from feedback at both the user and organizational level. Wooly provides an AI interface for asking questions about engineering performance, AI usage, deployment bottlenecks, and team effectiveness using connected internal records as evidence. Weave is intended for engineering executives, development managers, platform teams, AI transformation leaders, and enterprises seeking greater visibility into the productivity and financial impact of AI coding tools.

Description

Unify your code, tickets, and additional resources for comprehensive visibility in software development, all centralized in a single platform. You can track aspects like scrum sprint completions, accuracy of estimates, lead times, and kanban work-in-progress, among others. Additionally, manage development time, cost capitalization, R&D credits, and overall project completions seamlessly. You can monitor bug-related metrics such as Service Level Agreements (MTTR), rate of change failures, thoroughness of reviews, total bug load, and beyond. Also, assess pull request cycle times, ticket lead times, time spent in various statuses, context switching, and flow efficiency, to gain deeper insights. All pre-built reports utilize the minQL query language and are completely customizable. With access to any field from any data source, you can develop tailored metrics using advanced formulas, which include custom event cycle times. Establish objectives and monitor your advancement toward reliable software delivery through minware’s extensive collection of scorecard metrics. Evaluate best practices related to code and tickets, such as the use of pull requests, detailed code reviews, and linking branches to tickets for optimal project management. Furthermore, the platform empowers teams to enhance their workflows and drive continuous improvement in software development practices.