Weave

Weave is an engineering and AI intelligence platform designed to help software organizations measure, understand, and optimize how their engineering teams use AI throughout the software development lifecycle.

The platform connects engineering activity and AI usage data to show companies how much value they are getting from their AI investments. Rather than measuring AI adoption only by the number of prompts or tokens consumed, Weave evaluates cost, efficiency, code quality, engineering output, and other development metrics.

Weave analyzes engineering activity from prompt to production. It combines AI-specific metrics with established engineering frameworks such as DORA, SPACE, surveys, pull requests, commits, code reviews, deployments, and AI telemetry to provide a centralized view of engineering performance.

One of its main capabilities is engineering intelligence. Teams can analyze how engineers use AI, how AI affects their development output, and where bottlenecks exist in the software development lifecycle.

For example, an engineering manager could use Weave to determine which developers are getting the most value from AI coding tools, whether AI-assisted development is improving team output, and whether code quality is being maintained as AI usage increases.

The platform provides individual and team-level AI impact metrics. It can show measurements such as AI-assisted merges, changes in engineering output compared with a baseline, AI usage, code quality, and overall AI scores.

Weave also provides token intelligence. It analyzes AI prompts and token consumption to help organizations understand where their AI spending is going and whether that spending is producing useful engineering outcomes.

Instead of simply reporting that an organization spent a certain amount on AI models, Weave helps compare token consumption, model usage, cost, efficiency, and engineering results. The platform benchmarks this information against data from thousands of engineering organizations.

Another major component is the Weave Prompt Router. The router evaluates incoming AI prompts, classifies the requests, and routes them to an appropriate AI model based on factors such as quality, cost, and speed.

This allows engineering organizations to avoid automatically sending every request to an expensive frontier model. A simpler coding task can potentially be routed to a less expensive model, while more complex tasks can be sent to a more capable model.

The router is designed to learn from feedback at both the individual and organizational level. Developers can continue using their existing AI clients while the router operates as a proxy between those clients and AI model providers.

Weave provides a command-line installation process and supports AI providers including Anthropic, OpenAI, and Google. The company also makes its router available through an open-source GitHub repository.

The platform includes an AI agent called Wooly. Wooly analyzes the organization's engineering and AI data and provides recommendations about where teams could improve.

Users can ask Wooly questions such as which engineers are using AI most effectively, what strengths and weaknesses exist across teams, or where deployment cycles are getting stuck. Its answers are grounded in the organization's own records and include references to the underlying data.

Weave also includes code intelligence features focused on understanding AI-generated and AI-assisted software development. These capabilities cover areas such as code output, code quality, code reviews, AI health, and related engineering measurements.

The platform can also provide AI transformation and AI ROI insights. Organizations can use these measurements to understand whether investments in tools such as AI coding assistants are actually improving engineering productivity rather than simply increasing AI usage.

Weave is designed for engineering organizations ranging from startups to large enterprises. The company states that it is used by more than 500 engineering organizations and has analyzed more than 2 million pull requests, with data covering more than 25,000 engineers and 50+ AI tools.