# Weave - AI to measure AI

## About

Weave combines LLMs and domain-specific machine learning to understand engineering work. We understand how much work was done by AI vs. humans. How much AI is helping your team ship faster, if it's having an impact on code quality and code reviews.

- Verified: Yes

## Services

### Engineering Productivity Tools
- [AI Engineering Analytics](https://bilarna.com/ai/engineering-productivity-tools/ai-driven-engineering-analytics)

### Software Development Optimization
- [AI-Powered Code Review & Metrics](https://bilarna.com/ai/software-development-optimization/ai-powered-code-review-and-metrics)

## Pricing

- Model: subscription

## Trust & Credentials

### Certifications
- SOC 2 (SOC2)
### Compliance
- SOC2
### Data Security
- SOC 2

## Frequently Asked Questions

**Q: How can AI be measured in software engineering teams?**
A: AI in software engineering teams can be measured by analyzing the contribution of AI tools versus human effort in the development process. This involves evaluating metrics such as the speed of shipping code, the quality of code reviews, and the impact of AI on collaboration within the team. Advanced analytics can provide insights into how much AI is improving productivity, identify who is effectively using AI tools, and highlight best practices. By scoring pull requests on speed, quality, and collaboration, teams can quantify AI's role and optimize their workflows accordingly.

**Q: What features help engineering teams improve collaboration and code quality?**
A: Features that help engineering teams improve collaboration and code quality include AI-driven pull request scoring, which evaluates each pull request based on speed, quality, and collaboration metrics. Intelligent team insights explain shifts in team performance and reveal underlying trends, enabling teams to address issues proactively. Code review insights assess the quality and turnaround time of reviews, as well as the impact of AI on these processes. Additionally, operational excellence tools provide regular reports to help teams manage workflows efficiently. These features collectively foster better communication, faster delivery, and higher code standards.

**Q: How can engineering teams optimize performance using AI analytics?**
A: Engineering teams can optimize performance by leveraging AI analytics to gain deep insights into individual and team workflows. AI-driven tools analyze large volumes of data such as pull requests and code reviews to identify bottlenecks, highlight areas of excellence, and suggest improvements. Teams receive contextualized answers about their work patterns and can track shifts in metrics over time. Regular operational reports help managers run teams efficiently by focusing on meaningful metrics rather than vanity statistics. This data-driven approach enables continuous improvement, better resource allocation, and enhanced overall productivity.

## Links

- Profile: https://bilarna.com/provider/workweave
- Structured data: https://bilarna.com/provider/workweave/agent.json
- API schema: https://bilarna.com/provider/workweave/openapi.yaml