How to Measure the Impact of GenAI Code.
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GeNAI Code Orchestration
Is your SDLC Gen AI ready?
Maximize your investment in Gen AI in less than 30 days with:
- Policy-as-code to de-risk how code is delivered
- Orchestration of Gen AI code through your dev pipelines
- Visibility into the adoption and impact of Co-Pilot and other Gen AI solutions
Empowering the world’s most innovative engineering leaders
Programmable Workflows
Orchestrate Gen AI code through your SDLC
Maximize the productivity of Gen AI and bot-created code using automation. Accelerate code approval, CI runtime and testing.
Increase merge frequency
Optimize your code review process by automatically routing Gen AI pull requests to the most relevant experts in your team.
Enforce policy standards
Maintain strict security requirements for PR Agents and automatically alert your OpsSec team when action is necessary.
Engineering Metrics & Insights
Measure the impact of your Gen AI investment
Benchmark your performance
Gain visibility into the impact Gen AI adoption is having on your delivery pipelines by using standard DevOps metrics to show the before and after.
See how GenAI is performing
Compare Gen AI workflows against your organization's baseline with dynamic label tracking.
Resource Allocation
Realize the ROI of your Copilot investment
Reshape your investments
Uncover how adoption of AI helps remove inefficiencies and reallocate resources into revenue generating activities.
Goals and Reporting
Ship code you can trust
De-risk adoption at scale
Turn best practices into working agreements. Ensure Gen AI code contributions receive the right level of review and attention using team level goals.
PROJECT FORECASTING
Deliver on-time, every time
Ship projects ahead of schedule
Optimize for delivery metrics that expose the true ROI of your Gen AI code enhancements.
Engineering Leader’s Guide to Predictable Project Delivery
Predictable project delivery is the ultimate engineering outcome. In this comprehensive guide, you’ll find:
How Syngenta became more predictable at scale
Guidance on optimizing engineering spend with resource allocation visibility
Tactical advice for setting data-backed goals that align stakeholders with clear reporting
“During a crunch time, the thoroughness of PR reviews is the first to suffer. The first SEI automation we implemented–and the one that brought the most value–was auto-assigning PRs to people that were familiar with that area of the codebase already. Now that process is streamlined and we know every PR is getting routed to the right person.”
Jeremy Lamit
Head of Engineering, Rabbit Care