Ship Faster, More Predictably: Engineering Insights Driven by Your Data

Frequently asked questions

What data sources does Optimal connect to?

We integrate with the tools your engineering team already uses. This includes major source code repositories (like GitHub, GitLab, Bitbucket) and popular project management systems (such as Jira, Azure DevOps, Linear, etc.). We focus on leveraging the data you already generate to provide insights.

How difficult is the setup and integration process?

Getting started is designed to be quick and straightforward. Typically, it involves authorizing secure, read-only access to your selected tools via standard methods (like OAuth). Most teams can be up and running in a few minutes, viewing initial insights, within a few hours, with clear documentation and hands on support available.

Does your platform store our actual source code?

Absolutely not. We prioritize your intellectual property and security. Our platform analyzes metadata about development activities (like commit timestamps, PR interactions, merge details) and project management data, not the underlying source code itself.

How do you ensure our engineering data is secure and private?

Security is fundamental to our platform. We use industry best practices like encryption (in transit and at rest), secure authentication, and role-based access controls. We are committed to data privacy standards, as well as our adherence to SOC2 standards.

Practically speaking, how does this help my team ship faster and more predictably?

Optimal connects the dots between your code activity and project progress. By visualizing metrics like PR Cycle Time and Deployment Frequency (DORA), analyzing sprint performance, understanding workload distribution, and tracking goals, you can pinpoint specific bottlenecks and inefficiencies in your workflow. Our AI-driven insights help you focus on the highest-impact improvements, leading directly to more efficient and predictable delivery cycles.

We use dashboards already. What makes Optimal different?

Standard dashboards often show isolated metrics. Optimal Insights provides context by correlating data across different tools (code, projects, CI/CD) to give you a holistic view of your engineering system. Features like the Activity View, Allocations, and AI-driven insights go beyond raw numbers to surface patterns, dependencies, and actionable recommendations that are harder to see otherwise. It's about understanding the why behind the numbers and knowing what to do next.

My engineers might worry about being "spied on." How should I approach this?

That's a valid concern. We strongly advise positioning Optimal as a tool for process and system improvement, not individual performance tracking. Use the data to facilitate conversations about removing roadblocks (e.g., "How can we shorten our PR review times?"), ensuring fair workload distribution, improving sprint planning, and celebrating team successes backed by objective data. Transparency about how the insights are used is crucial for building trust and focusing on collective improvement. Almost 80% of Optimal AI's customers give individual engineers access to the platform so that they can drive team and self improvement initiatives.

What kind of "actionable insights" does the AI actually provide?

Our AI analyzes trends and patterns across your integrated data sources to proactively flag potential issues or areas for improvement. Examples include identifying recurring sprint planning challenges, detecting anomalies in PR cycle times for specific repo areas, highlighting potential workload imbalances before they cause burnout, or suggesting correlations between certain activities and deployment success rates. The goal is to surface these signals so you can investigate and act faster.

Can we customize the metrics and reports to fit our specific team structure or processes?

Yes, while we provide standard, industry-recognized metrics (including DORA) and dashboards based on best practices, the platform offers flexibility. You can typically configure team structures, filter data by date ranges or projects, set custom goals, and adjust parameters to better reflect your unique engineering context.

How quickly can we expect to see meaningful results or ROI?

You'll likely gain valuable visibility and initial insights within days of connecting your tools – often highlighting immediate areas like long-running PRs or sprint volatility. Translating these insights into measurable improvements (like reduced cycle time or increased deployment frequency) typically follows as you implement changes based on the data, often within weeks or the first couple of months.