Analytics

Where monitoring tells you whether Syntic Code is healthy and costs tell you what it spends, analytics tells you who is actually getting value from it. For a leader trying to justify or expand a rollout, this is the most important lens: it shows where adoption is real, where it has stalled, and which teams have turned the agent into part of how they work.

What team analytics reveal

Useful analytics answer questions about people, not just packets. How many developers used Syntic Code this week, and is that number growing. Which teams lean on it heavily and which have barely started. Is usage concentrated in a few power users or spread across a group. Are people running short interactive sessions, long refactors, or automated jobs. These patterns tell you whether adoption is broad and durable or fragile and dependent on a handful of enthusiasts.

Where the data comes from

Because Syntic Code talks to the model over the network, an LLM gateway is the ideal place to gather adoption data, since every request carries the identity or team tag you attach and lands in one aggregated view. Your Syntic AI account’s usage view provides an organization-level summary when no gateway is in place. Aggregate to the team level for reporting and be deliberate about individual-level data, treating it as a way to find who needs support rather than a productivity scorecard, and follow your privacy obligations.

Acting on what you learn

Analytics is only worth collecting if it changes what you do. Teams with low adoption are candidates for targeted enablement, such as a champion or a focused workshop, rather than a broad push. Heavy, healthy usage is a signal to expand access or invest in more internal plugins. Pair the numbers with qualitative feedback so you understand the why behind a trend, and revisit the dashboards on a regular cadence so decisions about the rollout are grounded in evidence rather than anecdote.