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RadiantOps matches your AI spend to the work that actually shipped. Matched by team, by dollar, every week.
PRs live in GitHub. Plans live in Jira. Token costs live in a billing dashboard nobody checks. Seat licenses live in a spreadsheet someone started in January. The information to answer every question on the right is already inside your org, scattered across systems that have never been connected.
Every quarter, the questions get sharper. The answers stay manual.
Trace your AI spend back to outcomes, and then see the story underneath: your teams are developing new ways of working with AI, and most of it is invisible. RadiantOps makes those patterns visible so you know where to invest.
RadiantOps matches token spend to engineering output continuously: by team, by workflow, by tool. You see which investments are returning and which aren't, without building the spreadsheet yourself.
3 categories drifted > 5 points this week.
Underneath the spend data, patterns are forming. Who's building leverage, where craft is developing, which teams found something that works. RadiantOps surfaces those patterns so you can invest in what's actually taking shape.
A written narrative, in plain English, drafted from real data. Not a changelog. Not another deck. The document that explains what your AI investment is producing to the people who need to know.
In April, the org committed 20 percent of engineering capacity to AI investment. We delivered 27. Most of the overshoot lived in two places: the agentic onboarding work in growth, and the search-ranking rewrite in discovery.
Two products that ship in May owe their schedule to the AI overshoot. The cost was four points of tech debt deferred to Q3.
Defer the dependency upgrade to Q3. Reallocate two engineers from infra to discovery for May.
The agent-pairing pattern that emerged in growth is now a documented practice. Owners listed inside.
GitHub and Jira, scoped to the org you choose. No writes, no agent installs, no code on your servers.
No new tools, no tagging, no time tracking. Engineers keep working. RadiantOps reads what they already produce.
Every merged PR, every resolved ticket, every token log, matched as the work lands. No manual cycles.
Copilot, Cursor, Claude, custom agents. Whatever your teams use. The analysis works across all of them.
Most AI-powered dashboards bolt a chatbot onto metrics. Ask a question, get a number back. RadiantOps works the other way. The AI reads across your entire engineering surface and tells you what it found. Patterns you didn't ask about, connections between teams that weren't visible from any single source. That's why a brief reads like it was written by someone who understands your org.
"Reviews under 18 hours. Pairs from different orgs. One agent run before the human review."
RadiantOps surfaces the patterns worth investing in: how your teams are adapting, what's working, where the leverage is. A growth narrative. Never a performance review.
If this is the kind of clarity you've been looking for, we'd like to hear from you. We're working with a small number of engineering organizations right now, and the conversation starts with an email.
We're working with engineering organizations, typically 100+ engineers, building with AI and looking for clarity on what's working.
Get in touch →You'll hear back within a day. No mailing list, no marketing emails.