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Machines on-call for humans
Machines on-call for humans
Agents that can fix your production issues need research-grade architecture around the model, and a platform that scales with your teams and investigations
4 common ways DIY builds break
People cost
Productizing demo agent takes a mix of AI and systems engineers, with a dedicated investment before reliably using it for on-call and incidents
Compute cost
Model and compute costs climb with every investigation. As volume grows, your subsidized token costs turn into seven digit invoices
Operating cost
Every model release, team expansion, or new use case needs rework to agent architecture, evals, and productionizing the agent
“We pull fewer engineers into war rooms, on-call is materially better.”
“I don't need more numbers or more data. What I need is a root cause.”
“It surfaced accurate root causes 72% faster than our teams, integrating cleanly into our existing stack.”
Put in your own numbers and see what it would cost to build and run this in-house
The full build-vs-buy decision, piece by piece
Model orchestration, context engineering, causal reasoning, and more
Resolve AI's Dave Lawson and Ed Li walk through the build-vs-buy decision, the five pieces you need, and the cost of running each stage in-house
We'll talk through what you're building, what it takes to reach real root cause at your scale, and where Resolve AI fits.
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