Platform
Take every AI agent from pilot to production
Connect your systems, describe the process in plain English, test it on real cases, and promote it with a check at every gate. Every run is on record, and every cost is tracked.
Get a demo
Runs inside the systems you already use
Sometimes, putting AI to work in the enterprise feels like swimming against the tide
The demo works. Then security, finance, and the systems of record get involved, and every new agent starts the same fight from scratch.
Put an agent into production in four steps
Connect your systems once
Link NetSuite, Salesforce, Workday, and the rest with scoped credentials. Every agent after that inherits the same reviewed connections.
Describe the process in plain English
The team that runs the process writes the agent. Where a step has to be exact, it is, and every change is a version a reviewer can read.
Test it on last month's cases
Run the agent on real cases before it goes live. If it passed last week, it still passes today, and every change runs the same suite.
Promote it with a check at every gate
Dev, staging, production. A person signs off at each gate, and cost and accuracy stay on screen as the agent spreads from one team to the whole company.
Runs where your data lives
Deploy Dynamo in your own cloud or on your own servers. Your data stays where it is, and traces go to the observability stack you already run.
- Your own servers
Every run is on the record, from the first step to the last
A full record of every run
See what each agent read, what it wrote, which model it used, and what every step cost. When an auditor or a controller asks what happened, the answer is one click away.
Run #4,812 27 steps · $0.41
See what every agent costs, and what it's worth
Dynamo research
What a correct answer really costs, model by model
Two models with the same accuracy can cost ten times apart per correct answer. See how the frontier models compare on real enterprise work.
Get the paperCost per correct answer on enterprise tasks
Learn more about putting agents into production
Accuracy is the wrong number to pick a model on
Two models that score the same on a benchmark can cost ten times apart per correct answer. Here is the number a finance team should look at instead.
Why we started Dynamo, and why we started with finance
Every company we talked to had built an agent that worked in the demo. None of them had one in production.
Why most enterprise AI stalls before production
The proof of concept is the easy part. The last two months, where security, permissions, and the systems of record get involved, are where projects go quiet.
Get started today
Ready to see Dynamo run one of your processes? Book a demo with our team.