A seller used to assemble the account brief. Soon she checks the one an agent built, fixes what's wrong, and spends the hour with the customer. That shift is the whole game, and it only works when the agent reads one version of the truth, passes its evals, and stays inside its guardrails.
We build all three, and we teach your people to operate them. Our team ran data, AI, and GTM at Salesforce, HubSpot, Adobe, and Procore. We've seen plenty of pilots crush it in a conference room and retire to a farm upstate the week they met the CRM.
For CROs, CTOs, CDOs, and RevOps leaders at software companies who are done funding demos and want something their team runs on Monday.
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Your CRM agrees. Billing says it's four. Marketing has it as "ACME, Inc." with a lead score of 97, and finance stopped asking in 2023. We build the context layer that settles it for people and agents alike.
See Context and data foundationThey arrive with notes, a theory, and mild despair. We turn the prep into an agent the rep operates, fix the handoffs between teams, and give sellers their afternoons back.
See GTMOne is an HR chatbot with the confidence of someone who has never read the handbook. We help you find the three, fund them properly, and give the chatbot a dignified send-off.
See AI StrategyA CSM used to hunt for churn signals. Now he reviews the accounts an agent flagged, decides who to call, and logs the decision so the model learns. A RevOps analyst used to rebuild the forecast by hand. Now she checks the one the agent assembled and chases the two numbers that moved. People move up a level: from doing to directing, reviewing, and deciding. That shift only works when the agent reads one version of the truth, passes its evals, and stays inside its guardrails. We build all three, and we train the people who run them.
How the context layer worksThe people in the first meeting are the people writing the code. You see a working version on your own data in the first weeks, and a demo every week after. If something isn't working, you'll know before the invoice does.
We do the revenue engine: selling, GTM planning, RevOps, customer success, and the data underneath them. It's a short menu, and everything on it is something we used to run.
Every agent we ship comes with the same five things, whether it's the first or the fifteenth:
That's why the second agent costs less than the first, and the tenth is boring in the best way.
What we do
Which AI work deserves money, in what order, and who owns it.
The context layer, the agents on top of it, and the tests that let your risk team say yes.
Selling, customer success, GTM planning, and revenue operations, where we've spent our careers.
Monitoring and improvement after launch, so the agent you approved in March still behaves in September.
Every company has a spreadsheet that runs it. It's called FINAL_v7_REAL, it has survived two reorgs and a CFO, and exactly one person knows what column AF does. Before we build anything, we sit with the people who keep it alive and map where the work stalls. Sometimes the answer is an agent. Sometimes it's deleting three steps and a standing meeting. We'll tell you which, even when the cheaper answer means less work for us.
See how we workWe built data and AI inside Salesforce, HubSpot, Adobe, and Procore. We've owned the forecast, the data platform, and the 7am exec review where the dashboard and the forecast disagreed and both had a VP defending them. That history is why we build things people open on Monday morning. It's also why we ask annoying questions about definitions in week one.
Meet the teamSoftware companies from growth stage to enterprise, and the private equity firms that back them. We've spent our careers inside the systems these teams run, which is why the first conversation usually starts with your data instead of ours.
Tell us in two sentences. Bonus points if you name the spreadsheet. We'll tell you honestly whether we can help, and if we're the wrong fit we'll point you to someone better.
Tell us what's stuck (2-minute form)