AI Automation for Revenue and Operations
AI that takes real work off your revenue and operations teams
Most “AI” projects add a layer that reads your data and writes you a paragraph about it. That’s the easy part. The harder, more valuable part is knowing which signals in your systems actually predict a stalled deal, a missed renewal, or a contract quietly going sideways, and building the scoring logic that catches it early. We do that work first. The AI writing on top comes last, not first.
Why start here
Every operations and revenue team is being told to “do something with AI” right now. Most of what gets built is a summarizer bolted onto existing reports. That’s fine, but it’s not where the money is. The real value is in the scoring: which deals, contracts, or work orders are actually at risk, weeks before your forecast or your finance team would otherwise notice. We build that layer, then decide whether an AI-written summary on top is worth adding at all.
What we do
AI readiness assessment (start here)
Before anything gets built, we score what's actually feasible: which of your processes have enough clean history to predict on, where a straightforward rule beats a model, and what a build would actually need from your systems to work. You get a scored roadmap, not a vendor pitch.
Predictive risk scoring
Contract and renewal risk, stalled pipeline or work orders, and pricing or discount patterns that don't match policy, built as scoring logic on top of your existing systems, not a separate dashboard you have to remember to check.
AI agent and automation builds
Implementation work on agent and automation tooling where it closes a real workflow gap, whether that's routing, escalation, or flagging, not because a demo looked impressive.
AI governance and trust review
Before any model touches production data: what it's allowed to see, what a wrong output actually costs you, and how a person catches it before it reaches a customer or a leadership report.
Salesforce as the delivery platform
When the work runs on Salesforce we build natively, on a data model we have cleaned up and approvals that actually enforce something. We have delivered CPQ, Revenue Cloud, and ERP integrations for years, which is most of why the AI layer holds. The Salesforce services page covers the audit, Revenue Cloud, and integration work.
How we handle your data
By default, the scoring and prediction work runs on aggregated signals, patterns, counts, percentages, flags, rather than raw customer records, wherever that’s workable for the engagement. Where a project genuinely needs deeper access to do its job, we scope that explicitly with you up front rather than assuming it. If your security team wants to review the access model before anything starts, we’d want that too.
The AI readiness assessment
We inventory the recurring work in your revenue and operations systems and score each candidate on how much history it has, what it costs you today, and whether a plain rule would do the job. You get a ranked roadmap, including a frank list of what is not ready, and you keep it whether or not you build with us. We work from aggregated signals wherever that answers the question and scope anything deeper with you first.
Built for
Revenue, operations, and finance leaders at mid-market companies whose CRM, ERP, or service platform holds a year or more of reasonably consistent history on contracts, renewals, quoting, forecasting, or work orders. We have seen this in SaaS, industrial services, energy, and field services, and the pattern is the same anywhere recurring work moves through a system that is supposed to track it. You do not need to be on Salesforce. It is a wrong fit if you are pre-revenue, if your records are too thin to build on, or if you want a chatbot because the board asked for one. We would rather say that on the call than after you have paid for an assessment.
FAQ
Do you build custom models, or work with tools already built into our platform?
Both, depending on what the assessment finds. Sometimes a rules-based check outperforms a model, sometimes a custom scoring model is worth building. We tell you which before we build either.
What happens to our data during the assessment?
We scope data access with you up front for that specific engagement. By default we work from aggregated signals wherever that’s enough to answer the question.
Can this start as just the assessment, with no build commitment after?
Yes. The assessment stands on its own. You get the roadmap either way.
We're not in software, does this still apply to us?
Yes, if you run recurring contracts, service agreements, or repeat work through a CRM or operations system. The scoring logic adapts to what “renewal” or “risk” means in your business.
Do we need to be on Salesforce?
No. The scoring and automation work applies to any platform with enough history. If you are on Salesforce we build natively, and the Salesforce work on this site is often the groundwork we do first.
Ready to see what is worth automating?
Book a scoping call. Thirty minutes, no deck, no pressure. Worst case, you leave with a sharper picture of your own systems.