AI that catches revenue risk early, not another chatbot that summarizes your week
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 & predictive readiness assessment
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.
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.
Built for
Operations and revenue teams running on a CRM or operations platform where contracts, renewals, quoting, or forecasting have enough history to work with, roughly a year or more of reasonably consistent data. This applies across SaaS, industrial services, and asset-heavy sectors like oil and gas, anywhere recurring contracts and service work move through a system that’s supposed to track them. Wrong fit if you’re pre-revenue or your records are too thin to build on; we’d rather tell you that on the call than after you’ve 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.
Ready to see where the risk actually is?
Book a scoping call. Thirty minutes, no deck, no pressure. Worst case, you leave with a sharper picture of your own systems.