ABOUT

The instinct came first

Some people are born tinkerers. The kid who takes apart the remote control not to break it, but to understand how it works. The one who reverse-engineers a toy to see why it does what it does — then puts it back together better. That instinct never went away. It just found bigger things to take apart.

CHS Consulting was built on that same impulse: look at a system that isn't working, figure out why, and fix it. The systems got more complex — from gadgets to revenue processes to enterprise technology stacks — but the diagnostic approach never changed. Understand it first. Then build.

Three generations of selling

We come from a family where selling wasn't a career — it was the family business. Three generations of understanding that every sale is a relationship, every pipeline is a process, and every number on a forecast is either a commitment or a guess. That background shapes everything we do.

It's why we don't just look at your CRM configuration — we look at how your sellers actually work. It's why we don't optimize dashboards in isolation — we trace a deal from first touch to close and find where the process actually fails. The technology is the instrument. The sales instinct is the strategy.

Human judgment, AI horsepower

AI is the most powerful tool to hit revenue operations in a generation. It can research prospects in seconds, enrich data at scale, draft personalized outreach, score leads, identify intent signals, and automate workflows that used to eat hours of a rep's week. We use it aggressively — and we think any team that isn't is leaving money on the table.

But here's what AI can't do: it can't diagnose why your pipeline stalls at Stage 3. It can't tell you that your reps are sandbagging because the comp plan punishes them for accurate forecasting. It can't look at a broken handoff between marketing and sales and understand the politics underneath it. It can't decide which problem to solve first.

That's the human intelligence layer. The judgment that comes from years of carrying a bag, managing sellers, and sitting in the room when a forecast goes sideways. AI executes at speed and scale. The strategist decides what to point it at and when to override it.

How we use AI — specifically

This isn't abstract. Here's what AI-powered revenue operations looks like in practice:

Every tool and workflow we build follows the same principle: automate the work humans shouldn't be doing manually, and keep human judgment in the loop where it matters most. We don't just "implement AI" — we design systems where AI and people each do what they're best at.

The tools on our Projects page are proof of this approach. Each one was designed by a human who understood the problem, then built with AI as the accelerant. That's the methodology we bring to every client engagement.

From the field to the systems behind it

The path to revenue operations wasn't a straight line — and that's the point. We've worked in environments where decisions are life-or-death and where they're quarter-over-quarter. We've carried a bag, managed sellers, rebuilt pipelines, implemented CRMs, and automated workflows that were drowning teams in manual work. The range matters because revenue problems don't come from one place.

What ties it all together is the diagnostic approach: walk in, observe how the system actually works (not how the playbook says it should), find the breakdown, and fix it. Sometimes that means coaching a sales manager on pipeline reviews. Sometimes it means re-architecting a HubSpot instance. Sometimes it means building a tool from scratch because nothing on the market solves the actual problem.

Where we've worked

Revenue problems look different on the surface but break the same way underneath. We've diagnosed and fixed them across industries:

The pattern recognition across industries is a feature, not a bug. The best solution to your pipeline problem might come from an approach that works in a completely different vertical.