AI is already in your stack.
How hard is it working for you, and what's it costing you when you're not watching?
You already have AI in production. Do you know where, and what it's doing when you're not looking?
If your team is already using AI somewhere, what if the head start you need is buried in what they built?
If nobody has mapped what's actually deployed across the whole company, how would you know what's working, what's drifting, and what's costing more than it should?
If the tool your team quietly depends on tripled its price next quarter, or leaked data it shouldn't have had access to, would you catch it before it mattered?
If you could see the whole stack at once, sanctioned and shadow, what would you do differently this quarter?
What's the cost of waiting another quarter to find out?
Every week you don't have visibility, the shadow stack grows. Tools that were free last quarter start billing. Workflows that one person built quietly become load-bearing, and nobody documents them. The finance team sees a line item they can't explain. The security team finds an integration nobody approved.
None of this is urgent. Which is exactly why it's dangerous. When the tool your team quietly depends on triples its price, or leaks data it shouldn't have had access to, or stops working the week your board meeting lands, the fix is no longer cheap.
What could that cost you?
Ever hired a consultant who handed you a deck and disappeared?
Most AI consulting ends in one of two places. A slide deck nobody implements, or a dependency nobody can unwind. We built this practice to do neither.
Every engagement has a specific end state. A working system running in your stack. A runbook your team wrote with us. An operator on your side trained to own it, extend it, and fix it when something breaks. We leave documentation, not meetings.
Ask yourself this. If you hire a partner for AI operations, do you want to come back because you chose to, or because they made sure you couldn't leave? Capture isn't a relationship. It's a bill you can't stop paying.
What would it look like if you actually had a map of all of it?
Three offers, designed to meet you where you are. Each one earns the next.
The Audit
2 to 3 weeks • remote by default
We run a structured diagnostic. You and your team complete a guided operator questionnaire. We interview the people doing the actual work in video sessions, usually 4 to 8 of them at 30 minutes each. We map every workflow where AI or automation could create value. We classify each one by what level of solution it actually needs. Off-the-shelf tool, light integration, custom build, or leave it alone.
You walk away with a prioritized roadmap, a cost-benefit estimate for every opportunity, and a clear answer to the question you came in with. Where is AI worth it for us.
Travel available at cost when an in-person working session makes sense, held at a location we choose so the work stays focused.
The Sprint
4 to 8 weeks
Once you know what to build, we build it. Sprints take the top 1 to 3 opportunities from the audit and implement them end-to-end. Tool selection, integration, workflow redesign, team training, and measurement setup.
The Retainer
ongoing
After the sprint, we stay. We monitor what's running, optimize what's underperforming, catch drift before it breaks anything, and keep finding the next opportunity.
See what your LLM is actually costing you.
LLM Lens
open source • runs on your infrastructure
LLM Lens is the open-source token tracker we built for ourselves. Drop it in, see what every model call costs, and catch waste before it hits your bill. No signup. No data leaves your infrastructure.
You probably don't need more AI. You need someone who's lived inside the workflow mess and knows where the leverage actually is.
We built P&R Ventures because most growth-stage companies already have AI running. What they don't have is the time to keep up with what's deployed. We partner with leadership to map it, optimize it, and make sure every piece earns its keep.
Phillip Ross
Phil spent years in the middle of operational complexity, building and fixing the workflow systems that growing companies run on. He founded the practice after running his own automation stack at a growing logistics company and realizing the same playbook could work for every under-teched growth-stage company he talked to.
Richard Hanberry
Richard brings the architecture and engineering depth that turns a workflow diagnosis into a working system. He's led software and engineering teams, managed DevOps infrastructure, and is currently CTO of a tech startup he co-founded.
Together we diagnose the real friction, classify every opportunity by what level of solution it actually needs, and make sure the right fixes get implemented, adopted, and measured. Not just recommended.
Ready to find out what's actually running, and whether it's worth keeping?
Tell us a little about where you are. If it sounds like a fit, we'll set up a 30-minute discovery call at no cost. If it's not, we'll tell you that too and point you somewhere useful.