AI Roles · The method
An AI Role is a sales or marketing function that runs itself, with a person steering it. The reason most "AI employees" fail is that they are a clever prompt in a trench coat. We start with your process, then place small, accountable agents inside it, then put a person on the moments that matter. The model is the last thing we add, not the first.
Notice where the model sits: last. The system is the work. The AI is one part of it.
How we build it
These are not buzzwords. Each one is the answer to a way these systems go wrong in the wild, and the reason your AI Role holds up when the demo is over and real work starts flowing through it.
We map how the work actually gets done before a model is anywhere near it. The AI fits your process. You never bend your process around the AI.
We ship the smallest version that works, then add only what proves its worth. No speculative cleverness you have to maintain forever.
Many narrow agents that each do one thing well, not one big bot trying to do everything. Easy to inspect, easy to trust, easy to fix.
A short chain with a check after each step beats a long one that runs blind. Errors get caught early, before they compound into nonsense.
Anything that reaches a customer, or carries real risk, passes a human first. The AI does the volume. Your person keeps the judgment.
We ground every agent in your own clean data, and feed it only what the task needs. That is how you get relevance instead of generic slop.
The interfaces your agents use are built deliberately, with the same care as a product surface. Reliable inputs make reliable outputs.
We measure accuracy, watch every run, and put guardrails in before launch, not after something breaks. And it is all built into something you own.
Why not one big agent
Reliability is the per-step success rate raised to the length of the chain. Say each step works 95 percent of the time, which sounds excellent. Run 20 of those steps back to back with no checks, and the whole chain only works about 36 percent of the time. Errors do not add up. They multiply.
That is exactly why we never build one big agent. We keep the chains short, check the work between steps, and place AI only at the decision points. Drag the slider and watch it for yourself.
Reliability is the per-step success rate raised to the length of the chain. A long, unchecked chain collapses. Keep the chains short and check the work between steps, and it holds. This is why we never build one big agent.
The human gate
Anything customer-facing passes a person before it goes out. This is not a fallback we bolt on at the end. It is a gate we design into the build, on the one step that reaches a buyer. The agents do the volume work behind it. Your person reviews, edits and approves.
So quality cannot quietly collapse, your domain and brand stay safe, and nothing you would not have sent yourself ever reaches a customer.
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On your stack, your data
A product sold to ten thousand companies has to assume nothing about yours. We build the opposite: an AI Role wired into the tools you already run, grounded in your data, fitted to how your team actually works. That fit is the moat. It is also why this lives on your stack, not behind someone else's login.
Your CRM, your inbox, your warehouse, your tools. We build inside what you already use. No rip and replace, nothing for your team to migrate to.
Every agent reasons over your accounts, your voice, your history. That is what turns a generic model into a function that sounds like it works at your company.
The workflows, the agents and the data stay yours. You can run it on our infrastructure, or take it fully in-house. Either way it is never a black box you cannot leave.
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Book a 15-minute call. We will walk your process, find the first function worth turning into an AI Role, and show you how we would build it into the stack you already run.
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