A chatbot answers questions from a script. An agent does work. The difference is the whole point: a well-built agent reasons through a task end to end — takes in a request, gathers what it needs from your systems, drafts or decides, and follows through — using the tools and data your business already runs on, and it keeps going after the office closes.
We design each agent around a specific operation, not around a demo. Generic agents fail loudly the first time a real conversation leaves their training distribution; the ones we ship are scoped to the work in front of them and tested against the edge cases that work actually produces.
From $3,999 · typical builds $4K–$60K
Designed for the operation, not bolted on top
Before any prompt is written, we map the operation the agent is joining: what it needs to accomplish, what it can touch, what it must never do, and the tone it has to hold. That map is the difference between an agent that quietly absorbs a real workload and a chatbot that produces confident nonsense the moment it hits an unfamiliar case.
The result is an agent shaped like your business — your inquiries, your tools, your data, your voice — rather than a SaaS skin everyone else also bought.
Prompts and tool boundaries written per engagement
Every prompt the agent runs and every tool it can call is written, reviewed, and version-controlled — not pulled from a starter kit. When we connect the agent to your systems, each capability is scoped to exactly what the work requires. The agent does what it was authorized to do, declines what it was not, and logs both for audit.
We treat the prompt surface and the tool boundary the way we treat a security perimeter: small, deliberate, and known. That is what makes an agent safe to put in front of customers and connect to real data.
The right model, chosen against a real posture
The right model is the one that meets your accuracy, latency, privacy, and unit-cost requirements — not the one with the loudest launch. We map those requirements before the build, choose the model that fits, and document the reasoning so the choice stays defensible the next time the landscape shifts, which it will.
Adversarially tested before it goes live
Every agent ships through a written test plan: prompt-injection attempts, tool-misuse scenarios, jailbreak patterns, and the out-of-distribution inputs your real operation throws off. We do not consider an agent ready until it refuses the things it should refuse — with the same composure it brings to the work it does take on.
For the safest rollout, most clients start agent-in-the-loop: the agent does the heavy lifting and a person approves anything with consequences, until the error rate has earned more autonomy.