We build AI agents that do real operational work: campaign optimization, reporting, retrieval over internal data, and voice agents, all grounded in your own systems.
Why most AI pilots never reach production
The pattern is consistent. A pilot impresses everyone in a controlled demo, then dies on contact with reality. It has no access to the systems where the work actually happens, so a human still does the last mile. It cannot explain why it did something, so nobody will let it touch a live budget. And there is no evaluation harness, so no one can say whether this week is better or worse than last. The blocker is almost never model capability. It is grounding, permissions, and measurement.
What we build
- Agents wired into your warehouse, ad platforms, and internal tools with scoped, revocable permissions
- Retrieval layers so every answer is grounded in your documents and data rather than model memory
- Action boundaries with approval gates: the agent proposes, a human approves, or it acts within defined limits
- Full audit logging of every decision, its inputs, and its outcome
- Evaluation harnesses so quality is a tracked metric, not an impression
- Cost controls and monitoring, because token spend behaves like infrastructure spend
How we work
Pick a workflow with a measurable cost today, so success is provable
Ground the agent in real data and run it read-only alongside your team first
Compare its decisions against what your team actually did, and tune until they agree
Grant limited write access with approval gates, then widen the boundary as it earns trust
Typical stack
Frequently asked questions
A script follows a path you defined for conditions you anticipated. An agent decides which steps to take based on what it finds, which is valuable exactly where the decision space is too large to enumerate. If your process genuinely is a fixed decision tree, a script is cheaper, more predictable, and the right answer. We will tell you when that is the case.
Hard limits in code rather than instructions in a prompt. Agents get scoped credentials, explicit caps on what they can change, and approval gates on anything above a threshold. Every action is logged with its reasoning and is reversible. A prompt asking a model to be careful is not a control.
Work that is high-frequency, decision-heavy, and already data-driven. Campaign pacing checks, anomaly triage, report assembly and commentary, and retrieval over internal documentation all qualify. Poor candidates are low-frequency, high-stakes, judgment calls where the value is the human accountability.
To a degree, and this is the honest constraint. An agent reading unreliable data will confidently produce unreliable output. You do not need a perfect warehouse, but the specific data the agent depends on has to be trustworthy. That is often the first phase of the work rather than a prerequisite you handle alone.