Most US teams we talk to don't have an "AI problem." They have a repetition problem. The same follow-ups go cold every week. The same data gets re-keyed into three systems. The same research sits in a tab nobody closes. Those are exactly the jobs an autonomous AI agent is good at — repetitive, rule-based, and high-volume — and they are exactly what we build.
We're a remote studio, but we run delivery around US business hours, from the East Coast to the West. You get replies in your working day, not a 3 a.m. ping from a team on the other side of the world. And because deployments are self-hosted by default, your data stays in your own environment — which means US teams can keep everything on US infrastructure and out of someone else's training set.
We're not a chatbot shop. A chatbot answers; an agent acts — it pulls the data, sends the message, updates the record, and reports back. If a task can be described as a workflow, we can probably automate it. And we start every US engagement with a free agent blueprintthat scopes the highest-value automation before you pay a cent.
What US teams build with us
The work is the same everywhere — the framing is local. US teams we work with tend to be scaling fast and hiring slowly, so the agent has to earn its keep on day one:
- SaaS & VC-backed scale-ups — demo-request and trial agents that qualify and route inbound the minute it lands, so AEs work warm leads from New York to San Francisco.
- Digital & growth agencies — white-labeled delivery and reporting agents that let a Chicago or Austin shop add an AI service line without adding headcount.
- B2B revenue teams — pipeline agents that enrich the CRM, draft follow-ups, and book meetings across US time zones, from Pacific to Eastern.
- Founders & SMBs — inbox, research, and ops agents that reclaim 10–15 hours a week before the first hire.
Built for how US teams actually work
A few things matter specifically to US businesses, and we've built the engagement around them:
- US business hours, not just "remote." We run delivery and support aligned to US time zones, so a question asked at 10 a.m. Central gets answered the same morning.
- Your data stays in the US. Self-hosted, model-agnostic deployments mean your information lives in your own infrastructure. We never use your data to train someone else's model.
- Priced in USD, scoped as a project. No hourly drift, no surprise invoices. The free blueprint gives you a fixed scope and a fixed price in dollars before you commit.
- No retainer to start. We prove the agent on your real work first. You see the time saved before any larger commitment.
Whether you're a founder wearing every hat, an agency adding a white-label AI service, or a revenue team that can't hire fast enough, the model is the same: automate the repetitive work so your people focus on what moves revenue.
How AI agent development works
Every engagement follows the same structure, because structure is what keeps agent development from becoming a science project:
1. Free blueprint. We map your workflow, identify the highest-value automation, choose the right framework (Hermes, OpenClaw, or custom), and scope the build. You see exactly what the agent will do, what it will cost in USD, and how long it will take — before you commit anything.
2. Proof on real work. We don't build demos. We build the agent on your actual workflow, with your actual data, handling your actual edge cases — and you watch it process real work.
3. Measure and decide. We track time saved, accuracy, and escalation rate. You see the numbers, then decide whether to expand — based on results, not promises.
4. Scale. Add workflows, integrations, and agents. The architecture is designed to grow without re-architecting the system.
Frameworks: how we choose
We're not tied to one framework. We choose the one that fits your problem:
- Hermes — single agents with persistent memory. Best for inbox management, research, and reporting that should improve over time.
- OpenClaw — multi-agent orchestration. Best when the workflow spans many systems with handoffs between agents, like lead pipelines and support triage.
- Custom — bespoke builds on your data and model. Best when you need self-hosted deployment, audit logging, and compliance requirements off-the-shelf frameworks can't meet.
You don't need to know which you need. That's part of the blueprint — we assess the workflow, the integrations, and the governance needs, then recommend the right path.
What it costs — honestly
AI agent development cost depends on the workflow's complexity, the number of integrations, and whether you need a single agent or an orchestrated system. We don't publish a price list because every build is different, and a price list would be dishonest.
What we can tell you: the free blueprint gives you a fixed scope and a fixed price inUSD before any invoice. For context, comparable US AI agent development companies charge roughly $150–$250/hour for production builds. We scope the work as a project, not an hourly engagement, so you know the total cost upfront — and there's no retainer to start.