We build AI agents that do real operational work: reading intake, booking and following up, watching for problems, and reporting on the business. They work inside your systems, escalate to your team, and log every action.
Every operation has people re-keying invoices, chasing confirmations, checking dashboards for problems, and building the same report every Monday. It is necessary work, and it is not what you hired them for.
Generic AI tools do not fix this. A chat window your team has to remember to open does not enter the invoice, call back the customer, or catch the late load at 2am.
Agents do. They own a specific job, work directly inside your systems, and hand off to a person whenever a case falls outside the limits you set.
Wait to be asked. Live in a separate tool. Suggest, but never finish the job. No record of what they did or why.
Pick up work as it arrives. Act inside your systems. Escalate exceptions to the right person. Log every action with its reason.
Each agent is shaped around one job in your operation. Most clients start with one and add more as the savings show up.
Stop paying people to re-key data. Agents read invoices, orders, referrals, and contracts as they arrive and enter them into your system, with human review only for exceptions.
Agents handle booking, reminders, follow-up, and routine questions over SMS, email, and web chat, using your rules, your calendar, and your tone.
Agents watch your operation around the clock, catch problems early, and take the pre-approved action, or draft one for a person to approve.
Ask your operation questions in plain English and get cited answers in seconds. Agents also write the daily and weekly briefs your team would otherwise assemble by hand.
Agents earn autonomy on your real work. You decide how far each one goes, and you can dial it back at any time.
We sit with your team, measure where manual hours go, and pick the workflow with the best mix of volume, simplicity, and payoff.
The agent runs alongside your team on live work without changing anything. You see its accuracy before you trust it with anything.
The agent prepares the work and a person approves it with one click. Savings start here, and approvals become training data.
Routine cases run on their own within the limits you set. We keep monitoring, tuning, and adding agents as the wins stack up.
Every agent ships with explicit permissions, approval thresholds, and hard limits. Every action is explainable and reversible. Switch AI providers without rewriting code, and prove governance to any auditor.
A chatbot answers questions. An agent does a job: it reads incoming work, takes actions in your systems, and hands off to a person when something falls outside its limits. Every agent we build has a defined role, defined permissions, and a record of everything it has done.
Yes. Most agents connect to the systems you already run through APIs, email, and file drops. You do not need to rebuild anything to start. If your current tools make automation impractical, that is when we build custom software underneath, and we will tell you plainly if that is the case.
Agents start in shadow mode on real work, so you can measure accuracy before they change anything. From there they move to drafting for approval, then to handling routine cases on their own within limits you set. Low-confidence cases always go to a person, and every action is logged, explainable, and reversible.
We architect for zero single points of AI failure. Agents can run on Anthropic (Claude), OpenAI, Google (Gemini), or local models, and fail over between them automatically. You are never dependent on one provider's uptime, pricing, or policy changes.
Yes. Agents can run on local models inside your infrastructure so sensitive data never leaves your control, with full audit trails for HIPAA and other compliance requirements. For less sensitive work, cloud providers offer cost advantages while keeping the same governance.
A first production agent typically runs $30,000-$75,000 depending on the workflow and the systems involved. Most clients start with their highest-volume, lowest-complexity workflow, prove the savings, and add agents from there. Ongoing model costs vary by usage, and we optimize for cost at every layer.
In 30 minutes we'll find the workflow costing you the most hours, and show you what an agent would save before you spend a dollar.