We build AI Agents for businesses that can plan tasks, use your real business tools, make calls, qualify leads, schedule appointments, send follow-ups and complete workflows. Every action is logged, controlled and designed to involve a human when a decision carries real risk.
Where agents differ
An AI Agent that can act on your business systems needs the same reliability as production software. We build with permissions, rate limits, audit trails, rollback options and testing before the agent can take real actions.
We define each agent by the decisions and tasks it is allowed to handle. Focused agents that reliably complete valuable work are better than complex systems that constantly need human supervision.
Agents get access only to the specific tools and systems they need, with clear limits on what they can do.
Permissions, value limits, allow-lists and approval steps are defined and tested before the agent can take real actions.
Plans, tool calls, inputs, outputs and decisions are logged so every action can be reviewed and understood.
Actions can be paused, corrected or rolled back, giving your team control whenever something needs to be changed.
What we build
Four agent types, covering the work that needs more than an answer.
AI Agents that do more than answer questions. They can check orders, review policies, process eligible refunds, update customer records and resolve support tickets, with human approval required for higher-risk actions.
AI Agents that handle repetitive sales and operations tasks, from qualifying new leads and sending follow-ups to updating CRM records and reconciling invoices and orders.
AI Agents that gather information from your business data and approved external sources, verify key findings and create structured, cited reports for your team.
AI Agents that work alongside your team, helping draft content, find information and handle routine tasks. Actions can be reviewed and approved before they are taken, with permissions based on each user.
Operating standards
Autonomy is only acceptable with a boundary around it. These are the guarantees behind every agent we ship.
Plan, tool calls, inputs and outputs recorded on every run.
Any run can be paused, corrected or reversed by a person.
Scored against real historical cases before it touches a live one.
Monitored with alerting on failures, loops and silent stalls.
Engagement models
Prove one agent on one task, then widen the boundary once the evidence supports it.
One narrowly scoped agent, fully instrumented, running supervised on real work.
from $790 /month
The smallest deployment that proves the boundary holds.
An agent trusted to act within limits, monitored, with the gates relaxed only where evidence supports it.
from $1,290 /month
The lane most teams reach after a successful pilot.
A standing team building and operating a fleet of agents across your business.
from $2,190 /month
Best for operations running several agents at once.
Plenty should not be. If a process is deterministic, a plain automation is cheaper, faster and easier to reason about, and we will say so rather than putting a model where an "if" statement belongs.
How we work
Capability is the easy half. We spend the effort on the boundary, the evaluation and the audit trail.
We define the exact task, what success looks like, what the agent must never do and which decisions require a person.
We define the tools the agent can access, set limits on actions and add approval gates for anything important.
We build the agent with detailed logging and tracing, making every plan and tool call easy to review.
We test the agent against real historical cases, including difficult scenarios where mistakes could be costly.
The agent proposes actions while a person reviews and approves them. We measure results until its reliability is proven.
The agent gains more autonomy only when the results support it, with alerts, usage limits and a kill switch always available.
Outcomes
The difference between a system that drafts the work and one that finishes it.
AI Agents that do more than classify requests. They can answer calls, book appointments and complete tasks end to end, with clear limits and human approval when needed.
Follow-ups, customer requests and routine reconciliation can run outside business hours, so your team starts the day with less work waiting.
The same rules and business policies are applied consistently, with decisions and supporting information recorded for review.
Your team focuses on exceptions, complex decisions and customer relationships while the agent handles predictable work.
Every action and decision is logged, making it easier to review what happened and why.
Spend limits, permissions and approval gates keep AI actions controlled, with the ability to pause or reverse them when needed.
FAQ
Still unsure? Send us a note — we reply personally.
A chatbot provides answers, while an AI Agent can take action. It can make calls, book appointments, update your CRM, complete tasks and continue working until the job is finished or it reaches a defined limit. Because agents can act on real systems, they require stronger permissions and guardrails.
Every agent gets limited access to the tools it needs. We use approval gates, action limits, allow-lists, detailed logging and reversible actions, with a kill switch available when needed.
We test agents against real historical cases with known outcomes, including difficult scenarios. The agent then runs under supervision while we measure its results before giving it more autonomy.
We test agents against real historical cases with known outcomes, including difficult scenarios. The agent then runs under supervision while we measureAI Agents are useful for multi-step tasks that require information from different systems and decisions within defined rules. They can make and receive calls, qualify leads, book appointments, resolve support cases, send follow-ups, investigate exceptions, reconcile records and produce research.its results before giving it more autonomy.
Yes. AI Agents can work with your CRM, help desk, calendar, phone system, billing software and internal tools through APIs or custom integrations. They work alongside your existing systems rather than replacing them.
AI usage is measured per run, so we optimize costs using the right model for each task, caching repeated information and setting usage limits. We also track the cost per completed task alongside accuracy.
Data handling is defined before development begins. We use appropriate enterprise AI services, keep data within your infrastructure where practical and can deploy models in your own environment when security or compliance requirements require it.
Name the process that eats your team’s week. We will scope it, define the boundary and show you what an agent can safely own.
