AI Agent Development That Does the Work, Not Just the Talking

For operating teams drowning in repetitive decisions. We build agents that read your systems, act within rules you set, and report what they did — not chatbots that hand the job back to you.

• Built on Claude and current frontier models, chosen per task
• Human approval on every high-consequence step
• You own the prompts, the code and the documentation

The problem with most “AI” you have been sold

A chatbot answers a question and leaves the work exactly where it was. Someone on your team still opens the CRM, still copies the detail across, still writes the follow-up. The tool demos beautifully and changes nothing on Friday afternoon.

The gap is not intelligence, it is permission and plumbing. An agent that cannot reach your systems cannot do your work, and an agent allowed to reach everything is a liability nobody will sign off. Most projects die in that gap.

What you get

• A working agent deployed against one real process
• Connections to the systems it needs — CRM, helpdesk, email, sheets, internal APIs
• A rules layer deciding what it may do alone and what needs a human
• Full logging, so every action it takes is auditable afterwards
• Fallback paths for when a model or an upstream system fails
• Handover docs and a recorded walkthrough for your team

How it works

1. Map — We walk the process end to end with whoever actually runs it, and mark the decisions a machine can safely make. (Week 1)
2. Scope — Deliverables and success criteria written down and agreed before anything is built. (Week 1)
3. Build — The agent, its integrations, its guardrails and its logging — tested against real cases, not happy paths. (Weeks 2–3)
4. Hand over — Deployed, documented and walked through, with your team able to change it without us. (Week 4)

Questions

What is the difference between an AI agent and a chatbot?

A chatbot answers questions. An agent takes actions: it reads your systems, decides what to do within rules you set, and performs the work — creating the ticket, updating the record, sending the reply — then reports what it did.

What can an agent actually be trusted with?

Whatever you decide in the rules layer. Typically agents handle classification, drafting, data entry and routing on their own, while anything that spends money, contacts a customer unsupervised or changes a record of account routes to a person first.

Which models do you build on?

Primarily Claude, with other current frontier models where they fit better. Model choice is a design decision per task: the cheapest model that clears your accuracy bar is the right one, and that is rarely the largest available.

How do you stop it making things up?

By narrowing what it is asked to do. Agents are given the specific records they need rather than open-ended questions, high-consequence steps require approval, outputs are validated against expected shapes, and anything out of range falls back to a human.

How long does a first agent take?

A single agent against one well-defined process typically ships inside a two-to-four-week pilot, including discovery, build, testing and handover. Work spanning several systems or needing a security review runs longer and is phased.

What does it cost to run?

Two separate costs: the build, and the model usage once it is live. Usage is sized during discovery so the monthly figure is known before you commit. Routing cheaper models to easier tasks is part of the design, not an afterthought.

Do we own what you build?

Yes. Prompts, code, workflow definitions, documentation and credentials are handed over and belong to you. There is no platform you have to keep renting from LEVRYO to keep your own automation running.

Can it work with our internal systems?

Usually. Anything with an API is straightforward; anything without one needs a conversation about what is realistic. We say during discovery which integrations are easy, which are awkward, and which are not worth attempting.

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