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AI Agents & Business Automation

Automate real workflows with agents that talk to customers, process information, decide, and connect your systems.

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The problem

Your team answers the same questions, re-keys the same data and chases the same approvals every day. The work is predictable enough for software, but too varied for a rule-based script. Agents close that gap: they read, decide within limits you set, act through your existing tools, and hand off to a person when they should.

What we build

  • Customer and guest agents that answer, qualify and book across email, chat and WhatsApp
  • Back-office agents that read documents, update records and route approvals
  • Pricing and operations automations that run on schedules and events, with a person in the loop where money moves

AI agents, Workflow automation, Guest and customer communication, Pricing automation, Vertical AI

Business automation

How it works

  1. 01

    Map the workflow

    We sit with the people who do the work, list every step and decision, and pick the one process where an agent pays back fastest.

  2. 02

    Design the guardrails

    What the agent may decide alone, what it must escalate, which systems it may write to, and how every action is logged.

  3. 03

    Build and rehearse

    The agent runs beside your team on real cases for a week before it acts alone. Prompts, tools and limits are tuned from the log.

  4. 04

    Run and measure

    Live with monitoring, cost tracking and a weekly review of handled, escalated and failed cases.

Work that proves it

  • Zugrow home page: autonomous short-let hosting, beside a panel of occupancy and revenue figures

    Zugrow

    AI agents for property and vacation-rental operations

    Designed and developed for the client: agent workflows, guest communication and the pricing automation surface.

  • Userdesk home page: AI assistants that collect leads, beside a chat preview and content sources

    Userdesk

    No-code AI support and lead assistant trained on your site, Notion, PDFs and Drive

    Designed and developed for the client: retrieval pipeline, the embeddable assistant and the lead hand-off flow.

  • PDFData home page: an extracted invoice card beside a review queue of ready and flagged documents

    PDFData

    AI invoice and receipt processing to Excel, CSV and JSON

    Designed and developed for the client: extraction pipeline, bulk processing and the export and API surface.

What you get

  • Workflow map and agent specification

  • Agent with tool connections and guardrails

  • Escalation and hand-off rules

  • Action log and monitoring dashboard

  • Cost and quality report after the first month

  • Runbook for your team

Questions

What's included in an AI agent engagement?
A workflow map and an agent specification first, then the agent itself with its tool connections, guardrails and escalation rules. You get the action log and a monitoring dashboard, a cost and quality report after the first month, and a runbook so your team can run it without us. Every deliverable lands in your accounts and your repository.
How long until the first agent is live?
The first agent is usually live in four to eight weeks. What moves that number is how many systems it has to touch, how clean the data behind them is, and how fast decisions come back from your side. A single well-defined workflow lands at the short end; an agent that writes into a legacy system with no API sits at the long end.
What do you need from us?
Three things: access to the systems the agent will read and write, one person who can decide what it is allowed to do alone, and an hour a week to review what it handled and what it escalated. We also need a few dozen real past cases — emails, tickets, documents — because rehearsing on real work is what makes the limits right.
Who owns the code and the data?
You do, from the first commit. Code lives in your repository or moves to it at handover, infrastructure runs in your accounts, and your data stays yours — we hold access only while we work for you. Handover includes the repository, the environment variables, the runbook and a walkthrough with whoever takes over.
What happens when the agent gets it wrong?
Every agent runs inside limits you set: what it may decide alone, what it must escalate, and which systems it may write to. Actions are logged, so a wrong call is visible, reversible and traceable to the prompt and the data behind it. During the first weeks the agent runs beside your team on real cases before it acts alone, and the weekly review of handled, escalated and failed cases is where the limits get tightened.

Not sure where to start?

Describe the problem in a few lines. You get a straight answer on what we would build and how long it takes.

Book a 5-minute growth call
Book a 5-minute growth call