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APPROACH

Experience from the field. Adapted to your business.

We build on designs refined in other industries, then test them against your data, workflows, and risks. We call this process the Atreez Transfer Loop.

WHAT TRANSFERS

Design and operating experience, ready for the next challenge.

Client data stays with the client. We carry forward workflow designs, shared software, evaluation methods, and experience handling failures.

THE LOOP

Find the opportunity. Build on what works.

Run includes development, validation, and operations. At each stage, we check progress against the criteria agreed with you.

  1. 01

    Find

    Find the opportunity

    Commitment
    We identify where AI could make a useful difference to your business.
    Outputs
    Workflow map, current baseline, data and access review, priority use cases

  2. 02

    Fit

    Fit the solution

    Commitment
    We adapt designs from other industries to your data, workflows, and risks.
    Outputs
    Reusable components, new design requirements, human and AI roles, evaluation criteria

  3. 03

    Run

    Put it to work

    Commitment
    We build the service, connect it to your systems, and operate it.
    Outputs
    Integrated service, limited-rollout results, monitoring and response playbooks

  4. 04

    Grow

    Grow what works

    Commitment
    We use results from operations to identify the next workflow or business opportunity.
    Outputs
    Results review, improvement plan, reusable assets, next use cases

SHARED BUILDING BLOCKS

Shared building blocks, refined through use

We select components developed across our projects, adapt them to your business, and validate how they work together.

Industries with live services

  • Commerce & marketing
  • Fandom & creators
  • Customer service
  • Media & content
  • Community & events

Industries in development or testing

  • AI companions
  • Legal
  • Fintech
  • Public & transit
  • Healthcare & ERP

Shared building blocks, selected for your project

  • Conversation engine

    A reusable engine for AI that asks questions, organizes information, and suggests next steps.

  • Human handoff

    Distinguishes messages AI can answer from those needing a person, and passes on a summary of the context.

  • Model selection & integration

    We choose models for each task and work with multiple providers to balance quality and cost.

  • Payments & messaging

    Connects payments, notifications, and messages into the workflows your business runs on.

  • AI safeguards

    Personal data removal, budget limits, checks on data sent to AI, and fact-checking help us detect and respond to errors.

  • Multilingual operations

    Runs the same service in Korean, English, Japanese, Chinese, and more.

Your industry

MORE STORIES

More cross-industry stories

Designs from one service inform work in another. Some of these services are live; others are still in development.

  1. One conversation engine across services

    Services share an engine that asks questions, organizes information, and suggests next steps. These include both live services and projects in development.

    Started in
    A networking service2026.03
    Carried into
    • A media service2026.03
    • Brand–creator matching2026.04
    • A legal service (in development)2026.04

  2. AI reads meaning, rules check format

    AI reads meaning and context; rules check the format of numbers and dates and the required fields.

    Started in
    A customer service product2026.02
    Carried into
    • Product review moderation2026.06
    • Medical advertising review support2026.09
    • Community operations2026.10

WHAT WE LEARNED

Principles for putting AI to work

Our approach draws on project experience and published research, from choosing a use case to planning operations.

  1. 01

    Business goals and a baseline[3][4]

    Define the workflow and its owner. Establish current cost, time, and quality as a baseline.

  2. 02

    Usable data[3][4]

    Check data quality, access rights, sources, and freshness. Establish who keeps it current.

  3. 03

    Redesigned workflows[1]

    Design workflows that connect AI output to approval, action, and a record of what happened.

  4. 04

    Clear human–AI handoffs[5]

    Set review points by risk and uncertainty, with ways to correct and roll back.

  5. 05

    Task-level evaluation[4][5]

    Measure accuracy alongside task completion, rework, human intervention, and cost per task.

  6. 06

    Start small, iterate[3]

    Test on a focused workflow, then use the failures you find to improve the next version.

  7. 07

    Operations built in[2]

    Include cost and quality monitoring, change management, incident response, and training in scope.

Tell us what you want to change with AI.

Tell us about the service you want to build or the work you want to change. We'll define a starting point and scope together.

Discuss your AI projectEmail us directly