Fractz

What we do

AI Automation

Reduce manual work by up to 60% with agents, RPA, and integrations.

Talk to an engineer

What we do

We map the repetitive work in your operation, automate the parts with clear inputs and outputs, and leave the judgement calls with your team. Most of the value sits in unglamorous processes nobody wants to own — the weekly report assembled by hand, the data copied between two systems, the queue that only moves when someone is at their desk.

  • Baseline measured before we build
  • Human checkpoint on irreversible actions
  • Runtime logs and a kill switch

You probably need this if

What we build

01

A measured baseline

We time the manual process before touching it. Without a baseline you cannot prove the automation worked, and unprovable value is the most common reason these projects get cancelled.

02

Workflows with an audit trail

Every run logs its inputs, outputs and decision path — the record your auditor and your team both need when something looks wrong.

03

Agents with clean handover

Scoped to bounded tasks, with an explicit boundary and a clean escalation to a person the moment a case falls outside it.

04

Runtime control

Monitoring on tool calls, a human checkpoint on anything irreversible, and a kill switch a non-engineer can operate.

Typical stack

  • Python
  • LangGraph
  • Temporal
  • n8n
  • Make
  • Postgres
  • MCP connectors
  • your ERP / CRM

How an engagement runs

  1. Weeks 1–2

    Discovery and baseline

    Process mapping, measurement of the current state, and a written scope with a price and a date.

  2. Weeks 3–6

    Build and pilot

    The first workflow ships to a limited group. Weekly demos against the baseline, not status meetings.

  3. Week 7+

    Rollout and handover

    Extension to the full process, documentation, and handover of source and infrastructure.

Process

  1. 01

    Discovery & Audit

    Goal analysis, process mapping, and KPIs.

  2. 02

    Tailored build

    Rapid development with biweekly sprints.

  3. 03

    Deploy & Scale

    Monitoring, training, and continuous optimization.

Engagements

  1. 01

    Fashion manufacturing

    Product operations for a leather goods maker

    Pellemoda S.r.l.

    A production platform the marketing team runs without engineering support

    System
    Digital product platform + operations automation
    Duration
    Multi-phase
    Read the engagement
  2. 02

    Education

    Marketing automation for an international college

    H-Farm College

    Manual marketing tasks automated; team time moved back to campaigns

    System
    Marketing automation + reporting
    Duration
    Multi-phase
    Read the engagement

Common questions

How do you decide what to automate first?
Volume, boundedness and reversibility — in that order. A high-volume process with a checkable definition of correct, where a wrong output costs a correction rather than a customer. If we cannot measure today's baseline, it is the wrong first project.
What happens when the model gets something wrong?
Irreversible actions sit behind a human checkpoint by design: payments, external messages, deletions. Everything else is logged with enough context to reconstruct the decision and correct it.
Does our data leave the EU?
Not by default. We deploy on EU-resident infrastructure and tell you explicitly if a component would move data outside it, before you commit.
Who owns what you build?
You do. Source, infrastructure and models are handed over on completion. There is no dependency on us to keep it running.

Services