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First we understand your company. Then we decide what AI does.

These are the criteria we apply to any company. We publish them so that, before you talk to us, you know how we'll think about yours.

Fran Sacares, founder
  1. 1The process
  2. 2The tool
  3. 3Automation
  4. 4AI

Why in this order

AI amplifies whatever it finds. On a clear process and software that works, it multiplies what the team can do. On a muddled process, it multiplies the mistakes: faster, at greater scale and harder to see. So the question isn't where to put AI, but what needs fixing first.

Three decisions before automating

Keep

When it works.

If a program or a way of working does its job well, it stays as it is. We connect it to everything else so data is entered once and reaches everywhere.

Signs

  • The software covers what you need
  • The team uses it without workarounds
  • The problem lies between programs, not inside one
Systems integration

Change

When it's the problem.

If the process has steps that add nothing or the software forces workarounds, we say so. Redesigning the process or switching tools in time saves more than any automation.

Signs

  • Everyone does it their own way
  • A spreadsheet covers what the software can't do
  • There are steps nobody knows why they exist
Process redesign

Build

When nothing fits.

If no software on the market fits the way you work without making you give up what sets you apart, we build it to measure, integrated with everything else.

Signs

  • You've tried several programs already
  • The way you work is part of your edge
  • The alternatives mean constant patching
Custom software

Then

Automate, and use AI only where it adds value

With a clear process and the tools in place, we choose the simplest technology that solves each step. Most of the time it's a rule. Sometimes, AI. And an agent only when decisions need chaining together.

Not everything needs AI

Automation, AI and agents. The right technology for each process.

Putting a language model where a rule is enough costs more, runs slower and fails more often. We choose the technology based on what the process needs.

Clear rules

Traditional automation. No AI, no agents.

Structured data across several systems

Integration: data travels on its own between your tools.

Documents in different formats

AI to extract the data and validation against your systems.

Language and interpretation

Generative AI with defined criteria and a constrained output.

Several steps, tools and chained decisions

An agent, with permissions, limits and traceability.

A critical decision

AI and rules, with a person's oversight before it runs.

When an agent makes sense

When information first has to be interpreted, systems queried and conditional decisions made, an agent can add value. Always with permissions, limits, traceability and a person to escalate to.

How we design an AI agent

We automate the work. Your team reviews the exceptions.

How we work

We listen, analyze, propose, implement and support.

We don't install a tool: we design the system around your process.

First we understand the process. The technology is decided afterwards.

  1. We listen
  2. We analyze
  3. We propose
  4. We implement
  5. We support
Analyze my company
  1. 1
    60 min · at no cost

    First meeting

    You tell us how you work and where your time goes. We tell you where we see room for improvement and whether it makes sense to go further.

  2. 2
    Written report

    Assessment

    We go through your processes with your team: what comes in, who decides, what is done by hand. The result is a prioritized map of opportunities.

  3. 3
    Scope and cost agreed

    Proposal

    What gets automated, what gets integrated, where AI comes in and where a person approves.

  4. 4
    In deliveries

    Implementation

    We build, connect to your systems and test with real data.

  5. 5
    Monthly fee

    Ongoing support

    We keep it running, review exceptions and extend it to new processes.

Security and control

AI that executes, but under control.

Automating means giving access to email, documents and management software. It is designed with the same precautions as any other access to company data.

Permissions and authentication

Own credentials, revocable and separated by integration and environment. Each automation or agent only has the permissions it needs and explicit limits on what it can do.

Validations

Data is checked before it is written to your systems, not after. What doesn't fit is set aside with context instead of being forced through.

Traceability

A record of what the system did, with which data and when, plus alerts when a flow fails or behaves outside what is expected.

Human oversight

On critical steps the system prepares the operation and a person confirms before it runs.

No system is free of failures, and regulatory compliance depends on each case. That's why we design them so that, when something doesn't add up, it is detected and stopped before it spreads.

What you can expect

Four commitments

  1. No AI where a rule will do

    It's more expensive, slower and fails more. If a step can be solved with rules, it's solved with rules.

  2. We tell you when not to automate

    The assessment may conclude that the process needs sorting out, the software needs replacing or nothing should be done yet. We write that up like any other conclusion.

  3. No black boxes

    We work with n8n, which can be hosted on your own servers and whose flows can be exported, and with documented in-house development. Every part can be reviewed.

  4. Access that's yours and revocable

    Each integration uses its own credentials, separated by environment, with only the permissions it needs, and they can be withdrawn at any time.

Shall we apply it to your company?

In the first meeting, at no cost, we look at one of your processes with these criteria.

Analyze my company