CompaniesAI adoption and implementation

Before automating, we discover where the value is

We step into the company’s everyday work, identify tasks worth improving with AI and turn the best opportunities into solutions the team can use and maintain.

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Several signals and processes converging into a prioritized opportunity

First deliverable

A bounded diagnosis so you can decide without buying an implementation blindly

We agree in advance which department or processes we will observe and close the analysis with a concrete recommendation.

Process map

Tasks, tools, owners, inputs, outputs and the points where time or context is currently lost.

real workfriction

Prioritized opportunities

Cases ranked by value, feasibility, risk, effort and the team’s real capacity for adoption.

valuefeasibilityrisk

Action plan

What to test, integrate, automate or discard, with clear dependencies and next steps.

decisionnext steps

Clear scope

A first phase with a beginning, an end and a decision

The diagnosis is not an open-ended promise to transform the entire company. We agree on a manageable boundary and separate any later work.

diagnosis.diff+3−3

@@ scope · access · decision @@
−Auditing every department without a defined boundary
−Including licenses, development or deployment before deciding
−Starting with open-ended effort and no completion criteria
+Observe the agreed area with real examples and people
+Prioritize opportunities, dependencies and risks
+Close with a decision: test, train, integrate, build or discard

From analysis to real use

Implementation is optional; transferring knowledge is essential

The diagnosis may close the engagement or open a second phase. If we implement, we combine technology, training and measurement so the solution reaches daily work.

implementation.yml

01Boundthe department or set of processes
02Observethe tasks as they are actually performed
03Prioritizeby value, feasibility and risk
04Testthe solution within a clear boundary
05Implementin real tools and processes
06Transferjudgment, documentation and autonomy

Areas of application

Processes where AI can reduce friction and recover context

Every diagnosis starts from real tasks and the systems the team already uses. These are some areas where we can look for concrete opportunities.

Email and priorities

Review incoming items, recover context and suggest priorities before a person decides.

classificationcontext

Accounting

Detect pending work and prepare executable next steps while preserving oversight.

controlreview

Content

Research, structure and adapt material without losing sources or human editorial judgment.

sourcesjudgment

Document management

Find information, connect documents and answer with traceability back to the source.

searchtraceability

Conditions for trust

More productivity without losing control

ChatGPT, Codex and Claude are part of our experience, but we do not impose a provider. We build on what the company already uses and add complexity only when it earns its place.

Data and permissions

Minimum necessary access and explicit handling of sensitive information.

Traceability

Visible sources, decisions and actions so what happened and why can be reviewed.

Human oversight

Critical decisions retain a control point proportional to the risk.

Autonomy

We train and document so the system does not depend on us indefinitely.

Start with a process worth understanding

Tell us which department, tasks or friction points you want to review. We will bound the diagnosis before proposing any implementation.

Request a diagnosis