U.S. publisher · NDA
5% less team time
on average, to complete the tasks versus the manual process · 7 workflows deployed
MVPs, automations, and demos to test an idea's value before scaling — using tools like ChatGPT, OpenAI API, n8n, TypeScript, and AWS to prove what works with a tight scope.
U.S. publisher · NDA
5% less team time
on average, to complete the tasks versus the manual process · 7 workflows deployed
Lapidador
23 techniques
complete product shipped with integrated payments
Pulso
55+ editions
published at dorly.com.br/pulso
AI Daily Briefing
4 hours
from idea to the first working version
Short cycles, with an early first version
Product, innovation, operations, and technical leadership
MVP or demo that proves value, limits, and effort before scale
Prototype
Seven AI workflows were deployed in the editorial operation of a U.S. fiction publisher — from the first manuscript filter to marketing assets. On average, carrying out these tasks reduced the team's time by 5% compared with the manual process.
One flow evaluates ~50k-token manuscripts and returns a ~27-page editorial report; others generate clean versions of explicit texts, vertical-novel scripts, and 30-second trailers through a video API.
Everything orchestrated with n8n and Monday, with human approval between steps — AI speeds things up, but final calls stay human.
Company name and internal details withheld under NDA.

Users move from a rough idea to a structured prompt with an explanation of the choices

AI news from four continents becomes a daily edition in Portuguese, with the source and the checks in plain view

Users receive a review that reduces artificial markers without replacing meaning or voice with a generic style

Scattered news becomes a browsable audio briefing with a transcript and sources
Yes. For a U.S. publisher (under NDA), seven workflows were deployed in the editorial operation and reduced the team's time on these tasks by an average of 5% compared with the manual process. Own products such as Pulso and AI Daily Briefing also started as prototypes and went on to be published.
The work is organized into short cycles. We put an initial working version into evaluation early, then define the following cycles according to scope, findings, and required integrations.
To talk about this service, write to oi@dorly.com.br.
Before commissioning training, technology, or metrics, this engagement organizes the decisions: where to start, which pilot fits the context, who is accountable for it, and what needs to happen next. The scope ends with an approved design; execution, integrations, and operations are later workstreams.
Purchasable formats for a synchronous decision-making and practice experience: executive session, hands-on workshop, or modular path. Each format combines facilitation, real tasks, and feedback during the activity; it is not a generic lecture or a promise of behavior change without follow-through.
Enablement means giving a team practical conditions to use AI without always depending on outside help. The package turns recurring decisions and tasks into editable playbooks, templates, rubrics, and guides with a defined audience, validation path, owner, and maintenance rule.
I design the model connecting reaction, learning, behavior, and impact without treating correlation as causation. The work defines what to measure, where the data comes from, and how to read the signals; business outcomes and integrated dashboards depend on data quality and later implementation.