Cadencia

Felipe Luis Salgueiro / Selected work

Brand, product and operations. Built into systems.

I’m a Marketing Engineer working across brand strategy, product and GTM systems powered by AI. These cases trace the problem, my role, the system and what the evidence can support.

Explore selected workLet’s talk
Portrait of Felipe Luis Salgueiro

Felipe Luis Salgueiro

Marketing Engineer

Selected work

Six contexts. The decisions, systems and lessons behind the work.

Evidence guide: public product and teaching references are linked where included. Historical accounts are identified as such; missing artifacts and unverified outcomes are not presented as proof.

Conceptual illustration

01 / Product · GTM · AI systems

Cadência

Context
Marketing professionals need to operate content, distribution and customer relationships across different brands.
Problem
Translate real customer routines and constraints into a product without flattening each brand’s identity.
My role
Founder and Marketing Engineer, leading product, technology, growth and customer operations.
System built
A marketing platform connecting brand context, content workflows, distribution, CRM and automation. The work spans architecture, memory, roles, integrations and guardrails.
Evidence
The public product catalog describes capabilities and workflows. It is a product reference, not independent proof of customer outcomes.
Learning
Start from the customer’s operating constraints; make context, review and accountability part of the product.
Explore the product catalog (Portuguese)

Conceptual illustration

02 / Agent operations · Software delivery

PD Framework

Context
Operating AI agents across product, marketing and business work requires shared context and clear responsibilities.
Problem
Make work traceable across agents and sessions, with explicit boundaries for review and human decisions.
My role
Architect and operator of the multi-agent framework.
System built
An operating framework that connects roles, context, memory, skills, integrations, governance and execution evidence.
Evidence
This summary describes Felipe’s operating practice. An inspectable, sanitized framework artifact is not included in this portfolio.
Learning
Treat AI as an engineering and operating discipline: define responsibilities, inspect evidence and preserve human decisions.

Public artifact not included

Conceptual illustration

03 / Operations · Brand · Service systems

O Berro

Context
A restaurant where Felipe progressed from kitchen work to chef, operations leadership and partnership.
Problem
Connect tacit team knowledge, menu economics and service capacity with brand and demand.
My role
Kitchen practitioner, chef and later partner, working alongside kitchen and front-of-house teams.
System built
Documented recipes and specifications, contribution-margin and cost routines, purchasing, inventory and operational leadership, connected to brand and distribution work.
Evidence
The case draws on Felipe’s career account. Original operating documents and comparable performance records are not included here.
Learning
Observe the work with the people doing it. Connect demand generation to the operation’s ability to deliver.
Visit O Berro’s website(opens in new tab)

Conceptual illustration

04 / Brand strategy · Growth · Commercial operations

Grupo JDR

Context
An agency connecting brand positioning, growth and commercial operations across client accounts.
Problem
Translate each client’s business reality into a coherent direction for brand, demand generation and sales follow-through.
My role
Founder and brand, growth and GTM strategist, leading client diagnosis, field research and strategic direction across accounts.
System built
Client visits and research informed positioning, narrative, founder brands, content and distribution. Planned and coordinated launches, paid media, lead capture, CRM and sales automation. In one truck-body engagement, observing truck drivers’ influence on purchasing informed a TikTok channel and narrative choice.
Evidence
The career account documents the engagement scope and the truck-body research example. Original campaign artifacts, client identities and measured commercial outcomes are not included.
Learning
Connect positioning to the commercial operation. Research reveals both the buyer and the people influencing the decision, helping determine narrative, channels and follow-up.

Public artifact not included

Conceptual illustration

05 / Product discovery · Partnerships · Experiments

Dagood

Context
A leisure discovery venture exploring how people choose occasions, places and activities together.
Problem
Turn identity and belonging into useful discovery while validating a viable revenue model.
My role
Co-founder working on discovery, brand, partnerships, fundraising, SEO and launch, in coordination with engineering leadership.
System built
Discovery around group organizers and occasion filters, plus Missões Dagood experiments with QR codes and rewards.
Evidence
This is a retrospective account of the venture and its experiments. Revenue was still being validated when it closed; public experiment artifacts are not included.
Learning
Validate revenue early alongside user value. A useful experience alone does not settle the business model.

Public artifact not included

Conceptual illustration

06 / Teaching · AI product development

NoCode Startup

Context
Teaching professionals to build and operate software with AI through courses and live sessions.
Problem
Move from isolated prompts to a complete development process with architectural reasoning and verification.
My role
Instructor and speaker on AI product development and software architecture.
System built
Curriculum, materials, repositories and live demonstrations connecting the problem, PRD/RFC, code, APIs, Git, databases, tests, review and operations. Cadência and PD Framework provide practical teaching cases.
Evidence
The public lesson hub provides topics and lesson material. It documents the teaching approach; it does not claim learner or business outcomes.
Learning
Teach the decisions and checks behind a working system, so learners can reason beyond the demonstration.
Explore the lesson hub

How I work

A practical loop from observation to correction. The scale changes; the need to understand the work does not.

  1. 01

    Observe behavior

    Start with customers, workers and the constraints of their actual routines.

  2. 02

    Frame a hypothesis

    Describe the problem and the assumption that needs testing.

  3. 03

    Build a system or decision

    Connect the people, product, process and tools needed to act.

  4. 04

    Inspect evidence

    Separate what was built from what was observed and what remains unknown.

  5. 05

    Correct and document

    Use the finding to revise the work and preserve the learning.

A systems view

Conceptual view of the connections across my work.

  1. 01

    People & context

    Discovery and field research

    Dagood · Grupo JDR

  2. 02

    Brand & product

    Context translated into product workflows

    Cadência

  3. 03

    Operations & tools

    Operating routines and responsibilities

    O Berro · PD Framework

  4. 04

    Evidence & learning

    Practice translated into teaching

    NoCode Startup
↺ Learning feeds back into the next observation and decision.

Looking for someone who connects strategy to execution?

For roles spanning brand, product, GTM and AI operations, start with my CV or get in touch.

English CV (PDF)Portuguese CV (PDF)
LinkedInEmail FelipeProfessional profile