Bruno Caldas
with Orion Matthews

Podcast Overview

A candid, executive-level conversation between two people who have spent their careers turning complex project and financial data into decisions leaders can trust. Orion Matthews, CEO of Queryon, hosts; Bruno Caldas is the guest, walking through his career, from his first mega-projects to founding his own company, and on to data, BI and AI in project delivery.

Episode Agenda

The full running order: each block is its own chapter with talking points and questions. Times are guides for a ~60–75 minute cut; let strong stories breathe. Click any block to jump to it.

Opening

A short, warm open. Orion frames the episode: a conversation with someone who has spent 18 years making project and financial data tell the truth (on some of the world's largest capital projects) and now builds the tools and teams to do it at scale.

Introducing Orion Matthews

Orion Matthews

CEO & Lead Consultant, Queryon · programmer, entrepreneur, technology executive

Eugene, Oregon, United States

Enterprise DataData VisualizationCustom SoftwareEntrepreneurshipNon-profits

“Connecting bright minds to the decision makers changing the world.”Orion Matthews · LinkedIn

Orion is an award-winning programmer, entrepreneur and technology executive. He helps managers harness the storytelling power of data to modernize and transform their businesses, and he and his team have built solutions used by some of the world's largest enterprises, governments, NGOs, educational institutions and project-control organizations.

1. Orion, you founded your first software company out of high school and have been building ever since. What's stayed constant in how you approach a hard problem?
2. Your tagline is “connecting bright minds to the decision makers changing the world.” What does that look like on a real engagement?
3. You've worked with everyone from Antarctic research stations to global enterprises. What made you want to host a conversation like this one?

Introducing Bruno Caldas

Bruno Caldas, MBA

Founder, BC Project Management · Project Controls, Capital Allocation, BI & AI

Toronto, Ontario, Canada · originally from Brazil

Project ControlsCapital AllocationFP&ABusiness IntelligenceAI

Bruno has built an 18+ year career (16 of them in mining) at the intersection of projects, finance, systems and analytics. He pairs the owner's-side discipline of governing capital with hands-on project controls, and backs both with advanced business intelligence and AI. This chapter is the quick “who he is”; the story unfolds across the chapters that follow.

“Without data, you are just another person with an opinion.”Bruno's guiding line, after W. Edwards Deming

Career journey

1. If you had to describe what you do to someone outside your field, how would you put it?
2. You've lived and worked in five countries and speak four languages. How did that shape the professional you became?What did building credibility as the newcomer teach you?
3. What drew you to the intersection of projects, finance and data, rather than picking just one?

Major Projects

Bruno's first project was the Presidente Getúlio Vargas Refinery (REPAR) in Paraná, a large oil & gas build, and where he learned the fundamentals of planning, scheduling and progress. Then came rail, with the São Paulo Monorail at Grupo MPE. From 2011, mining at world-class scale: Simandou in Guinea and S11D in Carajás. This chapter lives at the project level; the next one rises to the portfolio.

1. Your first project was REPAR, the oil refinery in Paraná, then the São Paulo Monorail with Grupo MPE. What did those first builds teach you about how big things actually get built?
2. What was it like to work in Guinea, in West Africa, on the Simandou iron-ore mega-project?What surprised you most on the ground?
3. Building a greenfield project from almost nothing (mine, plant, 600+ km of railway, port), what did that teach you about integrated planning?
4. S11D became one of the largest iron-ore projects in the world and a benchmark for delivery. What are you proudest of from that chapter?
5. On S11D, logistics were half the story: the rail spur, the Carajás Railway duplication and the Ponta da Madeira port. Back then, one of your progress reports was a photo taken every Friday from the same spot on the railway. If you ran that report in 2026, with AI reading the images, what would change?What could AI catch that the eye would miss?
6. These projects run US$15–20 billion. How does that scale of CAPEX change the way you plan, control and communicate, and how do you hold your nerve under that executive pressure?
7. When you present numbers that leaders will bet billions on, how do you build the trust that makes them believe you, and how fast can you lose it?

Portfolio Manager · FP&A & PMO

After the projects, Bruno's career rose to the portfolio level. From 2017 he joined Vale's FP&A team for the Base Metals (copper & nickel) portfolio: budgeting, forecasting and Board-level reporting. Then, from 2019 to 2023, he built and led the Base Metals PMO in Canada as Project Controls Manager: capital allocation, investment committees and multi-year governance for a portfolio of roughly US$2–3 billion a year.

1. You moved into FP&A on the copper & nickel portfolio, reporting up to the Board. How did seeing projects through a financial lens change the way you think about controls?
2. Then you built a Base Metals PMO from scratch in Canada. What's it like to build a whole planning-and-governance model from zero?
3. Capital allocation means saying no. How do you decide which projects in a multi-billion-dollar portfolio get funded, and which have to wait?What makes a prioritization model people trust?
4. What changes, in your job and in your head, when it stops being about one project and becomes about a whole portfolio?

Entrepreneurship: BC Project Management

In parallel with his corporate career, Bruno founded his own company: BC Project Management: a project management, project controls, AI, data engineering and business intelligence firm. It designs end-to-end data strategy and builds PMOs from the ground up (project management standards, templates, procedures, manuals and guidance), turning everything he learned on mega-projects and portfolios into a way for organizations to see their projects clearly and decide faster.

What the company does

Capabilities

1. What made you decide to start your own company, BC Project Management, alongside everything else you were doing?
2. What is BC Project Management, in your own words, and what problem is it built to solve better than anyone?Who gets the most out of it?
3. You went from doing the technical work to running a business. What was the hardest habit to unlearn, and what does a technical person underestimate about the business side until they live it?
4. What's the belief at the center of BC Project Management that ties project controls, data and AI together?
5. Where do you want BC Project Management to be in a few years?

Mining Industry

Bruno's current work sits at the level of a Business Intelligence & Project Services Manager in the mining industry: standardizing reporting across a large, diverse portfolio, and bringing data, analytics and AI into how mining capital projects are planned, governed and delivered. This chapter keeps it industry-wide: seven commodities, the full mine-to-port chain, and what BI and AI are changing in mining today.

1. You've worked across iron ore, copper, nickel, lithium, cobalt, aluminium and gold. What changes from one commodity to another, and what stays the same?Which was the steepest learning curve?
2. How do you see Business Intelligence (Power BI) in the mining sector today? Where is it making the biggest difference, and where is the industry still behind?
3. Mining means huge CAPEX, long decision cycles and remote logistics. What does the sector still get wrong about forecasting and risk on big capital projects?
4. A lot of your current work is standardizing project reporting and bringing AI into lessons-learned. Where's the biggest opportunity for the mining industry there?What would a truly data-driven mining project look like?

Project Controls & Performance

Now the conversation shifts from Bruno's story to the craft itself. Project controls (cost, schedule, risk, change, forecasting, reporting, performance measurement and governance) is the nervous system of any capital project. This chapter explains it plainly and gets at why it decides whether a project performs.

1. For someone who's never heard the term: what is project controls, and why should a CEO care about it?
2. Where's the line between project controls and project management, and where do the two get confused?
3. Forecasting is where controls earns its keep. How do you build a forecast people actually trust, and handle the optimism bias baked into most plans?
4. You've stood on remote sites and you also build the systems that model them from afar. What does field experience catch that a model on a screen never will, and where can a digital tool dangerously oversimplify?
5. What's the smallest set of controls a small team can start with tomorrow and still get 80% of the value?

Data, BI & Reporting

How the right business intelligence turns fragmented project, financial and operational data into clear, reliable decisions: Power BI, data warehouses, semantic models, automated reporting and the governance underneath. Plus the honest question every organization faces: when does Excel stop being enough?

1. A dashboard is easy to build and hard to build well. What separates a dashboard people ignore from one they run the business on, and how much is design versus the data model underneath?
2. Let's be fair to Excel: where is it genuinely the right tool, even for a serious organization, and when does it quietly become a risk?
3. The hardest part isn't the dashboard; it's the moment a leader looks at it and acts. What makes data actually change a decision, rather than just inform it?
4. Everyone wants dashboards and AI; almost no one wants to fund data governance. How do you make that unglamorous foundation matter to leaders?Where do you start with governance?
5. Where do BI projects most often fail: the data, the model, the design, or the adoption?

AI Applied to Project Environments

Getting concrete about where AI helps in project work (meeting summaries, documentation, reporting, data analysis, decision support and knowledge management), and where it doesn't. The interesting part is the guardrails: governance, data quality, security and human oversight.

1. Cut through the hype: what's an AI use case in project work that's genuinely saving you time today?
2. Where would you never let AI near a project controls process without a human firmly in the loop, and is it data quality, security, or accountability that worries you most?
3. You're building an AI-driven lessons-learned approach, combining historical data, benchmarking and AI. What could that change about estimating accuracy and risk?
4. For a project professional who's curious but cautious about AI, what's the first thing they should try, and the first thing they shouldn't?
5. Orion, you build AI into products. Where's the line between AI that helps a decision and AI that quietly starts making it for you?

Future of PMO & Project Services

How PMO and project-services teams change with data, automation, AI, integrated systems, predictive insights, better governance and faster decision cycles, and what the analyst's job becomes when the reporting runs itself.

1. If reporting increasingly runs itself, what does a great PMO analyst actually spend their day doing five years from now?
2. Predictive insight is the promise everyone makes. What has to be true, in data and governance, before prediction is actually trustworthy?
3. Will the future PMO be smaller and sharper, or bigger and more strategic? What's your bet?
4. What skill should every project-services professional be building right now to stay relevant?

Career Advice

Practical, transferable guidance for people building careers in project management, controls, BI, data, PMO, AI applied to projects and international work, framed as invitations for stories, not lectures.

1. For someone starting in project controls, PMO or BI today, what's the one skill you'd tell them to over-invest in?
2. How does a technical person grow business judgment without losing their technical edge?
3. What's a failure, or a risk that didn't pay off, that ended up teaching you more than any success?
4. Was there a mentor, or a single conversation, that changed the direction of your career?
5. For someone dreaming of an international career, what's the honest first step, and the honest hard part?
6. What advice would you give your 25-year-old self, knowing everything you know now?

Closing Reflections

How to end well, and a short checklist for turning this brief into a clean recording.

Production notes

The Big Ideas

Three ideas that carried the released conversation, drawn on the whiteboard. Quotes are lightly condensed; timestamps are approximate.

Added by the show from the released episode; this section and the next are available in English only.

Books & Takeaways

No book got pitched on the air this time; the conversation stayed on the work. What the episode leaves behind instead: one guiding line with a long lineage, six takeaways in Bruno's own words, and every link you need.

Takeaways, in Bruno's words

Question Bank

The Core Questions (the essential ones that would carry the episode on their own) pulled from each chapter and gathered here in one filterable place, English and Portuguese side by side. Each chapter holds its full set; this is the shortlist for live use.