Macrobond Appoints Pedro Rodrigues as Chief Platform and AI Officer to Build the Missing Reasoning Layer for Macroeconomic AI
AI / Machine Learning | 4 min read
Macrobond, the leading macroeconomic data and analytics platform trusted by central banks, asset managers, research institutions, and financial data teams worldwide, has announced the appointment of Pedro Rodrigues as Chief Platform and AI Officer. The appointment marks a defining moment in Macrobond's evolution — from a trusted data provider into a fully AI-native intelligence platform for the global macro research community. Rodrigues brings more than 15 years of experience building production-ready AI systems for global financial institutions, most recently as CTO and Co-Founder of Engine AI. He arrives at a moment when AI experimentation across financial services is near-universal, but production deployment is not — because the reasoning layer that allows AI to operate on economic information with macroeconomic logic, semantics, and domain understanding does not yet exist at scale.
"Macrobond has spent 20 years building the data foundation that AI needs to be trustworthy in financial research. My role is to unlock what that foundation makes possible."
— Pedro Rodrigues, Chief Platform and AI Officer, Macrobond
The Problem: AI Experimentation Is Universal. Production Deployment Is Not.
Macrobond's proprietary survey of 400 global macro research professionals identified the precise blockers preventing institutions from scaling AI in macro research. The bottleneck is not willingness — 81% of peers plan to increase investment in AI and workflow tools over the next two to three years. The bottleneck is confidence in the data and methodology underneath the outputs. 42% identified higher-quality underlying data as the single biggest factor for broader AI adoption. 44% named explainability and transparency as the biggest fix the industry needs. Most AI tools in financial services fail not through obvious error but through something harder to catch: outputs that are plausible, well-formatted, and wrong in ways only a domain expert would recognise. In financial research, that is not a minor inefficiency — it is institutional risk. Addressing that gap requires three things working together as a single connected system: domain knowledge embedded into the reasoning layer itself; AI capability designed for the specific demands of macroeconomic work; and workflows that reflect how macro experts actually think and operate.
The Foundation: 300 Million Time Series and 20 Years of Data Governance
Rodrigues inherits a data infrastructure that Macrobond argues no competitor can replicate quickly: more than 300 million normalised time series from 2,500 global sources, curated and governed over 20 years, with full data lineage and traceable transformations built into the platform from the start. Where most AI tools in financial services are layered on top of fragmented data, Macrobond's intelligence layer grows from the data up — making it purpose-built rather than retrofitted. The platform's data governance, provenance tracking, traceable transformations, and domain-specific AI guardrails address the precise blockers preventing institutions from scaling macro AI in production. Macrobond's vision centres on serving three audiences: Insight Producers (economists, analysts, strategists) who need to move from data discovery to defensible published view without manual reconciliation or trust gaps in the AI layer; Decision Makers (CIOs, portfolio managers, chief economists) who need research in the flow of their work with traceable reasoning; and Builders (developers, quants, platform teams) who need licensed, governed macro data that plugs directly into infrastructure, models, and agentic workflows. The next phase of macro research, Macrobond argues, will not be defined by who has access to the most data — it will be defined by who can make that data intelligent, traceable, and ready to act on. Macrobond is backed by Francisco Partners.
Key Takeaways
- • Macrobond (leading macroeconomic data and analytics platform; backed by Francisco Partners; trusted by central banks, asset managers, research institutions, and financial data teams) has appointed Pedro Rodrigues as Chief Platform and AI Officer — marking the company's transition from a data provider into a fully AI-native intelligence platform for the global macro research community.
- • Pedro Rodrigues: 15+ years building production-ready AI systems for global financial institutions; most recently CTO and Co-Founder of Engine AI. His remit at Macrobond is to build the missing reasoning layer — the system that allows AI to operate on economic data with macroeconomic logic, semantics, and domain understanding that macro analysis requires, and that is currently absent from the architecture of most financial AI tools.
- • Macrobond's survey of 400 global macro research professionals: the AI adoption bottleneck is not willingness (81% plan to increase AI and workflow tool investment in the next 2–3 years) but confidence in the underlying data and methodology. 42% cited higher-quality underlying data as the single biggest factor; 44% named explainability and transparency as the biggest fix needed. Most financial AI failures produce plausible, well-formatted, but wrong outputs — a form of institutional risk that only domain-embedded reasoning can prevent.
- • Macrobond's data foundation: 300+ million normalised time series from 2,500 global sources; curated and governed over 20 years; full data lineage and traceable transformations built in from the start. This infrastructure enables purpose-built AI (reasoning growing from the data up) rather than retrofitted AI (layered on top of fragmented data) — the distinction Macrobond argues no competitor can replicate quickly.
- • Three platform audiences: Insight Producers (economists, analysts, strategists — data discovery to defensible published view without manual reconciliation or AI trust gaps); Decision Makers (CIOs, portfolio managers, chief economists — research in their workflow with traceable reasoning and confident action); Builders (developers, quants, platform teams — licensed, governed macro data for infrastructure, models, and agentic workflows). Rodrigues' appointment accelerates Macrobond's ability to serve all three at enterprise scale in real time.