People Analytics

Patria Investimentos (Gente & Gestão)

Client

Venture Building, AI Product Validation,

Services

2025

Patria’s G&T team wasn’t blocked by lack of HR data — they were blocked by the effort required to turn data into decisions across multiple investidas. The reporting workflow was long, manual, and prone to rework. OUTCOME We designed People Brain: an MVP product experience that compresses the “Excel/BI → deck → alignment” cycle into a guided analytical workspace — where insights are surfaced automatically, exploration is structured, and knowledge accumulates over time.

MY ROLE (Principal Product Designer) I led the product design from concept to a validated prototype: • translated the “Brain” platform vision into a client-ready MVP experience • defined the information architecture and the core interaction model (how the product reveals insights + guides drill-down) • designed and iterated the insight cards system (structure, content hierarchy, and tone of voice) • facilitated multiple testing rounds with Patria, validating: the concept, end-to-end UX, insight card readability, and voice

The problem: The existing reporting process had: a multi-step workflow split between investidas and G&T ~60 days from start to delivery high cognitive load, frequent rework, and low scalability The core insight behind the project was simple: It’s not a data problem. It’s a cognitive load problem. Success criteria: We anchored the MVP around measurable outcomes: • Reduce 75% of the end-to-end reporting cycle time • Reduce 90% rework after delivery What we built 1) A guided “thinking workspace” (not another dashboard) Instead of giving users more charts, the product focuses on: faster comparisons across investidas automatic detection of what changed guided exploration into the “why” A key principle: the system should highlight relevant variations and suggest where to deepen — reducing noise and reading time. 2) An MVP flow that matches the real workflow The experience is structured from: signal → comparison → diagnosis → deep dive → narrative → action plan → learning 3) An IA built for decision cadence We defined the product map and page model (MVP scope alignment): Home · Analytics · Visão Geral/Aprofundada · Workforce · Conversation · History · Collections · Reports plus knowledge/report views, AI chat, agents hub, action plan, and automation/knowledge hub. Testing & iteration: We ran multiple rounds of testing with Patria stakeholders, iterating on: • whether the concept matched their mental model (“is this useful / trustworthy?”) • the navigational logic and drill-down paths (“can I get to why quickly?”) • insight card structure (signal clarity, comparability, actionability) • tone of voice (confidence without sounding like a black box) This is where the product shifted from “promising concept” to something decision-makers could actually use.