Joan Oliver Poyatos

Co-founded a non-profit delivering SaaS and AI solutions for the social economy. • Defined vision, roadmap, and GTM for Connectia’m and Escaly. Connectia’m: • Launched an AI-first participation platform that reduced reliance on facilitators and sustained community engagement in conservative NGO markets. • De-risked launch with early paying customers and public funding (€85K pre-launch revenue + €29K grant). • Built and deployed AI assistants using ChatGPT API + n8n automations. • Executed lean 12-week GTM campaigns (SEO, LinkedIn, webinars) with SMART KPIs. Escaly: • Created Spain’s first SaaS for standardized social assessment scales, unlocking ~50% time savings in pilots. • Framed the product as an “impact measurement ally,” resonating with compliance-driven directors. • Integrated Supabase (backend), Stripe (subscriptions), and Holded (ERP) to create a scalable financial and operational backbone. • Partnered with federations (Liber, Plena Inclusión) to extend credibility and accelerate adoption.

Led the transformation of an R&D lab into a sustainable SaaS business in the dependent care sector. • Reported quarterly to the Board; aligned product, funding, and strategic priorities. • Covid-era turnaround: Cut 50% of staff, refocused on profitable products, and restored financial health in 9 months. • Institutionalized product governance: introduced Jira + Confluence, quarterly board reporting, and portfolio-level roadmapping. Laundry ID: • Scaled from prototype to Spain’s market leader in care-home garment tracking (40+ deployments, ARR growth from €3K → €120K). • Reframed product from “tracking tech” to board-level decision intelligence, enabling directors to resolve complaints and improve service quality. • Achieved 40× ARR growth with zero marketing budget by pre-selling features and retaining IP. • Negotiated global distribution partnership with a top industrial laundry machinery manufacturer. Nodus: • Built a politically neutral SaaS decision-support platform for public administrations. • Secured €300K+ in public funding and co-investment while retaining full IP. DeliveryBot: • Applied LLMs for adaptive planning of hospital logistics — a pragmatic alternative to complex fleet optimizers.

• Engineered hard real-time C++ control algorithms on embedded Linux for humanoid robots (REEM-A and REEM-B), enabling advanced manipulation using redundant actuators, task prioritization, and real-time visual feedback. • Developed robust firmware for DC and brushless motor controllers and implemented a custom CAN bus protocol to enable synchronized, distributed actuation. • Worked with DevOps practices: implemented unit testing, version control, build automation, and continuous integration pipelines to ensure code stability and rapid iteration. • Built modular software libraries for kinematics, motion planning, and inverse dynamics, accelerating system integration and reuse across projects. • Operated within an agile, cross-functional R&D team to deliver functional prototypes through iterative development cycles. • Supported engineering team growth through recruitment, technical onboarding, and mentorship.

• Designed and implemented motion control algorithms with force/visual feedback in C++ for a 6-DOF industrial manipulator, leveraging complex algebraic models to achieve precise, real-time robotic control. • Developed (soft) real-time C++ software, including a custom mathematical library for matrix and vector operations, to support dynamic constraint handling and adaptive motion behaviors. • Built robust calibration tools and procedures, improving system accuracy and repeatability for academic-industrial research collaboration. • Supported early-stage productization through applied research and iterative prototyping, laying the groundwork for potential clinical applications. • Contributed to dissemination of findings through academic publications and presentations; assisted in lab instruction and mentorship of engineering students.