ShelterIQ is a data platform purpose-built for large municipal shelter systems โ turning raw intake and outcome data into actionable predictions that help more animals find homes.
So every animal gets the right care, at the right time, from staff who know exactly what to do next.
ACC is our alpha. A multi-tenant platform for every shelter system is the vision.
AI-powered tools purpose-built for municipal shelter systems โ from predictive risk scoring to real-time leadership dashboards.
A borough-indexed, animal-level data architecture connecting intake, outcome, behavior, medical, foster, and community records across all five shelters โ the single source of truth.
InfrastructureIntake forecasting per shelter, surrender risk scoring, length-of-stay prediction with cross-shelter transfer optimization, and foster conversion models โ built with production-grade pipelines.
ML / AILive, per-shelter views replacing static reports โ tracking intake, placement rates, capacity, and community program effectiveness in a format non-technical staff can act on immediately.
DashboardsEvery architectural decision is made with portability in mind. The schema, pipelines, and dashboards built for ACC form the core of a scalable product for municipal shelter systems nationwide.
ScaleModel outputs translated into plain-English recommendations. Which animals are at risk? Where should transfers go? What drives foster conversion? Answers staff can use โ without needing a data background.
UXSimulation tools that model shelter population over time โ letting leadership stress-test policies, plan for intake spikes, and measure the systemic impact of community programs before rolling them out.
ResearchA lean, experienced founding team combining deep data science expertise, healthcare strategy, and applied machine learning.
Former Senior Partner and Global Life Sciences Practice Leader at McKinsey & Company. Roy brings decades of experience advising large healthcare systems, biopharma companies, and multi-site organizations on data strategy, R&D, and operational transformation. He is a board member at ACC and the connective tissue between ShelterIQ and its alpha partner.
Lead Data Scientist and Machine Learning Engineer at ShelterIQ. Previously at IBM, holding Data Science Professional Certification Level 2 Expert. Mona is a licensed professional engineer (P.E.) and LEED AP with deep expertise in applied data science, Python, SQL, and ML model deployment. She mentors the next generation of data scientists through Coursera and IBM's training programs.
Data Scientist with a track record of extracting actionable insights from complex healthcare datasets at AllazoHealth, and applied analytics experience at Raymour & Flanigan. Trained at Columbia University with a Business Analytics concentration, Claudio brings rigorous modeling skills โ from time-series forecasting to survival analysis โ paired with a strong sense for communicating results to non-technical stakeholders.
We don't assume we know the problems. We're visiting ACC to understand what staff actually face โ and then we build.
We're visiting ACC shelters across the five boroughs to interview staff, understand day-to-day workflows, and map the real pain points that data could address.
In progressDesign and implement a unified shelter database, establishing data pipelines that connect all five boroughs and serve as the foundation for every model downstream.
Coming nextDeploy intake forecasting, transfer optimization, and foster conversion models at ACC โ then validate the platform with other large municipal shelter systems nationwide.
The visionWhether you're part of the ACC team, another shelter system, or a potential collaborator โ reach out. We're in discovery mode and we want to learn.
mona.hatami@shelteriq.org