iora.ai is a data and intelligence company. We capture information at scale — from the open web, from teams working in the field, and from the people using our apps — then verify it, structure it, and turn it into software that businesses and consumers can actually act on.
Most data work stops at a dashboard. We treat capture, verification, and the tool built on top as one continuous job — because an insight nobody can act on is not worth much.
We build the pipelines that collect data where it actually lives: public web and media, connected devices, field teams on the ground, and direct input from the people using our apps.
Raw data is noisy, and AI on top of noisy data invents things. We clean and structure it, then verify every claim against its source, so what comes out the other end can be traced and trusted.
We ship the software that uses it: analysis platforms for businesses, field apps for teams working outdoors, and consumer apps that put the same intelligence in someone's pocket.
Each platform captures a different kind of data and serves a different audience, but they share the same pipeline underneath.
Category intelligence across owned, earned, and paid media. Captures what brands say about themselves, what consumers say unprompted, and what brands pay to say — then triangulates across an entire category.
Learn more →Field organizing for nonprofits, community groups, and campaigns. Door-to-door canvassing, walk lists, phone banking, signs, and coverage analytics on one live map, with routes recorded as volunteers walk them.
Learn more →An operations platform for healthcare practices of any type. Front desk and scheduling, staff and payroll, inventory, patient outreach, and reporting — the day-to-day running of a clinic in one place, for the team inside it and the patients it serves.
Learn more →Practice before you read aloud. Upload an exam and it identifies every field-specific term worth rehearsing, with audio for each one, so readers can deliver unfamiliar terminology with confidence.
Learn more →The same capture-and-verify approach, pointed at everyday problems instead of organizational ones.
An AI outfit assistant for iPhone. Builds your wardrobe from your own photos, learns what you actually wear, and suggests what to put on for the weather ahead.
Learn more →Trivia, sourced. The host’s TV asks the questions and everyone answers on their phones. Every question is generated, independently fact-checked, and shown with its citations — so the answer is never just trust us.
Learn more →A strand of research applying computer vision to aerial and street-level imagery, to measure how physical places change over time and what that change means for the people living in them. Read more about the work →
A per-lot timeline of two New Orleans neighbourhoods after Hurricane Katrina, comparing recovery trajectories lot by lot across two decades of historical satellite imagery.
Street-level imagery analysed for quality of life, heat, and equity across a small city, turning what a street looks like into measures planners can actually compare.
Structured capture from routine field visits, combined with continuous street-level coverage, to build the proprietary datasets that computer vision and operational intelligence depend on.