We normalize public, licensed, and consented first-party data into source-attributed, aggregate signals, and hold every source to the same governance, attribution, and privacy commitments, whether it runs in one of our own indices or on a partner's platform.
Our research explores how interconnected technologies (APIs, data infrastructures, and consumer devices) collect, transmit, and interpret information.
Investigating how consumer devices, CTV platforms, and advertising SDKs transmit data across networks. Our data telemetry mapping identifies opaque behaviors and supports privacy-centric design standards.
Studying the transparency and accessibility of institutional APIs (civic, governmental, and regulatory datasets) to inform equitable, accountable data governance. Active projects: PEPI Index & Civic Forecast.
Through initiatives such as Data Privacy Today, including our “What You Need to Know in 90 Seconds” series, we transform technical research into accessible public education.
Governance is a claim about how something is run, so here is the run. Each stage is governed, and no stage re-exposes source records, which is precisely what makes a derived index publishable where the records behind it are not.
Authorized API access or licensed feeds, with caching, retries, and rate-limit compliance, and only ever through a provider's sanctioned method and credentials.
Mapped into internal schemas with source provenance preserved as a first-class field rather than reconstructed later from memory.
Joined to the stable geographic and entity identifiers each source supports, so records aggregate without record-level exposure.
Aggregates, deltas, rates, per-capita values, severity bands, and topic tags. Transformative indicators, not the source records.
Indices, cards, and curated delivery to licensed destinations, with source attribution carried onto every surface it lands on.
Aggregate usage only: counts, rates, cohorts, and geography or topic buckets. No user-level export; partner reporting is aggregate-only.
Built on public data infrastructure and the same data telemetry that runs our products, from live indices and a research model to investigative telemetry work.
Presidential Economic Pressure Index, a real-time composite tracking U.S. trade policy pressure on domestic markets. Ingests 15+ public sources: BLS, Census FT-900, USITC HTS, and the Federal Register. Government API data structured into a queryable, continuously-updated intelligence layer. Available on the Snowflake Marketplace.
Civic Forecast surfaces structured signals from public legislative, regulatory, and administrative data, giving organizations advance visibility into policy shifts before they move markets. Powered by the same API infrastructure that underlies PEPI.
API Automations' proprietary deep-research and database language model: the retrieval and reasoning layer over our own indexes, built to answer questions against sourced records rather than open-web text.
Mapping cross-device surveillance and API telemetry in Connected TV ecosystems, identifying opaque data relationships within advertising SDK networks across Roku, Fire TV, Android TV, and Smart TV environments.
API-based public data analysis examining how FOIA intersects with modern government API architectures, and what it means for institutional accountability.
Stated plainly, because it bounds how any supplier’s data can be used here, and a boundary a partner cannot check is not a boundary.
The same undertaking regardless of who the provider is or what the source costs. Our full sourcing and attribution practice is set out in Open APIs & Public Data.
Some of what the pipeline computes is worth putting in front of peer review, where a method either survives being read by people paid to find its faults or it does not. That is a programme with gates, not an intention: each is answered in writing before a manuscript leaves, and there are no partial sign-offs.
The cleanest route through the ethics gate is an academic collaborator whose institution acts as review board of record. That is a real dependency rather than a courtesy, and we would rather say so. Partners get a documented method, a governed pipeline, and a corpus that is already computed, not a data dump to clean.
We combine empirical analysis with system-level inspection. All experiments are performed in controlled, consented data environments and documented for peer review and open publication.
We work with institutes, journalists, and industry partners advancing data ethics, privacy innovation, and API standardization.
