Methodology
ModelHQ exists to separate the AI changes that matter from the noise. This is how the pipeline works, end to end:
1. Source filtering
We continuously ingest from a curated set of primary sources — model vendors, research labs, developer platforms, regulators, and established technology press. Sources are scored for reliability and relevance; low-signal and duplicate feeds are pruned.
2. Signal tracking
Every item is classified against the AI ecosystem surface we track: model releases, API/SDK and pricing changes, benchmarks, funding and market moves, compute and infrastructure, governance and policy, and research. Items are routed to the appropriate desk (Model Wire, Power Map, Research Digest, and so on).
3. Scoring and de-duplication
Our editorial system (VEAS) scores each item for quality, freshness, relevance, and engagement, and collapses duplicate coverage of the same underlying event. Only items above the quality threshold reach the site and briefs.
4. Summarization and briefing
High-scoring signals are summarized into plain-English briefs oriented around decisions: what changed, why it matters, who it affects, and what to consider doing about it. Executive summaries in HQ Brief add ROI, risk, and governance framing.
5. Human review
Automated curation is reviewed by editors. Corrections and removals are applied at the source so the same error does not recur.
Questions about our standards or a correction request? Contact us.