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Continuous ingestion, consistency model, and the architectural difference from streaming + downstream search.
Ingestion and query paths are decoupled. New content is structured and committed to the live graph through an asynchronous pipeline, while queries hit a consistent view that updates in near real time. There's no batch reindex window. The graph is always live, always queryable.
Ingestion-to-availability throughput: parsing, entity extraction, relationship mapping, vector embedding, and graph commitment. The figure represents sustained throughput, not peak burst, and includes the steps needed to make content queryable rather than merely stored.
Through continuous reconciliation against the existing graph. New mentions are matched to existing entities using a combination of vector similarity, structural context, and temporal signals. Ambiguous cases create candidate links rather than forcing premature merges, allowing resolution to refine as more evidence arrives.
What Curve is for, who benefits, and how it differs from alerts and dashboards.
Catalyst gives you depth, the full picture as it stands. Curve gives you live awareness, how the picture is changing, right now. Same foundation underneath (ColossioDB), different layer on top.
Anyone whose work depends on noticing things early: analysts, researchers, intelligence teams, investors, policy advisors. If the difference between knowing on Monday and knowing on Friday matters to you, Curve is built for you.
New content becomes part of the live picture within moments of arriving. There's no overnight refresh, no batch update window. When something happens, it's reflected almost immediately and pushed live.
Curve doesn't push every change at you. It maintains a continuously evolving view of relationships and signals, and surfaces what matters when you ask, or when something shifts significantly.
Alerts flag keywords and dashboards show numbers without interpretation. Curve shows meaning: how new information connects to what you already know, what it changes, and what it implies. That's the difference between data and insight.
The detection asymmetry case, who benefits, and how to measure success.
Curve's value is detection asymmetry. The ability to spot trends, correlations, and emerging signals earlier than competitors compounds in ways that time-savings calculations don't capture. In markets, intelligence work, and competitive strategy, the gap between knowing on day one and knowing on day fourteen is rarely a small efficiency gain. It's frequently the entire opportunity. Curve closes that gap.
Anyone whose work depends on early detection: investment teams, competitive intelligence, regulatory affairs, business development, policy and government affairs, market research, security and risk teams. If your organisation has someone whose job involves the phrase "we should have seen this coming," Curve is for them.
The honest answer: through case studies, not dashboards. Success looks like we identified the trend before it was a trend, or we caught the competitor's strategic shift in week one rather than quarter two, or we spotted the regulatory signal before it became a regulation. Most Curve customers track these qualitatively, because each one tends to be worth more than the platform's annual cost.