About Seekar
Most AI tools generate plausible-sounding text. Seekar does something harder: it reads real sources, shows its reasoning, and stands behind every claim with a citation.
The problem with most AI research tools isn't that they're wrong — it's that you can't tell when they are. Confident hallucinations dressed up in fluent prose. No sources. No way to verify. Just trust the machine.
Seekar was born from a different belief: that transparency isn't a feature, it's a prerequisite. Every answer should come with the chain of evidence that produced it. Every claim should link back to the page that said it. Every step in the reasoning should be visible, collapsible, and checkable.
Research shouldn't be a black box. It should feel like working with a meticulous colleague who shows their work.
Under the hood, Seekar runs a dual-model architecture. A primary reasoning model — GLM-5 — handles synthesis, citation matching, and the final answer. A faster secondary model — Step-3.5-Flash — deploys in parallel across up to 20 web pages simultaneously, reading and summarizing each independently before the primary model weighs them.
The result is research that's both fast and thorough. You see the search queries. You see which pages were read. You see the model's thinking. You see the citations. Nothing is hidden.