Why We Built Incord
The Incord Team·May 2026·5 min read
AI agents are only as good as the data they can reach. Here's why we built a real-time knowledge layer instead of another web-search wrapper.
The problem with chat-time search
Most AI agents reach for live information the same way: they call a web-search API in the middle of a conversation, wait for a crawl, and hope the result is relevant. That works for a demo. It falls apart in production.
Every query pays a latency tax while the open web is crawled and scraped. Results come back unranked and unstructured, and the model still has to read pages of noise to find one number. For anything time-sensitive, a price, a headline, a policy decision, the answer is often already stale.
A brain, not a search box
Incord flips the order of operations. Instead of fetching at chat time, it ingests the world ahead of time, continuously, embeds every item into a knowledge graph, and ranks it by relevance and freshness.
When your agent needs context, it makes a single /v1/context call. It gets back the top-K most relevant, already-embedded facts in milliseconds, with a confidence score and did-you-mean hints. No crawl, no scraping, no guessing.
Real-time by default
A heartbeat loop pulls market data, news, and global events on cadences from five minutes to daily, across 51+ sources and five asset classes. Each fetch is embedded in-process and written straight into the graph.
The result is a data layer that's never more than minutes behind reality, so your agent answers like it read the news this morning, because it did.