Espresso

Espresso is a business intelligence suite developed as part of Project Cafecito, designed to provide curated, structured intelligence for dashboards, monitoring workflows, and AI agents. It delivers concentrated digests of events and synthesized signals, aiming to distill actionable insights from large volumes of raw information. The platform emphasizes providing briefing-ready intelligence, reducing the need to sift through extensive unfiltered news or data streams.

Key features of Espresso include event and signal digests, semantic search capabilities with adjustable similarity thresholds, and tag filtering that allows users to AND-filter by topics, domains, regions, and entities. Users can also combine tag filters with date ranges for more precise and repeatable retrievals. The relationship graph functionality enables traversal of connections such as same_as and derived_from links, helping users follow how events relate to downstream signals and equivalent records across the intelligence graph. For integration with machine learning and large language model workflows, Espresso offers MCP-friendly outputs, supporting both structured JSON and flat plain text responses to accommodate varying token cost requirements.

Espresso is accessible via an API, providing a full endpoint reference, authentication, filtering options, and usage examples to facilitate integration into applications or AI agents. In addition to the API, Espresso Publications delivers human-readable analysis and curated editorials on the web, as well as short-form digests and insights distributed through social platforms like X and Threads. These publications are built on the same intelligence layer, offering deeper analysis and concentrated intelligence for topics such as technology, startups, and market trends.