Infino brings unified data retrieval to AI agents, simplifying a fragmented stack

A team of veterans from Amazon, Google, and LinkedIn has built a data retrieval platform tailored specifically to how AI agents work—and it's starkly different from human query patterns. Infino, launched this week, presents itself as a unified retrieval layer that consolidates what has traditionally been a patchwork of separate systems: search engines, vector databases, data warehouses, and ETL pipelines.

Why agents need a different approach#

Infino CEO Ekechi Nwokah describes agents as "the largest new consumer of data since the web browser," yet they're still being served by infrastructure built for a different era. Traditional data stores assume a single, human-initiated query followed by waiting for a result. Agents behave entirely differently.

An agent typically issues many small, rapid-fire questions to answer a larger one—often simultaneously. Each response consumes token context and can add latency and cost to subsequent LLM calls. This creates a fundamentally new access pattern: instead of reading one result from a search engine or vector database and moving on, an agent reads a handful of chunks and asks follow-up questions, or combines rows from multiple systems on its own.

"An agent behaves quite differently. It issues many small questions to answer one large one, often at the same time, and every result it receives consumes context, and can add cost and latency to subsequent model calls," Nwokah explains. Infino's answer is to embed retrieval functions directly into SQL so an entire question can be expressed as a single query rather than orchestrating multiple systems.

Unified data, unified interface#

Infino consolidates structured queries (SQL), unstructured search, keyword matching, semantic search, filtering, and aggregation into one interface running over a single copy of data. Rather than maintaining separate copies of the same information across different systems—each with its own ingestion pipeline and schema—the platform stores data as Apache Parquet files in object storage, with search indexes embedded inside the Parquet footer.

This design allows the data to remain readable by any standard Parquet reader (DuckDB, Spark, data warehouses, etc.) while Infino layers its retrieval capabilities on top. There's no lock-in: the data stays in the customer's bucket and can be accessed by existing tools across different teams.

Setup is simpler too—developers point Infino at their Parquet or JSON files, ingest the data, and immediately begin querying without provisioning clusters, managing shards, or hiring infrastructure teams.