Infino AI

Overview

Infino is a search and analytics infrastructure platform designed to execute multi-modal queries for AI agents directly over Parquet files on object storage. It was developed by the creators of AWS OpenSearch and engineering leaders from Amazon, Google, and LinkedIn, including Ekechi Nwokah, Vinay Kakade, Asif Makhani, and Murali Krishna. The company raised $7.5 million in seed funding led by Bessemer Venture Partners to unify keyword, vector, graph, and SQL analytics into a single data layer.

Key Features

• Unified SQL retrieval — Embeds full-text, vector, and graph traversal functions directly into SQL queries to eliminate multi-database orchestration for AI agents.
• Direct Parquet storage — Stores searchable indexes inside standard Parquet files on cloud object storage such as Amazon S3, Google Cloud Storage, and Azure Blob.
• Open table format compatibility — Operates alongside table managers such as Apache Iceberg and Apache Hudi so existing Parquet readers retain data access.
• Hybrid search ranking — Fuses keyword matching and semantic vector retrieval within the same query pass to maximize answer accuracy.
• Integrated inference models — Couples targeted machine learning inference with the retrieval engine to evaluate intermediate agent steps without external frontier model calls.
• Real-time ingestion — Ingests raw JSON and Parquet data into user-owned buckets without requiring separate stateful search clusters.

Who It's For

Infino is built for software engineers, machine learning teams, and data architects developing autonomous AI agents and retrieval-augmented generation workflows. It serves technical organizations managing multi-billion-row analytical workloads across sectors such as government, document processing, CAD design, and observability. The platform is primarily suited for teams seeking to query data lake storage directly rather than syncing multiple downstream specialized databases.

Notable Strengths

Infino consolidates fragmented stacks—replacing separate deployments of tools like Elasticsearch, Pinecone, Neo4j, and ClickHouse—by serving diverse query types from one data copy. By storing data in customer-owned cloud object storage and utilizing a read/write token pricing model, it eliminates the operational overhead and high hosting costs of dedicated search cluster nodes. Furthermore, the engine demonstrates validated performance, ranking first among search engines on ClickBench SQL analytics benchmarks while handling multi-billion-row scale.