Managed Apache Iceberg tables and hosted agents for AI to work with data
Agent optimization layer that automatically optimizes agent workloads to extend context windows and reduce costs
Stage
Startup
Not given
Founded
2026
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Based in
ES
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Pricing model
Freemium
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Pricing
Storage €0.10 per GB-month (5 GB free monthly), Compute €0.12 per CPU-hour (10 CPU-hours free monthly), AI tokens €2.50 per million (5 million free monthly). No request, ingress or egress fees.
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Key features
Managed Apache Iceberg tables with automatic maintenance and backups
Multiple data ingestion methods including files, APIs, webhooks, and scheduled URLs
Query with DuckDB, Spark, Trino, Polars, or PyIceberg
Public data catalogs served over a CDN
Automatic spatial clustering for geometry tables
FTP, FTPS and SFTP support for file uploads from cameras and devices
Corporate sign-in and granular access control for organizations
Audit trail and activity log for organizational use
Automatically removes redundant and stale context from agent workflows in real time
Reduces token usage by 37–67% across agent workloads
Maintains baseline pass rates and success rates without code changes
Extends context windows for longer-running workflows
Zero code changes required for integration
Continually monitors and learns agent workloads
What makes it different
Open table format: Apache Iceberg ensures your data remains portable and readable by any compatible engine
Agents keep what they observe, decide and do as persistent tables for learning across runs
Operated in Europe with data storage in Frankfurt and model inference in Paris
Pay only for what you use with no request, ingress or egress fees
Serverless platform with no servers or clusters to manage
Reduces agent inference costs by 37–67% while maintaining the same success rates
Works transparently with zero code changes required
Focuses optimization on removing wasted tokens while keeping success rates high
Enables teams to take on more ambitious problems with the same resources
LakehouseBox
Induction