Granica

Granica: an AI-native data platform for enterprises that own their AI

Granica builds AI infrastructure for enterprises that want to own their AI systems rather than rent them — keeping data, and the intelligence trained on it, inside their own infrastructure. Founded in 2019 and headquartered in Mountain View, the company attacks the first-mile problem of enterprise AI: the cost, efficiency, and safety of the data that everything downstream depends on.

The platform spans three layers. Crunch is a data lake optimization engine that continuously refines data layout, file sizes, and statistics, cutting storage by 20-50% and accelerating queries — Granica cites roughly $200K per petabyte in annualized ROI. Myelin provides stateful infrastructure for long-running AI agents, preserving agent state across sessions so work resumes without rebuilding context; users report running 4.2B tokens of agent work per day on it. Granica is also developing Large Tabular Models (LTMs), generative models designed specifically for enterprise tabular data rather than text and images.

Key Features

• Crunch data lake optimization: continuous refinement of layout, size, and statistics for 20-50% storage reduction and faster queries
• Roughly $200K per petabyte in annualized infrastructure ROI, per company benchmarks
• Myelin stateful agent infrastructure that persists state across sessions for long-running agents
• Production agent scale: users report 4.2B tokens of agent work daily on Myelin
• Large Tabular Models (LTMs) purpose-built for generative AI over enterprise tabular data
• Deploys within the customer's own infrastructure, so data and trained models never leave

Ideal Use Case

Data platform and ML infrastructure teams at enterprises with petabyte-scale lakes and growing agent workloads. Granica fits organizations where cloud storage and query spend is a board-level line item, where compliance demands that data stay in-house, and where long-running agents need durable state rather than stateless prompt-stuffing.

How Granica differentiates

Granica has raised more than $60M from NEA and Bain Capital, and its founding team draws engineers from Stanford, Google, Amazon, and Snowflake. Its customer list is unusually concrete for data infrastructure: Snapchat, Pinterest, Quora, Grab, ShareChat, Bigabid, Quantum Metric, and 6sense. Where most AI data vendors optimize a single slice, Granica pairs storage-level economics (Crunch) with agent-level state (Myelin), betting that owning the data layer is what makes enterprise AI affordable and safe at scale.

FAQ

What does Granica Crunch actually do? It continuously optimizes data lake layout, file sizes, and statistics, reducing storage 20-50%, speeding queries, and lowering infrastructure cost without changing the data itself.

What is Myelin for? Myelin gives long-running AI agents stateful infrastructure: agent state persists across sessions so agents resume work without rebuilding context from scratch.

Does my data leave my environment? No — Granica's positioning is AI you own, with data and trained models kept within your own infrastructure.

Who uses Granica? Named customers include Snapchat, Pinterest, Quora, Grab, ShareChat, Bigabid, Quantum Metric, and 6sense.

tl;dr

Granica is an AI-native data platform backed by $60M+ from NEA and Bain Capital: Crunch cuts data lake costs 20-50%, Myelin gives long-running agents persistent state, and LTMs bring generative AI to tabular enterprise data — all inside infrastructure you own.

Related

Looking for more options? Browse the AI Infrastructure directory or read our best AI infrastructure tools listicle. Granica is also tracked on Crunchbase.