The Efficiency Layer for Enterprise AI
Granica helps you run enterprise AI for less, from the data layer to agent execution. Crunch reduces your storage and processing costs. Large Tabular Models unlock predictions and generated data from governed enterprise tables. Myelin reduces token and compute overhead for long-running agents. Everything runs inside your perimeter, so you keep control of your data and the intelligence it creates.
- Data stays
- Runs inside your cloud. Granica never needs your bytes.
- Time to value
- Four weeks from kickoff to verified savings.
- Commercial model
- Outcome-based pricing tied to the value we create.
Lower the cost of every layer of enterprise AI.
Trusted by data-intensive teams
AI should belong to the enterprise that runs it.
Everything we build follows from that.
- 001.
Efficiency
You cannot scale what you cannot afford to run.
The cost of intelligence is the cost of moving data and computing on it. You do not get past that by spending more. You get past it by spending less per unit of intelligence. So we compute where the data already lives. The most capable intelligence we know runs on twenty watts. We treat that as the standard, not the exception.
- 002.
Trust
What's yours stays in your house.
Data stays in the customer's environment, and so do the models trained on it. We keep no copy. Trust earned this way isn't a promise in a contract. It's a property of where the system runs.
- 003.
Control
It answers to the people who own it.
The infrastructure we build for agents makes ownership literal: humans and agents act on the same data under the same controls, and authority never forks from a person to an agent.
Join Granica
If this is the work you want to be doing, join us.
We are a lean team of builders from Stanford, Google, Amazon, and Snowflake. We are backed by NEA and Bain Capital, with more than $60M raised.
Crunch · Data infrastructure
Cut data lake costs by 20% to 50%.
Crunch runs continuously in the background, following the policies you set to compress, organize, and maintain your data. Storage shrinks, queries scan fewer bytes, and your engineers stop babysitting recurring maintenance jobs.
Benchmark highlights vs. Databricks Auto Loader
- Up to6.0×
- lower cost per TB
- Up to3.8×
- higher throughput per core
- Up to74.6 pp
- higher data reduction rate
Granica Crunch cut serving cost per MAU, and unit economics improved with it.
$200K / PB
Annualized ROI across storage and compute.
4 weeks
Time to value, from kickoff to verified savings.
Hundreds
Recurring jobs eliminated, freeing engineers.
“Lowering the infrastructure cost to support each MAU is critical for us as the platform scales. Granica handled the heavy lifting, from setup to daily scheduling to verification, so we saw real savings quickly without disrupting our Delta pipelines, and could redirect engineers back to building the platform.”
Arya Ketan
Distinguished Engineer, ShareChat
Myelin · Agent infrastructure
Long-running agents that do not lose their place.
Myelin keeps agent state alive across sessions, machines, and handoffs. Agents resume with the files, decisions, and constraints they already learned, which reduces wasted tokens, repeated turns, and the cost of accepted work.
- 95.6x
- context resumed from cache instead of rebuilt
- 10x
- more agent sessions since adopting Myelin
- 4.2B
- tokens of agent work run each day
Granica Research Lab
Rigorous research on the data foundations of AI
At Granica, our dedication to efficiency is rooted in fundamental research on the problems at the heart of understanding and working with large-scale data. In the age of massive data, questions around data selection and compression become paramount in practical workflows:
- How do you subsample a dataset so that training stays tractable without throwing away the signal?
- When labels are scarce, can a small, expensive-to-collect dataset borrow strength from a larger one in a related domain?
- And how far can data be compressed before the structure that makes it useful begins to disappear?
Failure to leverage data properly means models trained at enormous cost, only to learn the wrong signal; pipelines that buckle under scale; and sub-optimal decisions made on data that was never representative in the first place.
The Granica research team has developed answers to such non-trivial questions, leading to published work at top machine learning venues such as ICML, ICLR, KDD, and NeurIPS. One of them, Towards a statistical theory of data selection under weak supervision, earned an honorable mention for Outstanding Paper at ICLR.
The insights behind this work flow directly into the products we ship.
Large Tabular Models: What we are building
Granica develops efficient solutions to hard data problems arising in enterprise. We see the next frontier as bringing generative AI to tabular data.
Data in enterprise workflows naturally comes packaged as tables, which the current text-and-image generative paradigm was never designed for. Bringing the full power of generative AI to enterprise therefore calls for a class of models that works natively with tabular data. We refer to these as Large Tabular Models (LTMs).
Building an LTM comes with a host of new challenges. The same questions we have tackled in our prior research are exactly the ones that prove critical to whether a tabular generative model succeeds: how to select, augment, and compress data without losing what matters. An LTM must be trained on judiciously selected data to learn well, and it must draw on related datasets to augment domains where examples are scarce. For LTM training to be tractable, the data must be compressed enough to fit within computational limits, without discarding the structure that carries the signal.
We bring our expertise on these questions directly to bear on building LTMs. For more on the new frontier, and the first step we’re taking toward building them, see our post and repo below:
Selected publications
- Train on Validation (ToV): Fast data selection with applications to fine-tuningICLR 2026Read paper
- Scaling laws for learning with real and surrogate dataNeurIPS 2024Read paper
- Towards a statistical theory of data selection under weak supervisionICLR 2024Read paper
- Scaling training data with lossy image compressionKDD 2024Read paper
- Compressing tabular data via latent variable estimationICML 2023Read paper
- Sampling, diffusion, and stochastic localizationPreprintRead paper
- Inline data detection in large data streamsUSPTO patentRead patent
- Efficient data deduplication through sketch computation and similarity metricsUSPTO patentRead patent