granica-ai

TransformLoadLoad Petabytes like it's TerabytesSelf-optimizing, lossless, state-of-the-art compression that turns petabytes into terabytes. Halve spend, double speed across Iceberg, Delta, Trino, Spark, Snowflake, Databricks and beyond.BOOK A DEMOgCost Savings DemoQuery Performance DemoThe above demo showcases Databricks, but Granica works seamlessly across Iceberg, Trino, Spark, Snowflake, BigQuery and more.Book a live demo on your lake →gTrusted by Data + AI leaders across the globeSee how top brands trim data bloat, speed queries, and free engineers to focus on new features.Global Revenue-Intelligence SaaS“Crunch halved our 20 PB data lake without a single pipeline change — this is magical.”VP, Data Engineering60%less storage — Hive on AWS$5M+annual ROICONSUMER SOCIAL-MEDIA UNICORN50%storage saved — Delta Lake on GCP2xfaster and lower cost than Databricks' built-in Optimize featureLEADING SOCIAL MEDIA COMPANY$20M+annual ROI — Hive/Iceberg on AWS3xless developer time on data-lake optimizationDIGITAL EXPERIENCE ANALYTICS SAAS3xlower TCO for data platform$3M+annual ROIFORTUNE 500 HEALTHCARE PROVIDER50%less storage — BigQuery/Iceberg on GCP2xlower data transfer costsCompress without limits, spend nothingSelf-optimizing, lossless compression that shrinks storage to pennies and supercharges every model with instant data access.Any LakeWorks with Iceberg, Delta, Trino, Spark, Snowflake, BigQuery, Databricks, and more—zero disruption.Petabytes to exabytesThroughput climbs, latency falls as data grows.Pays for itselfStorage shrinks, compute drops, pipelines fly—ROI in days.BOOK A DEMOgBuilt for structure, optimized for AIEverything you need to run structured AI that just works, forever.Native & TransparentDeploy inside your VPC. Zero code, zero downtime.Continuously AdaptiveLearns every query and data pattern, reshapes compression on the fly.Hands-off OrchestrationSet a cost-performance target once. Granica auto-scales forever.Trusted ControlsSOC-2 Type 2, full audit logs, nothing leaves your cloud.Lineage on TapPipe immutable logs to SIEM, finance, and compliance.Day-zero ActivationOne call. Dashboards show $-savings and performance gains before coffee cools.VIEW DOCSgProven performance at scaleReal-world results from petabyte-scale deploymentsBOOK A DEMOgCompression RatioCost Savings vs Data VolumeQuery Performance vs ComplexityDataset Type (sample)Compression Ratio (%)Query Cost Reduction (%)Best – highly compressible high cardinality data~80%35%Structured – enterprise logs, events & lookups~60%25%Average – Large fact & mixed workloads~40%15%Best – highly compressible high cardinality dataCompression Ratio (%)~80%Query Cost Reduction (%)35%Structured – enterprise logs, events & lookupsCompression Ratio (%)~60%Query Cost Reduction (%)25%Average – Large fact & mixed workloadsCompression Ratio (%)~40%Query Cost Reduction (%)15%Shrink data, shrink bills with SOTA compressionGranica's entropy-aware compression strips out 45–80% of bytes, slicing cloud query spend 15–35% across every workload class.MethodologyDirectional averages blend TPC-DS benchmarks with anonymized telemetry from production clusters (1–100 PB).Validated byDozens of SaaS, consumer-internet, healthcare and transportation deployments ranging from 1 PB to 100+ PB.A self-improving data factory, for AnythingAnalyticsAnalyticsWe're building a new class of data infrastructure for AI. Turn any lake into a self-optimizing data factory—compression today, advanced subsampling and safe synthetic data tomorrow.START BUILDINGgFundamental researchTurning entropy to intelligenceGranica is advancing the state-of-the-art in data for AI. Turning exabyte-scale noise into real-time reasoning. Shifting the world from ETL to E∑L.EXPLORE RESEARCHgScaling laws for learning with real and surrogate dataCollecting large quantities of high-quality data can be prohibitively expensive or impractical, and a bottleneck in machine learning. We introduce a weighted empirical risk minimization (ERM) approach for integrating augmented or 'surrogate' data into training.Read paperNeurIPS 2024Towards a statistical theory of data selection under weak supervisionGiven a sample of size N, it is often useful to select a subsample of smaller size n<N to be used for statistical estimation or learning. Such a data selection step is useful to reduce the requirements of data labeling and the computational complexity of learning.Read paperICLR 2024 Best Paper (Honorable Mention)Compressing Tabular Data via Latent Variable EstimationData used for analytics and machine learning often take the form of tables with categorical entries. We introduce a family of lossless compression algorithms for such data.Read paperICML 2023FAQsGet answers to common questions about Granica Crunch, our advanced compression system for AI and analytics workloads.BOOK A DEMOg01What is Granica Crunch?02How does Crunch integrate with my data stack?03Will Crunch speed up performance?04How is Crunch priced?05Is Crunch secure and compliant?Cookie PreferencesWe use cookies to improve performance, understand website usage, and identify visiting companies through our business intelligence tools. Manage your choices or accept all.Accept AllReject Non-EssentialCustomize --- AboutWE'RE HIRINGgFor three decades data has behaved like unspent energy: vast, noisy, stubbornly expensive to harness. Analytics and ML engines of today tackle this with brute force, shuffling terabytes through extract, transform, and load pipelines and scanning them in the hope of insight.Granica converts that entropy into intelligence. We weave a reasoning fabric into storage itself so curiosity is never throttled by compute and every table speaks back in real time.We are redefining ETL with E∑L: Extract, Signify, Load. During Signify the system learns while it stores. It compresses exabytes yet retains distributions, keys, and temporal drift, then reasons over a high-dimensional latent space. An analyst can spot a supplier defect before the quarter closes without writing a line of SQL, because the answer is inferred from learned structure rather than mined by a late-night scan.Most replies return without touching cold blocks at all. Granica plucks precise subsets, assembles correlations, or generates counterfactual rows in place, and it falls back to deterministic storage only when confidence dips. Transformation becomes cognition, and warehouses sink into quiet archives instead of standing between a question and its answer.Our first product, Crunch, delivers this leap at the foundation. Drop raw data in and watch storage/compute costs collapse while query latency shrinks from minutes to moments. Analysts can now converse with their tables, auditors follow cryptographic traces to ground truth, and CFOs watch understanding rather than input-output dominate the bill.Compute is no longer paid by the byte but by the residual uncertainty of a question. When understanding outruns batch jobs, the legacy data engines fade and curiosity rises. Imagination becomes the only limit on what data can do. Granica opens that door today.Reach out at hello@granica.aiGranica | Query Petabytes like it's TerabytesCookie PreferencesWe use cookies to improve performance, understand website usage, and identify visiting companies through our business intelligence tools. Manage your choices or accept all.Accept AllReject Non-EssentialCustomize --- ResearchNearly every analytic and ML pipeline spends more energy hauling entropy than learning from it. At Granica we frame the research question this way: If you can compress data to within a breath of the Shannon limit, can the compression step itself teach the system enough semantics that storage becomes a reasoning organ? Our answer is E∑L. In the ∑ step, incoming exabytes are squeezed, but also augmented with learned signal. Output then is bounded by residual uncertainty, not by the number of bytes you can brute-force through a cluster.That substrate unlocks a family of frontier problems: loss-bounded compression that preserves analytic fidelity; sub-millisecond subselection that skips 99.9% of blocks; generative augmentation for rare-event inference; retrieval and indexing that exploit grid-aware attention; probabilistic execution plans with deterministic fallbacks, all under continual learning from live traffic.If turning entropy into intelligence at exabyte scale sounds like research you want to stretch,reach us at hello@granica.aiWE'RE HIRINGgFeatured researchScaling laws for learning with real and surrogate dataSYNTHETIC GENERATIONCollecting large quantities of high-quality data can be prohibitively expensive or impractical, and a bottleneck in machine learning. We introduce a weighted empirical risk minimization (ERM) approach for integrating augmented or 'surrogate' data into training.Read paperNeurIPS 2024Towards a statistical theory of data selection under weak supervisionINFORMATION DISTILLATIONGiven a sample of size N, it is often useful to select a subsample of smaller size n<N to be used for statistical estimation or learning. Such a data selection step is useful to reduce the requirements of data labeling and the computational complexity of learning.Read paperICLR 2024Scaling training data with lossy image compressionLOSSY COMPRESSIONTo capture the trade-off between model performance and the optimal storage of training data, we propose a 'storage scaling law' that describes the joint evolution of test error with sample size and number of bits per image.Read paperKDD 2024Compressing tabular data via latent variable estimationTABULAR COMPRESSIONData used for analytics and machine learning often take the form of tables with categorical entries. We introduce a family of lossless compression algorithms for such data.Read paperICML 2023Sampling, diffusion, and stochastic localizationALGORITHMSDiffusions are a successful technique to sample from high-dimensional distributions that are not given explicitly but rather learnt from a collection of samples. We generalize the construction of stochastic localization processes.Read paperInline data detection in large data streamsLOSSLESS DATA REDUCTIONWe present a novel approach to data processing and reduction method that involves receiving an input data stream and computing a set of features that are representative of or unique to the stream.Read paperEfficient data deduplication through sketch computation and similarity metricsLOSSLESS DATA REDUCTIONThe methods provide a more efficient and effective way of handling large data streams, which can be particularly beneficial in applications that require real-time data processing and reduction.Read paperGranica | Query Petabytes like it's TerabytesCookie PreferencesWe use cookies to improve performance, understand website usage, and identify visiting companies through our business intelligence tools. Manage your choices or accept all.Accept AllReject Non-EssentialCustomize --- 6 AI model optimization techniquesAI Development & Implementation6 minBy GranicaJan 23, 2025AI Development & ImplementationFiltered Posts5Cloud Cost Optimization• ResearchScaling training data with lossy image compressionCloud Cost Optimization2 minResearchImproving AI via optimal selection of training samplesResearch8 minInsider• Product• ResearchAdvancing data privacy by tailoring detection to the data typeInsider7 minResearchUsing image compression to improve computer vision (part 2)Research5 minResearchUsing image compression to improve computer vision - part 1Research15 minBlog | GranicaCookie PreferencesWe use cookies to improve performance, understand website usage, and identify visiting companies through our business intelligence tools. Manage your choices or accept all.Accept AllReject Non-EssentialCustomize