Snowflake stores your data. Eisberg runs your company.
The data warehouse era served the 10% of your company that writes SQL. The agent era serves everyone — and it runs on a different platform. Eisberg is the autonomous intelligence OS, built GPU-native and agent-native from line one. We watch every signal your business produces — every query, every Slack thread, every pull-request comment — build your business ontology in two weeks, sign every agent at the compute layer, and run every team on data you own in open format. Your data warehouse is a 2012 architecture. This is 2030, shipping today.
a quarter on Eisberg · autonomous
- 1,247tablesunified across 8 source systems into one coherent business ontology
- 156rulesof tribal knowledge captured from analyst conversations and Slack threads
- 38incidentsof schema drift auto-remediated without paging on-call
- 47saudit packgenerated for SR 11-7 model validation, regulator-ready
- $2.1Mreclaimedin annualized compute by routing the heavy aggregations correctly
- 12opportunitiessurfaced from your data that the analytics team had missed
Representative outcomes from a mid-market FinServ design partner. Yours will be measured against your real workload.
In the design-partner pipeline · names under NDA
Top-10
US Wealth Management
Global
AmLaw 50 Firm
Top-20
US Health System
Series-D
Fintech
Fortune 500
Industrial Manufacturer
Federal
Civilian Agency
Eisberg learns your business. Then it runs every team on what it has learned — under signed agents at the compute layer.
Snowflake stores your data. Databricks runs your notebooks. Catalog tools document your assets. Palantir delivers an ontology after 18 months of consulting. None of them combine all three pillars below. Eisberg learns your business (the Knowledge Layer + ontology). Eisberg extracts and binds the tribal knowledge that lives in Slack and Confluence and pull-request comments (not just linking — extracting). Eisberg signs every agent with a cryptographic Birth Certificate enforced at the compute layer where the action actually runs (catalog tools certify metadata; Eisberg governs actions). The three together is what makes the platform structurally different from anything else shipping today.
The Knowledge Layer
Every query your team runs, every action your agents take, every classification, every approval, every fix — captured as institutional memory. Your business ontology auto-discovered from your data + workload in two weeks. The platform gets smarter from operating, not from training.
Tribal Knowledge — extracted, not just linked
The actual rules your company runs on are not in your warehouse. They are buried in a 1998 stored procedure, an Excel VBA macro, an ABAP routine, an email from the controller in 2019, a Slack thread, a Confluence page nobody updates. Catalog tools embed a link next to the table. Eisberg parses the code body, the macro body, the message body — extracts the rules and exceptions, binds them to your ontology, signs the provenance, cites them in every agent answer.
Signed Agentic OS — at the compute layer
Every agent receives a cryptographically signed Birth Certificate at creation — intent, scope, allowed data classes, action ceiling, expiry, pinned policy hash. The Job spine refuses to compile any step whose certificate fails verification. Catalog tools certify metadata about agent observations. Eisberg governs agent actions themselves — at the compute layer where the action actually runs. The structural answer to EU AI Act Articles 11 + 12.
Not a cheaper warehouse. The OS that makes the warehouse model archaic.
The warehouse era served roughly 10% of your company — the data team. Treasury still waits for a dashboard. Underwriting still maintains a spreadsheet. Demand Gen still pings the analyst Slack. Clinical Ops still maintains a CSV. Talent still emails the recruiter. Loan Servicing still runs nightly batch. S&OP still re-keys data into Excel. Quants still maintain their own feature store. Pharmacovigilance still triages signals by hand. Field Service still dispatches from a whiteboard. Internal Audit still pulls evidence by hand. Eisberg is the agentic OS those 90% configure themselves — every function, every sub-function, every workflow in every industry — on the same governed substrate, with the same primitives, with the same agent-governance plane. GPU-native, agent-native, customer-owned. This is what makes Snowflake and Databricks look like fax machines. If your company does it, Eisberg runs it.
Pick a team
Data engineering
Pipelines stop paging on-call.
Schema drift detection + auto-remediation
→ Pull-request-style summaries replace 2am pages.
Source-to-warehouse pipeline generation from natural language
→ What used to be a sprint is a 30-minute review.
Per-query routing across the right engine for cost and latency
→ Engineers stop tuning warehouse sizes for every workload.
CDC + streaming pipelines with auto-classification on arrival
→ PII / PCI / PHI never lands ungoverned.
Dependency graph maintenance — orphaned views surfaced and proposed for cleanup
→ Tech debt gets a queue, not a graveyard.
The eight things every function in your company configures.
Snowflake hands data engineers tables and SQL — useful for the 10% who write queries, useless for everyone else. Eisberg hands every function eight primitives — connectors, storage, engines, semantic layer, agents, governance, workflows, ontology — that each team customizes for their own work. GPU-native compute. Agent-native architecture. Customer-owned data in open Iceberg format. The six moats below are structural properties of the OS itself, designed in from line one. This is the architectural shift that retires the warehouse era.
Enterprise knowledge layer
Every business term, every metric definition, every tribal rule that lives in a Slack thread or an analyst's head — captured automatically and reasoned against. Not a glossary you maintain. A living knowledge graph the platform builds for you and uses on every query.
Automated business ontology
The platform discovers your entities, your relationships, your business processes across every source — Snowflake, Salesforce, ERP, sensor streams — and assembles them into a single coherent model. Six months of data modeling work, replaced by two weeks of letting the platform watch.
Agentic governance
Policy as code that AI agents read, enforce, and explain. Every classification, every mask, every action governed by risk-graded approval gates and tamper-evident audit trails. The first data platform built so agents can be trusted with real authority.
Autonomous operations
Pipelines that fix themselves when schemas drift. Anomalies that triage and remediate themselves. Compute that routes itself to the cheapest engine that can answer the question. The platform stops being something you operate and becomes something that operates for you.
Composable open architecture
Apache Iceberg in your bucket, behind your KMS keys, in your cloud. GPU-priced compute on neo-clouds, hyperscaler when you need it. Every layer replaceable, every byte portable, every component auditable. The opposite of vendor lock-in by structural design.
Compounding intelligence
The platform learns from every query, every classification, every successful agent action — across every customer who opts in. The longer you run it, the smarter it gets. The more customers we have, the smarter yours becomes. A moat that grows on its own.
The honest version of the head-to-head.
Every row is something you can verify in a 30-minute demo on your data. We will not claim a capability we cannot show end-to-end on your workload.
| Capability | Eisberg | Snowflake | Databricks |
|---|---|---|---|
| Knowledge Layer — learns your business from operating | |||
| Tribal knowledge extracted + bound (not just linked) | |||
| Cross-domain stitching (business + software + comms entities) | |||
| Signed agent Birth Certificates (cryptographic identity) | |||
| Compute-layer agent enforcement (not metadata certification) | |||
| Automated business ontology (2 weeks vs 18 months of consulting) | |||
| Customer-owned object storage | |||
| Open format (Iceberg) by default | |||
| GPU-native query engine | |||
| Sub-100ms agent API targets | |||
| MCP-protocol native | |||
| Per-action agent metering | |||
| Policy-as-code governance at every layer | |||
| Compliance modules (BCBS 239 / SR 11-7 / HIPAA / 21 CFR Part 11) | |||
| Autonomous data classification | |||
| Pipelines that resolve their own failures | |||
| Compounding intelligence across customers (k≥3 anonymity) | |||
| Cost ceiling via outcome pricing |
See it before you buy it.
We will run a live demo against your actual data — Snowflake export, S3 dump, raw CSVs. You will see autonomous classification, agents executing approved actions, and the cost compare in under 30 minutes.