The agentic OS that retires the warehouse era.
Snowflake is a warehouse for the 10% of your company that writes SQL. Databricks is a notebook for the data-science minority. Eisberg is the agentic OS the other 90% configures and runs on — Treasury, Underwriting, FP&A, Demand Gen, Clinical Ops, Talent Acquisition, Loan Servicing, S&OP, Quants, Pharmacovigilance, Field Service, Internal Audit, and every other function in every industry. Same primitives, same governed substrate, same agent-governance plane, in your own bucket. GPU-native compute (8-12x cost advantage). Agent-native architecture (sub-100ms API, per-action metering, signed Birth Certificates). Customer-owned data in open Iceberg format. We don't build a cheaper warehouse — we make the warehouse model obsolete. The starter packs below are reference examples teams fork. Not the product. The OS is the product.
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.
What incumbents cannot retrofit.
Each pillar above maps to a structural advantage that Snowflake, Databricks, and ClickHouse cannot match without rewriting themselves. Designed in from line one, compounding from day one.
Enterprise knowledge layer
Tribal rules captured automatically and reasoned against on every query. The longer you run it, the deeper the moat — every rule a competitor would have to extract from a human conversation.
Automated business ontology
Two weeks of letting the platform watch your workload replaces six months of consultant data modeling. Ontology learned from one customer informs heuristics for the next.
Agentic governance
Policy as code that agents read, enforce, and explain. Risk-graded approval gates with tamper-evident audit trails. Agent Birth Certificates define identity, scope, ceiling, and expiry at creation — not after the incident.
Autonomous operations
Pipelines that resolve their own failures. Compute that self-routes to the cheapest engine that can answer the question. Anomalies that triage and remediate themselves. The platform stops being something you operate.
Composable open architecture
Open table format in your bucket, behind your KMS keys, in your cloud. Every engine replaceable, every byte portable. Every layer abstracted so the next hardware shift slots in as a new backend, not a rewrite.
Compounding intelligence
The platform learns from every query, classification, and successful agent action across every customer who opts in — under a k≥3 anonymity gate. The longer it runs, the smarter it gets. The more customers, the smarter each one's becomes.
Things that are true about Eisberg by construction.
Not roadmap items, not marketing claims — architectural decisions that define what the platform is and what an incumbent would have to abandon to compete.
We will never store your data.
Customer-owned data plane is structural, not contractual. Eisberg infrastructure is incapable of holding your data — it lives in your bucket, behind your KMS, in your cloud. Most incumbents cannot match this without abandoning their revenue model.
We bill outcomes, not warehouse-seconds.
Snowflake's pricing assumes you'll forget to suspend warehouses. Ours assumes the opposite — pause-to-zero by default, GPU-priced compute, outcome-based billing on the intelligence layer. Every dollar on your invoice ties to a measurable outcome.
We were designed for agents from line one.
MCP-protocol native, per-action metering, identity-bound agents, signed Birth Certificates. Not features bolted onto a human-first warehouse. Architecture designed for the world where agents are the primary users.
Same manifest, every cloud.
One Helm chart deploys on AWS, Azure, GCP, and neo-clouds. Switching clouds is a configuration change, not a rewrite. Snowflake accounts and Databricks workspaces are per-cloud — we are not.
From first byte to first dollar saved — in days, not quarters.
The platform starts producing useful work within hours of the first connection. Every stage produces evidence you can verify.
Connect any source.
Snowflake, Databricks, Postgres, Salesforce, S3, raw CSVs, sensor streams. The platform meets your data where it lives.
Land it in Iceberg.
Open format, your bucket, your KMS keys. The platform never owns your data — that is the entire premise.
Auto-classify, auto-govern.
Every column gets a classification, every PII field gets a mask, every action gets a policy. No spreadsheet, no committee.
The intelligence wakes up.
Autonomous discovery surface joins, anomalies, business definitions. Agents propose actions you can approve in one click.
Outcomes you can audit.
Every saved dollar, every resolved anomaly, every prevented violation lives in a replayable evidence trail.
What is structurally true about Eisberg.
Components are deliberately not enumerated — implementation choices stay private until NDA. What is public is the property each layer guarantees. The full architecture deck is available to design partners and serious prospects on the second call.
| Layer | Property |
|---|---|
| Storage | Apache Iceberg in customer-owned object storage. Open format, customer KMS keys, structurally portable to any compliant engine. |
| Catalog | Open-standard catalog with namespace branching and time travel by reference. No proprietary dialect to defend. |
| Query path | Four engines selected per query for cost and latency: embedded for sub-second agent calls, distributed for medium analytical, GPU-native for heavy aggregation, MPP-on-Iceberg for real-time at scale. |
| Streaming | Sensor-scale ingestion + change-data-capture from operational stores. Auto-classification on arrival. Schema drift handled without alerts. |
| Orchestration | Cloud-agnostic Kubernetes manifests across hyperscalers and neo-clouds. Identical configuration everywhere. |
| Workflow | Durable execution model — survives infrastructure failures and resumes from the last committed step. Required for trustable agent autonomy. |
| Policy | One policy plane every component reads from. Row-level, column-level, action-level, agent-level — all expressed as code. |
| Lineage | Cryptographically chained, open-standard lineage emission at every layer. Cross-system chain of custody, replayable on demand from the bill alone. |
| AI router | Provider-agnostic LLM dispatch. Any model, routed to the cheapest capable. No hardcoded vendor lock-in. |
| Caching | Predictive cache aware of your data layout, with measured cache-hit accounting that feeds the router. |
Eisberg, by capability.
Intelligence OS
Autonomous discovery, agentic actions, automatic classification, lineage-aware briefings. The platform doesn't wait to be asked.
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Governance & policy
Policy as code at every layer. Dynamic masking by access context. Compliance modules for BCBS 239, SR 11-7, HIPAA, 21 CFR Part 11.
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Agent OS
MCP-native. Per-action metering. Identity-bound agents with risk-graded approval gates and Birth Certificates the regulator can replay.
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Composable engines
Embedded, distributed, GPU-native, MPP-on-Iceberg — picked per query for cost and latency. One router, four lanes.
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Streaming & CDC
Sensor-scale telemetry and change-data-capture from your operational stores, all landing in open-format tables in real time.
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Migrations
Snowflake, Databricks, Teradata, Oracle, SQL Server. Bring your DDL, your code, and your data. Leave the lock-in behind.
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The honest head-to-head.
Every row is something we will demonstrate end-to-end on your data in a 30-minute call.
| 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.