One AI-ready data layer across every system in your estate.

One control plane makes mainframe, warehouse, lake, cloud, and SaaS behave as a single governed surface. Agents get full context. Sensitive data never leaves your perimeter. No migration.

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Your AI program is stuck on the data layer, not the model.

Models only reason from the context they're given, and that context is scattered across mainframe, Teradata, Cloudera, three clouds, and your Snowflake or Databricks footprint.

Platforms specced before AI bolt on vector stores, agent frameworks, and model servers, each with its own identity, ACL, and audit trail.

Even with unified data, every request runs to the most expensive model, so token cost climbs without better results.

There’s a better way.

A data layer that already covers every system you own, governs them the same way, and lets agents reason across them without sensitive data leaving your perimeter.

AI agents and LLMs need an AI-native data solution

01 · One context plane across the entire data estate

Mainframe, Teradata, Cloudera, Snowflake, Databricks, S3, SaaS, and the lakehouse you just stood up, federated under one catalog, one identity, one policy model. Agents reason across the whole estate, not one warehouse pretending to be the company.

02 · One sovereign estate for regulators

Inference, embeddings, vector indexes, retrieval, agent state, traces, and evaluation data run inside the boundary you own, with no outbound path when the workload requires it. Classified, OT, and DR environments included. Run open-weight small models on your own estate, and call a frontier model only when the work demands it. You don't want to outsource intelligence.

03 · One governance and access definition enforced everywhere

Row, column, tag, and policy rules apply to a human analyst and an AI agent the same way, from one identity backbone. Agents inherit the permissions of the user they act for. Every query, prompt, retrieval, and tool call lands in the audit log your team already trusts.

Meet the first AI & Data Control Plane

One governed boundary over every model, engine, user, and agent. It reads what each request is for and who's asking, routes it to the right place, enforces policy at the edge, and records all of it. Routing, governance, and cost in one console, across cloud and on-prem.

Route on intent. Deterministic queries hit a query engine. Predictions go to your existing ML models. Narrow tasks go to small models. Repeats return from cache for zero tokens. Frontier models run only for genuinely generative work.

Govern at the boundary. Jailbreak and PII signals stop unsafe requests before they reach a model or your data, logged with the reason and the user's identity.

Price every token. Per-role budgets and per-request ceilings cap spend. Routing chases the cheapest model that can do the job, across your infrastructure and hosted providers. Reroute on quality — degrading models are rated, penalized, and routed around automatically.

See everything. Spend, cloud-token share, latency per model, consumption per user, and every security event, on one complete record.

Learn more and watch a demo →

Meet the first AI & Data Control Plane

Govern at the boundary. Jailbreak and PII signals stop unsafe requests before they reach a model or your data, logged with the reason and the user's identity.

See everything. Spend, cloud-token share, latency per model, consumption per user, and every security event, on one complete record.

Learn more and watch a demo →

Meet the first AI & Data Control Plane

Govern at the boundary. Jailbreak and PII signals stop unsafe requests before they reach a model or your data, logged with the reason and the user's identity.

See everything. Spend, cloud-token share, latency per model, consumption per user, and every security event, on one complete record.

Learn more and watch a demo →

Try routing an AI Query yourself

See how every request is routed, governed, priced, and logged by NexusOne.

Move from a stack of disconnected AI bolt-ons to one AI-ready data layer in weeks.

NexusOne doesn't replace your core, your warehouse, or your legacy Hadoop. It lays over them.

Your data estate today

Vector stores, agent frameworks, and model servers bolted onto whichever cloud each team picked first.

Every new AI workload comes with its own identity model, its own ACL, and its own audit trail.

Agents reason only against the one room they were pointed at; mainframe and Teradata stay invisible

Sensitive data leaves your perimeter the moment a hosted model is called

Prompts and retrieved documents land in vendor logs you don't run and can't easily audit.

Regulators find data drift before your reporting team does; POCs sit in pilot purgatory

Every AI request goes to the most expensive model by default

With NexusOne

One layer federates every source behind one catalog, one identity, one policy model.

Hadoop, Spark, and warehouse workloads inherited as-is, no rewrites

One semantic surface across the estate, with lineage and audit regulators can inspect

Credit, claims, and PII never leave your VPC or region

Every call masked, policy-checked, and logged in an audit trail you run

Automated lineage testing catches data-quality failures before they reach a report or model

Each request routed on intent, capped by budget, blocked on policy at the edge, logged in full

Power AI applications and use cases with NexusOne

Platform + People + Automation

Superior data technology

A composable, open data layer that lays over your existing stack. One catalog and identity model across the estate. Iceberg-native storage. Sovereign agent and LLM runtime. Kubernetes-native, on-prem, cloud, or hybrid. Same architecture in every site, including air-gapped.

→ Explore the platform

Forward-deployed engineers

Forward-deployed engineers who have stood this up inside Tier-1 banks, telcos, and credit bureaus. They sit with your teams, deploy in your environment, and deliver production outcomes.

→ Meet your new FDEs

AI/ML-enabled operations

HDFS-to-Iceberg in one click. Kafka-free CDC. Schema translation. Policy generation across systems that never spoke before. Dynamic agent governance and circuit breakers. Months of plumbing, done in minutes.

→ See the automations

Built for the work your team has to ship next quarter, not next decade

Run sovereign agents and LLMs

Inference, embeddings, retrieval, and agent state stay inside your boundary. Your data never trains someone else's model.

Run sovereign agents and LLMs

Inference, embeddings, retrieval, and agent state stay inside your boundary. Your data never trains someone else's model.

Ground RAG in the whole estate

Agents retrieve from a federated catalog with row, column, and tag policies already applied. No copying into another silo.

Ground RAG in the whole estate

Agents retrieve from a federated catalog with row, column, and tag policies already applied. No copying into another silo.

Govern agents and operations

Per-agent attribution, per-role budgets, circuit breakers, central registry, lineage on every write.

Govern agents and operations

Per-agent attribution, per-role budgets, circuit breakers, central registry, lineage on every write.

Route intelligently and control token economics

Right compute for every request; AI cost becomes a predictable, attributable line item. (full detail in the control-plane section)

Route intelligently and control token economics

Right compute for every request; AI cost becomes a predictable, attributable line item. (full detail in the control-plane section)

Regulate production AI

Credit, claims, fraud, AML, clinical, and trading agents with MRM-ready lineage and human-review checkpoints.

Regulate production AI

Credit, claims, fraud, AML, clinical, and trading agents with MRM-ready lineage and human-review checkpoints.

Provide cross-estate context in one semantic layer

One semantic model across mainframe, warehouse, lake, and SaaS. Agents see the business, not the schema.

Provide cross-estate context in one semantic layer

One semantic model across mainframe, warehouse, lake, and SaaS. Agents see the business, not the schema.

Use natural language on your data

Conversational and embedded analytics over the full estate, governed by the same identity model as your humans.

Use natural language on your data

Conversational and embedded analytics over the full estate, governed by the same identity model as your humans.

Achieve an AI-ready data layer in weeks, not years

5 hours

Deploy NexusOne. Launch the control plane in your VPC or air-gapped site. Federate existing identities. Import discovered policies.

5 days

Connect mainframe, warehouse, Hadoop, cloud, and SaaS into one catalog. Lineage live. First governed data products and embeddings shipping.

5 weeks

Production AI. Semantic layer, agent-ready endpoints, retrieval pipelines, MRM scaffolding, and human review running against your priority use case.

A Tier-1 global bank replaces legacy Hadoop and funds its AI agenda with the savings.

Ten-plus years of credit, risk, and transaction history on a sprawling Hadoop estate. Every modernization proposal on the table demanded a multi-year rip-and-replace. The bank was rushing to stand up its first production AI use cases.

$30M+

Cloudera license costs eliminated

$100M+

Hardware savings via serverless

30 Apps

Modernized for AI in under 4 weeks

0

Records leave governed boundary

The Result

NexusOne laid a composable layer over existing Hadoop and warehouses. Workloads kept running while HDFS migrated to Iceberg, delivering an AI-ready layer to power consumer LLM assistants. The bank is now zeroing out legacy Hadoop license spend, decommissioning obsolete hardware, and running production AI on a sovereign fabric.

Govern every AI agent.

One identity for humans and agents, FinOps and circuit breakers built into the layer.

Keep what's working

Mainframe, Hadoop, Teradata, warehouse, lake, and cloud stay where they are.

Design for open, sovereign data

Data, models, prompts, embeddings, and audit trails stay inside your boundary.

Build in air-gapped, 360 Agent governance

Full context to the model, one identity for humans and agents, FinOps and circuit breakers built into the layer.

Govern every AI agent.

One identity for humans and agents, FinOps and circuit breakers built into the layer.

Keep what's working

Mainframe, Hadoop, Teradata, warehouse, lake, and cloud stay where they are.

Design for open, sovereign data

Data, models, prompts, embeddings, and audit trails stay inside your boundary.

Build in air-gapped, 360 Agent governance

Full context to the model, one identity for humans and agents, FinOps and circuit breakers built into the layer.

See the platform against your estate.

A working session with a NexusOne engineer: we map the systems you run, the gaps between them, and the fastest path to AI-ready — in weeks, not years.

See the platform against your estate.

A working session with a NexusOne engineer: we map the systems you run, the gaps between them, and the fastest path to AI-ready — in weeks, not years.

See the platform against your estate.

A working session with a NexusOne engineer: we map the systems you run, the gaps between them, and the fastest path to AI-ready — in weeks, not years.

FAQs

How is this different from Snowflake Cortex, Databricks Agent Bricks, or any AI built into our warehouse?

Cortex and Agent Bricks are destinations inside one warehouse. NexusOne is the layer across them and across the mainframe, the lake, the cloud warehouse you don't run, and the SaaS sources of record. Our customers run Cortex and Agent Bricks. We're how their agents see the rest of the company at the same time. The estate is converging on open formats. Databricks acquired Tabular, Snowflake open-sourced Polaris, and NexusOne is the control plane across the converged surface.

We already have a vector DB and an agent framework. Why do we need another data layer?

Those are model-side. The data layer is what makes them safe and useful at production scale. Without one control plane across the estate, every retrieval index, agent transcript, and model log is its own perimeter with its own identity. NexusOne gives the same governance to a vector store, a Trino query, a Kafka topic, and a mainframe extract.

Won't this introduce another abstraction layer that will leak?

We're a control plane built on the same open formats your tools already speak. Iceberg, Arrow, Parquet, Trino, Gravitino, Kubernetes. 85+ open-source tools deeply integrated, not a homegrown fork. If you ever leave NexusOne, you take your storage and your data in open formats.

Can we build this ourselves on open source?

You can. Doing so takes 18–24 months, eight senior engineers, and a running tax on every patch and upgrade. Our automation layer removes that tax. You keep the open-standards foundation without inheriting the maintenance.

How does it stay sovereign when our agents call external models?

Local LLMs and embedding models run inside the control layer on hardware you control. When a workload calls for a hosted model, NexusOne enforces the same row, column, and tag policy on what crosses the boundary, masks PII in flight, and lands every call in the audit log. The decision of what may leave is yours, not the agent's.

How does the cost compare to what we run today?

Simple vCore pricing, fully managed, with enterprise discounting on top of rack rate. In most engagements NexusOne is net cost reduction before counting the AI upside, because we let you sunset legacy license and hardware spend that was funding the old stack.

How do you keep AI token costs under control?

A semantic cache answers repeats with no model call. Per-role budgets and per-request ceilings cap spend. Requests route to the lowest-cost model that can do the job, including your own local models. You see spend by user, by model, and by cloud-token share. (Full routing logic in the AI Data Control Plane section above.)