An agent is a language model plus a harness. We bring the harness.
Getting a good model is a procurement decision now. Everything around it is the work: a machine-readable model of your business, the policy on each field, the connections into systems of record, and the trail that proves what happened.
NuStudio is that harness, already built. Your engineers and ours point it at the systems you already run, model the first entity, and deploy agents against it.
Your systems of record on the ground, the work your business runs and the trail behind it on top, and the layer we bring in between.
We bring the harness and the engineers. What they build into your business is the part nobody can copy.
The harness is additive. It does not want to be your data platform, your warehouse, your catalog or your system of record, and it does not ask you to migrate anything into it.
Databricks, Snowflake, BigQuery, Synapse, read in place through federated query including their own semantic layers. 16+ databases federated through Trino, with Spark engines provisioned per workload and torn down after.
One metadata API over schema, lineage, ownership, classification and quality across 8 catalog backends including Atlas, Collibra, Alation, Glue, OpenMetadata, Iceberg and Polaris. No migration to a single catalog product. Lineage integrates with Unity Catalog, Alation and OpenLineage.
Target systems such as SAP, Epic, ServiceNow, Salesforce, Symitar and Maximo, reached through their own APIs and extended by our engineers to whichever ones you run. Connectors for the rest, plus crawl and onboarding for sources that do not have one yet.
11+ providers including OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, Mistral and Llama, routed per call from a catalog you approve. Or a model trained on your own data.
Airflow, Prefect and Dagster behind one Workflows API, with a unified view across whichever of them your teams already run.
Kubernetes-native in your region, your cloud, your keys. Our cloud, your private cloud, or on-premises in a cluster you run. Nothing phones home.
Anything not on this list joins through the connector framework: any database, warehouse or service becomes a federated source through configuration, with no fork of the platform and no copy of the data. Sources with no API at all are crawled and onboarded the same way.
Each layer is a point where most platforms require a commitment to their own technology. Here every layer is an independently deployable containerized service on the same API fabric, REST and GraphQL, with one authentication model and one response envelope. Adopt the parts you need and keep the data platform you already run underneath.
Paths are the live API domains from the NuStudio API reference. Items marked roadmap are published there as planned modules. Where a client already runs an equivalent of a layer, NuStudio adopts it rather than replacing it.
The agent is handed documents and a prompt. It finds a number, cannot tell which of four definitions produced it, and has no way to know the record is on a hold. It answers confidently and a person checks the answer by hand, which is where the cost sits.
The agent is handed an entity: its identity, its relationships, the definition behind each metric, and the policy attached to each field. It acts inside the limits, writes back to the system of record, and leaves a trail that names the source of every input.
Guardrails ship today: policy and content scanning, guardrail rules and trust assessment on every AI call, with every run traced and audited. The symbolic layer above them, where obligations are expressed in formal logic and evaluated at the reasoning level, is a roadmap module.
Language models read unstructured input, interpret intent, and propose the next action. They are routed from your approved-model catalog, frontier or trained on your own data.
Guardrail rules are defined once and enforced on every call, with the result recorded. Expressing those obligations as formal symbolic policy, evaluated at the reasoning level, is the roadmap module.
Multi-hop reasoning across the knowledge graph gives the agent the relationships behind an entity, so it can answer questions that span systems instead of retrieving documents that happen to mention them.
Checks run twice: before the language model is given data, and again before anything is written back to a system of record. Where the risk calls for it, a named human approves in between.