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AI for data and analytics

Spend AI on the question, the forecast and the interpretation, not on every data row.

Operational data needs a dependable path from its source to the people using it. Nexus combines that deterministic data path with a focused set of model-assisted capabilities: questions over approved datasets, load forecasts for planner review and investigation of data-quality exceptions. Snowflake and connected source systems retain their records, while the workflow connects each model task to its input scope and the responsible decision owner.

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Use cases in data and analytics
7Use cases in data and analytics
Places a model does the work
3Places a model does the work
Systems they run across
9Systems they run across
Your model, your infrastructure
Self-hostedYour model, your infrastructure

Where the model earns its place

What AI does here

Each of these is a point where the input varies enough that a rule cannot settle it, and where the output is something a person can check.

  1. Turns questions into analytical work

    A model translates the business question into a proposed read-only query against approved views and metric definitions. The workflow checks and executes the query, then the model explains the returned result with its filters and scope.

  2. Supports load planning

    A selected predictive model produces a forecast from the approved input snapshot, and model-assisted explanation connects revisions to the available drivers. The planner releases the chosen version with any override reason recorded.

  3. Prepares data-quality investigations

    A model examines records that need interpretation beyond established checks and proposes a field-level explanation or correction for the data owner. Source references and the comparison context accompany the proposal.

Human control

Where a person decides

The named data owner approves new data scopes, metric definitions and authoritative corrections, while the planner approves the released forecast. Financial, access and operational actions follow the approval policy of their respective owner before a separate system step applies them. The run connects the original query or proposal, the reviewed decision and the resulting destination action.

Cost

Deterministic or AI

Extraction, established transformations, key checks and total reconciliation should run as deterministic data operations, without a model charge on every row. Reserve model capacity for translating a question, producing a forecast or interpreting a quality exception. Nexus makes that choice per step, so growing datasets do not automatically expand inference across the whole pipeline. The deterministic use cases below provide the data foundation for the specific model-assisted tasks above.

How governed AI automation worksRules first, model proposal second, policy check, human review, captured decision.

7 use cases

Data and analytics use cases

Explore operational data pipelines together with model-assisted questions, load forecasting and data-quality review, each with its source scope and decision points.

Filter every use case by industry and theme

3 customer stories

Customer stories

Explore customer stories in energy load forecasting, logistics fleet analytics and manufacturing production forecasting.

Talk to a solutions engineer

See this run against your own data and analytics systems

Bring one process you would want to put a model in front of. We will map which steps stay deterministic, where the model is worth the call, who approves what, and what the run record has to show afterwards.

  • Runs on your infrastructure, including air-gapped.
  • Your model provider, or one you host yourself.
  • No system of record is replaced.

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Goes to a solutions engineer who will run the session against the capability you select, in a shared environment.

FAQ

AI for data and analytics: common questions

Where does model spending add value to these data workflows?
At the interpretation points: translating a business question, generating a selected predictive estimate or investigating an exception. Established data movement and validation can remain deterministic.
How does a business question become a Snowflake answer?
The model proposes a query, the workflow checks its read-only scope and executes it with permitted credentials, and the model explains the result with the query context available for inspection.
Who releases a forecast or approves a source correction?
The named planner releases the forecast, and the authorized data owner approves the correction; any posted-document change also follows the responsible finance policy.
Can we choose the model appropriate to each analytical task?
Yes. The workflow can connect language interpretation and selected predictive models to the same governed process, using external providers or locally hosted models as configured.

Architecture session

Work out where AI belongs in your data and analytics processes

We will go through the steps you run today and say which ones a rule should keep, which ones a model improves, and what each one costs to operate.

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