Data analytics platform for trusted AI answers and confident actions

A business logic layer standardizes operational knowledge and understanding, and automates how it gets executed so that it is consistent and repeatable. It uses visual workflows to capture the decisioning rules, calculations, variances, exceptions, and validations, and executes them consistently to LLMs, AI agents, BI/analytics, and other applications. Operationalizing business logic as a consistent enterprise layer transforms AI outputs from being probabilistic into deterministic— making answers and actions visible, understandable, repeatable, and auditable (VURA), so that organizations can attain a governed, trusted foundation for enterprise AI.

Semantic layers standardize how metrics are defined and calculated, while ontology layers standardize definitions for terms and users, and how they relate to one another. These combine into knowledge graphs that are queried by AI agents, LLMs, and analytics, so that they gain understanding that they can apply to probabilistic scenarios.

However, these layers stop short of being able to help AI to determine the correct action to execute based upon dynamic operational knowledge— for example, executing a financial close. This is exactly what a business logic layer does. Working side by side, business logic and semantic/ontology layers provide AI with complete contextual understanding and actionability of an organization’s data, knowledge, and operations.

• For example, in AI-assisted loan application processing at a bank:
  The semantic layer defines what ‘income’ or ‘credit score’ means for the applicant.
• The ontology layer determines that the applicant has multiple accounts or is linked to a joint account with another customer.
• The business logic layer executes the debt-to-income ratio calculation of the applicant and executes the bank’s exception rules to ensure that the proper approvals are in place.

Alteryx’s business logic layer sits between the data and semantic layers below it and the application or reasoning layer above it:

• Below: the data and semantic layers, which hold the governed data and the metric definitions.
• Middle: the Alteryx business logic layer, where analytic workflows pull governed data from the data layer and apply operational logic to it.
• Above: the application or reasoning layer— LLMs, AI agents, BI/analytics tools, and apps. These are the interfaces end users work with, surfacing the actions and insights that the workflows produce.

The logic layer goes beyond simple process automation. Where process automation moves a transaction from one step to the next, a business logic layer also decides whether that step is the right one to take— i.e. the correct reconciliation, calculation, or exception. Because the logic runs the same way every time and in every system that uses it, any LLM or agent in the enterprise can call it, and it will return the same answer each time it’s called.

The business logic layer is owned by the people who are closest to understanding day-to-day operations— your analysts and operations teams. There are two reasons the business should own the logic:

1. Only the people who work with the logic, such as reconciliation rules, can document, execute,  audit, and update  them accurately— IT can’t be responsible for rules it doesn’t understand.
2. This logic changes often, so a layer maintained only by a central team like IT tends to fall behind and return answers based on outdated information.
   As an integral layer of the data and AI stack, the business logic layer requires close partnership with IT, so that the business owns the logic, and IT owns the data platform and its controls. We call this approach governed autonomy— and with over twenty-five years of Alteryx adoption at 8,000+ organizations running 380M+ analytic workflows in production, it proves that it works.

Analysts and operations teams build self-service analytic workflows and agentic experiences using no-code interfaces, ranging from visual designers to conversational agents. These workflows translate the organization’s internal rules and policies into executable logic, which speeds up operations and insights.

Teams can then deliver those workflows to where enterprise users already work, such as Claude, Slack, Tableau, and custom agents and apps, which makes governed, repeatable AI automation available across the organization.

Alteryx connects to data in your enterprise data platform, including Google BigQuery, Snowflake, and Databricks, and business teams build against that data through visual workflows and agentic building experiences. This keeps business logic close to the data it uses.

Alteryx uses in-place analytics to push computation down to your cloud warehouse, where the data already lives, and it orchestrates the process without storing or moving the data. This approach has three benefits:

• Governance is locked in at the source: Access controls (RBAC) defined in your warehouse apply automatically; data never crosses secure boundaries; permissions travel with the query, not with a downloaded CSV file.
• Compute scales seamlessly with your warehouse: Analysts can work with billions of rows using the same drag-and-drop interface, but the heavy lifting is executed in your elastic cloud infrastructure.
• Costs drop when data stops moving: Egress fees vanish when queries run in the warehouse instead of pulling data out, so that you pay for storage once, not twice.

LLMs often produce different answers to the same question, with the model writing code and repeatedly querying datasets as it tries to guess the accurate answer, churning through tokens along the way. With Alteryx, the workflow executes the underlying joins, calculations, and aggregations first, so that the LLM receives only clean, structured factual summaries. Instead of guessing at business logic, the model simply synthesizes pre-validated insights—cutting execution time, using a fraction of the tokens, and achieving higher accuracy.

With Alteryx, a workflow runs the joins, calculations, and aggregations first, so the LLM receives only clean, structured summaries of the facts. Instead of inferring business logic, the model synthesizes results that have already been validated, which reduces execution time and token use and improves accuracy.

A 2026 benchmark study compared an LLM working alone, which is non-deterministic, with an LLM plus MCP, a deterministic hybrid in which an Alteryx workflow handled the data matching, filtering, and reconciliation logic and sent only the pre-processed summary breaks to the LLM.

Compared with the LLM working alone, the deterministic hybrid resulted in:

• Lower token costs: reduced by up to 95%.
• Faster LLM execution speed: improved by up to 85%.
• Higher output accuracy: from 81.8% to 100%.

Alteryx workflows allow experts with deep knowledge about how the business operates to define their rules, policies and operations in a visually explainable interface. Once published, users who may not understand the logic or strategy behind those rules, policies and operations can run the workflows as often as they need to and get trusted factual answers. These answers can then be used alongside AI applications which can explain these answers in plain english to business users from any part of the organization. As a result: demands upon IT and data teams to constantly run queries to support the business are alleviated; AI token spend decreases; and accuracy increases so that operations run effectively.

Start with a proof of concept (POC) built around a real business use case you want to tackle with AI, such as agentic month-end close reconciliation or agentic variance analysis. Alteryx will conduct a side-by-side comparison of the same LLM running on its own and working with Alteryx workflows and trusted business logic.

By the end of the POC, you will see how Alteryx can:
Improve AI accuracy, with answers grounded in your data and business rules.

• Get results faster, because governed workflows handle the complex data processing and calculations.
• Use fewer tokens and reduce AI consumption costs.
• Prove business value through measurable efficiency gains and time savings.