AI Driven Implementations

AI-Driven Implementations

ARPIA offers three ways to apply AI to your data. All three run inside the platform's access controls, and you can combine them in a single solution.

If you need to…	Use	What it does
Ask questions about your data in natural language	AI Agent	Interprets the question, queries authorized data, and returns an answer, table, or visualization
Get answers grounded in documents and knowledge sources	RAG AI	Retrieves relevant content at query time and generates a response from it
Run a repeatable process on your data	AI Worker	Executes a defined workflow and produces an output

Self-service analytics: business users query governed data without SQL (AI Agent).
Policy and procedure lookup: answers that cite internal documentation (RAG AI).
Compliance checks and anomaly detection: scheduled or triggered workflows that flag exceptions (AI Worker).

The implementations use ARPIA's internally designed models and third-party models through the multi-provider LLM gateway. See Model Catalog.

Role-based access. An implementation can only reach data the user's Security Profile allows.
Read-only by default. Write access requires explicit configuration. [VERIFY for AI Workers, which produce "operational updates"]
Scoped by workflow. Each implementation can only do what its configured workflow defines.
Configurable human review. You decide where a person approves or reviews output.
Activity logging. See Logs & Usage. [VERIFY: what is logged]
Change management. Changes to AI features follow a structured lifecycle and change process.

For policies, risk, and monitoring, see AI Governance.

Output quality depends on the underlying data and how the workflow is configured.
Review AI-generated output before you use it for high-stakes decisions.

Updated 4 days ago

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