AI Providers
AI providers are authenticated connections to external model services. Each provider gives your Elementum environment access to a different set of language models and capabilities.OpenAI
GPT and reasoning models for language processing, classification, and generation
Snowflake Cortex
AI that runs natively in your Snowflake environment with LLM functions, embeddings, and search
Google Gemini
Multimodal models through Vertex AI for text, image, and document processing
AI Models
Compare supported models, capabilities, and provider-specific features
You need at least one configured AI provider before you can create AI services or use AI capabilities in your workflows. You can configure multiple providers simultaneously to use different models for different tasks, implement failover strategies, or optimize costs by routing to the most efficient provider. See AI Models for a detailed breakdown of available models across providers.
AI Services
AI services sit between your providers and your workflows. They define which model to use, how it behaves, and where it can be applied. There are two types:- LLM services — Configure a language model for use in automation actions, agents, and other AI-driven tasks. Each service specifies the provider, model, and default parameters.
- Embedding services — Configure an embedding model for AI Search, which converts your data into vector representations for semantic querying.
AI Services
Create and manage LLM and embedding services for your environment
AI Search
Enable semantic search across Elements and Tables using embedding services
AI in Automations
AI actions are available as steps within Elementum’s automation system. They operate on record data and return structured results that your automation can act on.- Classification — Categorize records by analyzing field content and assigning labels with confidence scores
- Summarization — Generate concise summaries from long-form text fields, comments, or attached documents
- Data transformation — Restructure, normalize, or enrich field values using language model processing
- File analysis — Extract structured data from PDFs, invoices, contracts, and other uploaded documents
AI in Automations
Learn how to configure AI-driven automation actions
Automation System
Understand the triggers, conditions, and actions that power Elementum automations
AI Agents
Agents are conversational AI components that operate within your apps. Unlike one-shot AI actions in automations, agents maintain context across a conversation, use tools to read and write data, and follow policies you define.- Tools and data access — Agents can query Elements, update records, trigger automations, and call external APIs
- Skills — Modular, reusable capabilities that agents discover and execute at runtime
- Starting actions — Pre-configured action chips that guide users toward common tasks without typing
- Multi-channel deployment — Run agents in your Elementum app, Microsoft Teams, Slack, or over the phone via Twilio
- Multi-agent coordination — Agents communicate using the Agent-to-Agent (A2A) protocol to hand off tasks and share context across workflows
Building Agents
Create, configure, and deploy agents in your apps
Agent Architecture
How agents process requests, use tools, and integrate with workflows
Agent Skills
Define reusable skill modules that agents discover and execute
Agent Orchestration Center
Monitor and manage agents across your organization
Agent Integrations
Agents can operate beyond the Elementum interface. Connect them to the communication channels your teams already use, or trigger agent conversations directly from automation workflows.Microsoft Teams
Run agents as bots in Teams conversations
Slack
Deploy agents in Slack channels for team collaboration
Phone (Twilio)
Enable voice conversations with agents over the phone
Agent Tasks in Automations
Trigger agent interactions from automation workflows
Data Cloud Architecture
AI processing in Elementum runs within your data cloud environment. This architecture has several practical implications:- No data movement — AI actions query and process data where it already lives. Records stay in your data warehouse; only prompts and results move between systems.
- Inherited security — AI capabilities respect your existing access controls, role-based permissions, and audit logging. No separate security layer is required.
- Real-time data — AI operates on current record states, not cached snapshots. When an agent queries an Element, it reads the latest data.
Getting Started
Setting up AI in Elementum follows a consistent sequence. Complete each layer before moving to the next.- Connect a provider — Configure at least one AI provider (OpenAI, Snowflake Cortex, or Google Gemini) with your API credentials.
- Create AI services — Set up LLM services for the models you want to use in automations and agents. If you plan to use semantic search, create an embedding service as well.
- Enable AI search (optional) — Configure AI Search on Elements and Tables where you want natural language querying.
- Add AI actions to automations — Use AI automation actions to classify, summarize, transform, or analyze data within your existing workflows.
- Build and deploy agents — Create agents with tools, skills, and policies tailored to your business processes. Deploy them in-app or across Teams, Slack, or phone.