Build Agents Without Writing a Line of Code
Turn a conversation, a voice command, or a visual workflow into an agent that runs complex work from start to finish.
Automation That Doesn’t Wait on Engineering
Build Without a Development Backlog
Teams create and launch agents themselves through conversation or a visual builder, removing the wait for technical resources.
Change With Confidence, Not Caution
Every update is versioned automatically, so teams can test and refine agents without risking the workflows already running in production.
One Agent Reaches the Whole Tech Stack
Agents read, write, and act across connected applications from a single build, reducing the need for separate point solutions per system.
Build Autonomous AI Agents Without Code
Agents can be created through natural conversation, voice, or a visual builder, then set to automate complex, multi-step workflows from start to finish without writing code.
Test and Recover Without Disrupting Live Work
Every change is versioned automatically, so teams can experiment, refine, and roll back instantly, without touching workflows already running for the business.
Put Every Connected App to Work
Agents read, write, and act across all connected applications from a single build, so one agent can complete a task that would otherwise span several tools. Ready-to-use connectors integrate seamlessly with your leading enterprise platforms, no custom builds required
1. Incremental Crawling
Fetches only newly added or updated data, cutting processing time while keeping information continuously current.
2. Real-Time Data Access
Instantly accesses your latest enterprise data, powering faster, better-informed AI-driven decisions across the organization.
3. Permission Replication
Maintains your existing user roles and access controls, so AI always respects the same permissions.
4. Faster Deployment
Eliminates complex custom integrations with pre-built connectors, getting you to production significantly faster.
Automate on a Foundation of Governed Knowledge.
Agents ground their actions in live company knowledge and operate within built-in governance controls, applying AI deliberately rather than by default.
1. Right LLM for Every Task
The most suitable AI model is selected for each use case, even small tasks, to balance performance, cost, and accuracy.
2. AI Applied Only Where It Adds Value
Every process is evaluated at the task level to decide whether AI is needed and exactly where it should be applied.
Match AI Output to the Task at Hand
Response style can be tuned from precise and factual to creative and exploratory, so an agent’s output fits the goal and audience it’s built for, whether that’s a compliance summary or a first draft.
