All of your AI. One governed system.

AI² gives every model a boundary and a job, every output an owner, and every contribution an audit trail. Rapid Deploy delivers the platform in days.

Fast-track value through Rapid Deploy.

Time to deploy

Months to Weeks

Time to deploy
Months to Weeks
Architecture

AI sprawl to
Governed System

Architecture

AI sprawl to Governed System
What changes

Personal Productivity to Production Execution

What changes

Personal Productivity to Production Execution

400+ enterprises trust the engine.

Built for the multi-model reality: platform AI plus the assistants your teams already use.

Every model gets a lane.

In-workflow AI that answers from your environment.

Platform AI handles in-context work inside Jira and Confluence: retrieval, summarization, ticket-level assistance. Trundl configures the agents and strengthens the knowledge they answer from.

WHAT THE CONFIGURATION COVERS

Your approved assistants work inside the system of record.

Approved assistants, Claude, ChatGPT, or Copilot, connect through governed access layers like the Atlassian MCP server and take the cross-context reasoning and transformation work.

WHAT THE CONFIGURATION COVERS

A platform agents can act on safely.

Rapid Deploy is the enforcement mechanism. Workflows, routing, and permissions hold before any agent acts.

A platform agents can act on safely.

Rapid Deploy is the enforcement mechanism. Workflows, routing, and permissions hold before any agent acts.

WHAT THE CONFIGURATION COVERS

Governed for the audits you already answer to.

Industry-tuned patterns for regulated engineering organizations.

Healthcare

PHI is masked before any model sees it. Sign-off stays human at regulated decision points. Audits reconstruct input, output, and decision.

Manufacturing

AI-assisted documentation carries full attribution in Jira and Confluence, so ASPICE and ISO 26262 reviews pull evidence from the system of record.

Healthcare Technology Company

AI² turns escalation from a coordination project into a workflow.

A documented Healthcare Technology reference pattern: an owner and an AI role at every stage.

1

workflow

5

use case patterns in the starting scope

2

ready to compose AI layers

7 to 18 days

one audit trail

Every stage

to a working configuration

Day 0

Escalation lands. AI structures the report, environment, and history into one record. Support owns the submission.

Hour 1

AI drafts the package with reproduction steps. Support reviews before it moves.

Hour 2

AI suggests the routing. Support approves. The decision is logged for audit.

Day 1+

Engineering receives a standardized package, AI contributions logged per stage. Engineering owns the diagnosis.

Trundl’s job isn’t to follow a platform roadmap. It’s to arrive ahead of it, operationalize what’s possible, and deliver outcomes where it counts: in the workflows that drive how work gets done.

Manohar Goli

CTO, Trundl

Twelve hours.
A scoped AI² engagement.

Bring one workflow where AI is already in use. Trundl returns the assessment, the configuration, and the timeline.

Format

Remote, on-site, or hybrid

Duration

12 hours, one or two days

Participants

Trundl architects + your platform owner, security lead, and one workflow owner

You Receive

Current-state assessment, scoped configuration, timeline

Send the list of AI tools your teams use today. Rapid Deploy comes ready to build.

Latest from Trundl.

The Case for Contextual Delivery

The definitive guide to Accelerated Contextual Delivery. Eight pages. Built for CIOs, CTOs, and VPs of platform strategy.

AI² in healthcare technology
How a healthcare technology team moved escalation from individual prompts to one governed workflow, with audit trails on every AI contribution.
Scoping an AI² engagement
What the first twelve hours cover: the workflow, the models, the boundaries, and the plan to build from.

Common questions.

What is AI²?
AI² is the system Trundl configures so multiple AI models do governed work across your existing workflows. The capability comes from the models you choose. The execution layer Trundl builds around them defines what each model sees, what context each output gets, and what the audit trail records.
Customers do not converge on one AI platform. Most run Atlassian Rovo for the work that happens in Jira and Confluence, plus an external model for the analysis that needs to read across multiple systems. AI² is built for whichever combination you are actually running.
You set the policy. Trundl configures the system to that policy. The audit log records every model output, and rollback is part of the build.
You can run AI² with whatever AI you already have. If Rovo is the right in-context model for your Atlassian estate, Trundl uses it. If Copilot is the right fit for your Microsoft estate, Trundl uses that. The configuration is built around the AI you bring.
Copilot Enablement is a deployment of one product. AI² is a configuration above one or more products. If you are deploying Copilot for the first time, that is the Microsoft Copilot Enablement engagement. If you have Copilot, Rovo, and an external model and want them governed together, that is AI².
If you have committed to one AI vendor and will not consider others, AI² is more than you need. If you are running individual AI experiments and have not defined workflows yet, an enablement engagement is the better starting point. If governance and audit are not priorities yet, AI² will deliver more than you need.