SecurePM
Self-hosted AI for Jira

Your model.
Your network.
Your custom fields.

AI that drafts and enriches Jira work items grounded in your own knowledge, running on your own model, inside your own infrastructure. No third-party LLM. No telemetry. No content ever leaving your control.

Book a 15-minute technical review See the egress proof

Inference egress this product can reach: your model endpoint, and nothing else.

Content path vs. license pathtopology, not trust
Jira + Forge app reads custom-field schema + user intent Your Cloudflare account · data plane Vectorize RAG over your own sources Workers AI on the edge, or your AI Gateway → your model validates every value against your field schema CONTENT LIVES HERE · never contacts the vendor Vendor control plane license key + instance id no content, ever intent + schema draft back The only path that carries content is Jira to your own worker. The vendor link is content-free by construction, and provable from the worker's own egress allowlist.
The one defensible gap

Rovo is good, and free, for most teams. Some buyers it cannot serve at all.

What Rovo does

Atlassian's Rovo gives grounded, AI-assisted issue creation across the Teamwork Graph. For the median customer it is genuinely good. Competing with it head-on is a losing move, so this product does not.

But Rovo sends your Jira and Confluence content to third-party LLMs outside Atlassian Cloud, you cannot bring your own model, and it populates standard fields while ignoring custom fields.

Where it leaves buyers locked out

Data sovereignty. Regulated buyers in finance, defense, government, and health are not permitted to send issue content to public LLMs they do not control. That rules Rovo out for this use case entirely.

Custom fields. Severity, squad, compliance scope, cost center: this is where real orgs encode how they actually work. Ignoring them leaves the AI's output half-finished.

Architecture

The moat is the topology, not a privacy policy.

The product and your content are kept physically apart. Your app phones home for licensing. Your Jira and Confluence content never reaches the vendor, because the path to do so does not exist.

01

Content stays in your tenant

The data-plane worker runs in your Cloudflare account. Retrieval over your sources and inference on your model both happen there. The output goes straight back to Jira. The vendor is never in that path.

02

You choose the model

Run an open model on Workers AI at the edge, or point an AI Gateway at your own private endpoint. Either way, inference never touches OpenAI, Google, or any public provider, and nothing trains on your data.

03

The vendor link is content-free by construction

Licensing carries only a key, an opaque instance id, and content-free counts. The data plane's egress allowlist does not contain the vendor domain, so a content call-home is not a thing you have to trust, it is a thing that cannot happen.

04

For the strictest buyers, no cloud at all

A reference backend runs the same contract fully air-gapped inside your VPC, with an offline signed license, for organizations that will not allow even a content-free ping.

Don't trust us. Curl it.

The egress posture is a verifiable artifact, not a claim.

Every deployment exposes two unauthenticated endpoints so your security team can audit the boundary before trusting anything. This is the honest answer to "where does my data go," rendered rather than asserted.

GET  https://your-worker.workers.dev/egress
// the only external destinations this worker can reach
{
  "allowed": [],
  "note": "Runs in your Cloudflare account. Inference is
    Workers AI on the edge, or your own AI Gateway,
    never a public LLM provider. No analytics or
    call-home. Nothing trains on your data. This
    data plane does NOT contact the vendor."
}
Custom fields, validated

It fills the fields your org actually runs on, and refuses to write garbage.

Your project's custom-field schema is a first-class input. Every value the model proposes is checked against the allowed options for that field. Anything that does not validate is dropped with an explicit reason, rather than written into Jira.

Drafted, grounded in your sources

Severity: High Labels: payments Labels: reliability Compliance: PCI

Each value traces to a retrieved source, cited on the draft.

Rejected, never written

Severity: Sev-1 ✕ not in allowed options Story Points: "large" ✕ not a number

The validator drops out-of-schema values with a warning, so your field hygiene survives the AI.

Who this is for

Built for the teams a public-LLM tool can never onboard.

Regulated finance

Banks, crypto exchanges, EMIs, and neobanks with a hard no-public-LLM or data-residency policy.

Defense & government

Contractors and agencies on Jira where inference location is a control, not a preference.

Jira Data Center

Teams that cannot use Rovo at all, and need the AI to live where their issues already do.

Health & regulated data

Organizations where issue and incident text is itself sensitive and cannot leave the perimeter.

Currently in design-partner conversations

Book a 15-minute technical review.

If your security team has ruled out AI in Jira because of where the data and inference go, this is the conversation. We will walk the architecture and let you audit the egress live. No deck.

Request a review Send your security team's questions