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Name: Rangor
Author: Rangor

Understand any task in 30 seconds — not 30 documents.

Jira PROJ-482 · Webhook security

GitHub PR #318 · Signing middleware

Slack #platform · Scope decision

Rangor briefing PROJ-482

Ship webhook signing before launch

Summary

Security changes must ship before the Aug 1 launch — signing required, old keys retire.

Must know

Incoming webhooks must be signed — security review mandate
Legacy API keys stop working Aug 1

Risk

Platform team still disputes whether internal hooks need signing before launch.

Brief ready

Your AI is working blind

The full story lives across tickets, shared drives, wikis, and threads — but your agent only sees what you remember to paste.

Without Rangor

You hunt. You paste. The model fills the gaps.

Hunt across five tabs Jira, Drive, Slack, wiki — nothing wires itself to your agent.
Siloed by tool Jira here, Confluence there, PR on Git — three tabs, three pastes, nothing connects itself.
Too little or too much Skip a doc and the AI guesses. Dump everything and it drowns in noise.
Decisions get buried What was agreed? What's still disputed? Lost in comment thread #47.
Same work, every task Every ticket means rebuilding the same brief from scratch.

With Rangor

Open the task. Get the brief.

Full picture in seconds Rangor pulls ranked facts from every connected source — no copy-paste.
One package across tools Ticket, wiki page, PR, and Drive file — merged by the graph into one ranked brief. ACL on every object.
A brief, not a dump Only what matters for this task, compact enough for any agent.
Nothing swept under the rug Decisions, disagreements, and where each fact came from — all visible.
Where you already work Sidebar, Cursor, ChatGPT, Claude — same package everywhere.

One brief instead of 18,500 → 1,200 tokens (example thread; median ~2–4×)

One briefing — not a scavenger hunt

The same card shape everywhere: summary, must-know facts, risks, and what changed since yesterday.

Rangor briefing

Engineering PROJ-482

Summary

Security changes must ship before the Aug 1 launch.

Must know

Webhook signing required — security review
Legacy keys retire Aug 1

Changed since yesterday

PR #318 opened; platform comment disputes internal-hook scope.

Risk

Open disagreement on signing scope before launch.

Switch examples in the hero above — same briefing pattern for every team.

Every task makes the next one smarter

When your team finishes work, Rangor learns which facts actually helped — the next briefing surfaces what your team cites, not generic search noise.

This week on your team

12 briefings opened

~3h estimated time saved

Ranking improves from real outcomes — not from guessing.

Bonus · when work moves

Context that finds you

After you understand the pull flow — Rangor can also react to PR merges and assignee changes.

PR with Fixes PROJ-482 — Rangor checks the diff against ticket decisions before merge.

GitHub PR acme/api · #128

PR opened · Fixes PROJ-482

-  await retryWebhook(payload, 3)
+  await sendWebhookOnce(payload)

Checking against ticket decisions…

Where you already work

Same briefing — pick your surface. No re-wiring per tool.

Chrome Sidebar

Open a ticket or page — briefing appears beside your tab.

Cursor & MCP

Connect once — context in the agent without copy-paste.

Terminal

npx @rangor/cli init then rangor context …

Dashboard

Connect sources, team invites, integrations.

Full product tour →

For engineers & security

How it works under the hood

Open format, permission-filtered graph, provenance on every claim — details below.

Every source → one package

Connectors index into the same temporal graph. Open a Jira key, Confluence page, or PR — Rangor follows links (ticket keys, URLs, team-defined edges) and assembles one unified UCP, not separate fetches per system.

Data sources

Jira Confluence GitHub Gitea Bitbucket Google Drive + more planned

Your agents

Cursor ChatGPT Claude Sidebar

Unified context — not seven separate tools

Your feature spans Jira, Confluence, GitHub or Gitea, and Drive — but your agent shouldn't need four separate calls. Rangor follows links in the graph — ticket keys, URLs, remote links, manual team links — and assembles one permission-filtered package. Jira is a convenient hub, not a requirement.

Jira PROJ-482 ticket + Confluence spec + GitHub PR + Drive policy
Confluence PAY:482 wiki page + Gitea PR + linked files — no Jira required
Gitea acme/api#16 PR thread + spec page + manual cross-links

Not search. Not RAG. A context layer.

Glean finds documents. RAG finds similar chunks. Rovo stays inside Atlassian. Rangor assembles the state of the task — permission-filtered, provenance-backed, ready for your agent.

Glean · enterprise search

Query → doc links

You ask a question; it returns search results and chat answers. Synthesis stays on you — no task trigger, no structured package for agents.

RAG pipelines

Query → similar chunks

Embeddings retrieve text that looks like your prompt. Stale and current facts rank alike — no decisions, conflicts, or temporal validity.

Atlassian Rovo

Vendor-native AI

AI inside Jira and Confluence. Strong within Atlassian — but not a cross-source context graph with portable output for Cursor or other agents.

Rangor

Task → UCP package

Open any linked ticket, page, or PR — no query required. The graph merges neighbors from every connected system into one UCP: must-know facts, decisions, conflicts, and context_diff — to Cursor, ChatGPT, Claude, or Sidebar.

How Rangor compares to enterprise search, RAG, and Atlassian Rovo
	Glean	RAG	Rovo	Rangor
Trigger	User query	User query	Ask in Atlassian	Open task / issue
Output	Search results, chat	Text chunks	Chat in Jira/Confluence	Structured UCP
Knowledge model	Search index	Vector store	Atlassian data	Temporal graph
Cross-source assembly	Separate searches	Separate chunks	Mostly Atlassian-only	One UCP from linked sources
What changed?	—	—	—	`context_diff`
Conflicts visible	—	—	—	Explicit section
Learns from your team	—	—	—	Usage Receipts → warm ranking
Agent delivery	API / chat UI	Custom pipeline	Atlassian UI	MCP, Actions, UCP

Four questions search and RAG can't answer

What's true now?

Temporal graph with valid-from / valid-to — invalidated facts don't resurface in top results.

What changed since last visit?

context_diff against your last view of the task — not a fresh search every time.

Why was it decided?

Decisions layer with provenance — not buried in comment thread #47.

What contradicts?

Conflicts from graph invalidations stay explicit instead of being merged away.

Five things only a context layer does

Search finds documents. RAG finds chunks. Rangor assembles the state of the task — and keeps improving it for your team.

One unified package

Start from a ticket, wiki page, or PR — Rangor traverses the cross-source graph and pulls linked neighbors into a single UCP. Confluence + self-hosted Git works without Jira. Deterministic links, ACL on every object.

Permissions travel with context

ACL from every connected source syncs to every package. You never see a fact from a file or page you can't open — filtered per user, not per workspace.

Every claim is verifiable

Schema-valid UCP output: each fact cites a source, each source carries a sha256 hash. Disagreements stay visible instead of being merged away.

Context that learns

Usage Receipts record which claims your agent cited. Warm ranking boosts what helped your team — privacy-safe, claim IDs only.

Open format, managed platform

Rangor ships UCP — the open Universal Context Package we steward at ucpcore.org. Portable structure, fully managed SaaS at app.rangor.io.

Inside every package

One JSON document. Predictable sections any agent — or human — can consume. MCP carries access; UCP carries understanding.

must_know — ranked facts with salience scores
decisions — what was decided, when, and status
conflicts — contradictions kept explicit
context_diff — what changed since your last visit
usage_receipts — feedback loop for warm ranking (claim IDs, not text)
related_objects — cross-source neighbors merged into one package
sources — hashed provenance for every claim

Read the UCP spec

{ "summary": { … }, "must_know": [ … ], // salience-ranked "related_objects": [ … ], // cross-source "decisions": [ … ], // accepted / rejected "conflicts": [ … ], // kept visible "context_diff": { … }, "usage_receipts": [ … ], // learning loop "sources": { … } // sha256-hashed }

See what changed since your last visit

Search and RAG start fresh every time. Rangor remembers when you last opened a task and highlights only the delta — new decisions, moved deadlines, resolved conflicts.

Glean and typical RAG can't answer: «what changed since I was here Tuesday?»

PROJ-482 Last visit · Tue 2:14 PM

You open the ticket again three days later…

How it works

Connect once. Every task gets a ranked, permission-filtered package — in Sidebar, Cursor, ChatGPT, Claude, or the Dashboard. Submit Usage Receipts after you ship so ranking learns your team.

Connect sources

OAuth or tokens in the Dashboard — or connect GitHub, Gitea, and Jira from the terminal with rangor connect after npx @rangor/cli init.

JiraConfluenceGitHubGoogle DriveGitea

Build task context

Cross-source graph + salience ranking — no LLM in the ranking loop (graph, heuristics, Usage Receipts). Structural conflicts come from the temporal graph; optional LLM enrichment on generate can surface disagreements buried in comment prose.

Ranking decides what matters most. LLM enrichment, when enabled, helps with semantic synthesis and prose-only conflicts — not salience order.

Use anywhere

Browser Sidebar, Cursor, ChatGPT, Claude, Dashboard — or terminal briefings with rangor context.

Submit Usage Receipts

Mark which claims your agent used and whether the outcome worked. Warm ranking boosts what helped — claim IDs only, never your document text.

One context layer, every agent

Deep dive — CLI terminal, MCP setup, and per-agent configuration.

Same engine underneath — including the learning loop from Usage Receipts. Pick how your team loads task context.

Terminal onboarding & connect

npx @rangor/cli init signs you in, patches MCP for Cursor and other editors, and prints your first briefing. Connect sources without leaving the shell — same ACL and packages as the Dashboard.

rangor connect github — OAuth, gh CLI, or PAT + repo picker
rangor connect gitea — self-hosted base URL + PAT (private org repos)
rangor connect jira — Cloud OAuth or Server PAT + projects
--reconnect — rotate tokens without opening Integrations
Repo-level context: rangor context github acme/johndow · rangor context gitea acme/johndow

Try the live terminal → type help or click a shortcut.

terminal · rangor interactive

Your agent loads the task context

HTTP MCP with OAuth. Ask Cursor to load context for PROJ-123 or owner/repo#42. When the fix ships, send a usage receipt so ranking learns what helped.

{
  "mcpServers": {
    "rangor": {
      "url": "https://mcp.rangor.io/v1/your-team/mcp"
    }
  }
}

cursor · rangormcp

$ /ucp PROJ-482 ✓ generated UCP · 7 must-know · 2 decisions ✓ ~1,180 tokens rendered → usage receipt · mk-1 cited · merged

Proof: measured on real issues

Same token estimator (~4 chars/token). Raw thread = everything you'd paste into the model. UCP = ranked task context with decisions and provenance intact.

microsoft/vscode#519 · 596 comments over ten years

raw thread~18,500 tokens

ucp package~1,200 tokens

up to 15× smaller on large threads — with decisions, conflicts, and hashed sources. Typical median across issues is ~2–4×.

More benchmarks

Issue	Comments	Raw thread	UCP
microsoft/vscode#519	first 200 of 596	~18,500	~1,200
rust-lang/rust#158622	12	~4,450	~1,450
pallets/flask#5961	4	~800	~700
pallets/flask#5948	0	~500	~330

Large decade-long threads can compress ~15× while keeping decisions, conflicts, and provenance; median across a random sample is typically ~2–4×. Reproduce with benchmark_context.py (--sample N --json) or try live at ucpcore.org/try.

Try the format in browser Sign in to try

Built on open standards — run as managed SaaS

No fake logos. Here's what you can verify today.

Authors of UCP

We steward the open Universal Context Package spec at ucpcore.org — portable format, vendor-neutral protocol.

Reference platform

Rangor is the hosted implementation at app.rangor.io — indexing, graph, ranking, and MCP included.

Permissions first

Every package filtered per user from source ACLs. Fail closed — no data you can't already open.

Privacy-safe learning

Usage Receipts improve ranking with claim IDs only — not your document text.

Reproducible benchmarks

Token savings claims link to public scripts and live demos — try the format yourself.

Beta, clearly labeled

v0.6.10-alpha.1 — features and pricing evolve in the open. Enterprise SLAs on request.

Enterprise trust, built in

The first question from every security team: «will the AI see data my user can't access?» Rangor's answer is structural — not a policy page.

Permission-aware by design

Relationship-based access control syncs permissions from every connected source. Context packages are filtered per user before delivery.

Full provenance

Every claim links to hashed sources. Temporal graph tracks valid_from / valid_to — contradictions surface instead of hiding.

Fully managed SaaS

Hosted at app.rangor.io — connect your sources, we run indexing, graph, and MCP. No servers to provision.

Audit & team workspace

Shared index per organization with per-user visibility. Access audit for compliance reviews.

We wrote the standard

Rangor is the reference SaaS for UCP — Universal Context Package. Open spec at ucpcore.org; managed context layer at app.rangor.io. Protocol and product, separated on purpose.

Explore UCP spec

A new category — and a compounding moat

For teams evaluating category creation vs incremental search AI.

Context layer, not search

Search answers a question. Rangor assembles task state — triggered by opening a ticket, not typing a query.

Architecture

Precomputed temporal graph

Knowledge indexed ahead of time — enables context_diff, conflicts, and fast package assembly. Not on-demand RAG per request.

Flywheel

Open format · closed ranking

UCP is open at ucpcore.org. Team-specific warm ranking from Usage Receipts — data moat that doesn't export.

FAQ

Plain answers to what teams ask before they connect their first source.

Is Rangor just RAG?

No. RAG retrieves similar text chunks for a query. Rangor builds the state of a task from a precomputed graph — what's decided, what's disputed, what changed since your last visit — without you writing a prompt.

How is this different from Glean or Copilot in Jira?

Enterprise search answers questions across documents. Copilot summarizes inside one vendor. Rangor connects multiple sources and delivers a structured, permission-filtered package to Cursor, ChatGPT, Claude, or any agent — triggered by the task you opened, not a chat box.

Do you train AI models on our data?

No. Your content is used to build context packages for your workspace. Usage Receipts store claim IDs and outcomes for ranking — not document text.

Can we self-host?

Rangor is fully managed SaaS at app.rangor.io. Enterprise plans offer dedicated tenant isolation — contact us for deployment options.

What's included in the free plan?

Full UCP format, Sidebar, Cursor MCP, ChatGPT & Claude Actions, one data connector (GitHub public or one Jira project), 50 packages per month, and polling sync. Upgrade to Team for all connectors, shared workspace, and unlimited packages.

Will the AI see documents I can't access?

No. ACLs from Jira, Drive, GitHub, and other sources sync into every package. Context is filtered per user before delivery — fail closed.

What sources do you support?

Jira, Confluence, GitHub, Gitea, Bitbucket Server, and Google Drive today — with more connectors on the roadmap. Connect in the Dashboard with OAuth or tokens, or use rangor connect in the terminal after npx @rangor/cli init.

Does Rangor use LLMs?

Ranking — what surfaces first in must_know — uses the temporal graph, heuristics, and Usage Receipts. No LLM in that loop. Optional LLM enrichment on generate adds semantic synthesis and can extract conflicts or decisions stated only in comment prose; graph-based conflicts and cross-source merge work without it. Provenance is preserved either way.

Is annual billing available?

Yes — Team and Business include annual prepay at 20% off per seat ($240 and $432 per seat per year — $20/mo and $36/mo equivalents). Use the Annual toggle above; Enterprise is custom annual contracts via sales.

Is each connector a separate context call?

No. Every source indexes into the same graph. When you open any linked item — Jira key, Confluence page, GitHub or Gitea PR — Rangor assembles neighbors from other systems into one UCP. Links come from ticket keys, URLs in text, Jira remote links, and manual team links you confirm in the Dashboard — not from the model guessing.

Do I need Jira as the hub?

No. Jira is a common anchor, but you can start from Confluence, GitHub, Gitea, Bitbucket, or Drive. If the graph connects a wiki page to a self-hosted PR and a policy file, one context load returns all three — permission-filtered and ranked together.

How does Rangor learn over time?

When your agent uses a package, you submit a Usage Receipt — which claim IDs were cited, dismissed, and whether the outcome worked. Warm ranking uses those signals inside your workspace only. No document text is stored in receipts; the next package for similar tasks ranks higher on what actually helped your team.

What are Context Guard and Handoff Brief?

Context Guard (Team) watches GitHub PRs that reference a ticket (Fixes KAN-9). Rangor checks the diff against decisions and open conflicts, comments on the PR before merge, and records a warned receipt. Handoff Brief fires when a Jira assignee changes — a structured comment with context_diff for the new owner. Both are push context; rangor context in the CLI is pull. See Push context on this page.

Can I connect sources from the terminal?

Yes. Run npx @rangor/cli init, then rangor connect github (OAuth or PAT), rangor connect gitea (self-hosted URL + PAT), or rangor connect jira (Cloud OAuth or Server PAT). Add --reconnect to rotate a token without the Dashboard. Briefings: rangor context jira PROJ-1, rangor context github owner/repo, rangor context gitea owner/repo.

How do I connect ChatGPT or Claude?

Open the Dashboard → Agent connectors. For ChatGPT: import the OpenAPI schema into a Custom GPT and complete the OAuth wizard. For Claude: import the same schema into a Project and authenticate with a token. Cursor and other IDEs use MCP from the same page.

What is UCP?

Universal Context Package — the open JSON format for task context we publish at ucpcore.org. Rangor is the reference SaaS; any agent can consume UCP.

Pricing

Sign up in minutes — no credit card. Every tier gets the full UCP format; limits are on scale, sources, and team features.

Solo developer

Free

Full UCP — decisions, conflicts, provenance
Cursor MCP + Sidebar + ChatGPT / Claude Actions
1 connector (GitHub public or 1 Jira project)
50 packages / month
Cold ranking (structural salience)
Polling sync (~15 min)
Usage Receipts

Get started

Teams · 3–50 seats

Team

$25 / seat / mo

Shared workspace & invites
All connectors (Jira, Drive, GitHub…)
Unlimited packages
Webhooks + incremental sync
Warm ranking & team learning loop — no LLM in the ranking loop
Context Guard — PR push insurance (receipt + conflict warnings)
Handoff Brief — assignee change → ranked context in Jira
Permission-aware access control

Get started

Security & compliance

Business

$45 / seat / mo

Everything in Team
SSO / SAML
Audit log export
Admin roles & access review
Priority support

Get started

Regulated & large orgs

Enterprise

Custom

Everything in Business
Dedicated tenant / isolated infra
Custom SLA & DPA
Security review & compliance pack
Multi-org & volume billing

Contact sales

Limits and tiers may change. Free: cold ranking. Team+: warm ranking from Usage Receipts. Team and Business: monthly or annual prepay (−20%). Enterprise: custom annual contracts. Business SSO on request.

Load your first task context

Create a workspace with npx @rangor/cli init — connect sources in the terminal or Dashboard, then wire Cursor, ChatGPT, or Claude.

Get started See CLI tab

You opened a task.Here's everything you need to know.

Your AI is working blind

One briefing — not a scavenger hunt

Every task makes the next one smarter

Context that finds you

Where you already work

Chrome Sidebar

Cursor & MCP

Terminal

Dashboard

How it works under the hood

Every source → one package

Unified context — not seven separate tools

Not search. Not RAG. A context layer.

Query → doc links

Query → similar chunks

Vendor-native AI

Task → UCP package

Four questions search and RAG can't answer

Five things only a context layer does

One unified package

Permissions travel with context

Every claim is verifiable

Context that learns

Open format, managed platform

Inside every package

See what changed since your last visit

How it works

Connect sources

Build task context

Use anywhere

Submit Usage Receipts

One context layer, every agent

Terminal onboarding & connect

Context beside the ticket

Your agent loads the task context

Custom GPT with your team's context

Claude Projects with indexed context

Dashboard — workspace & connectors

Proof: measured on real issues

More benchmarks

Built on open standards — run as managed SaaS

Authors of UCP

Reference platform

Permissions first

Privacy-safe learning

Reproducible benchmarks

Beta, clearly labeled

Enterprise trust, built in

Permission-aware by design

Full provenance

Fully managed SaaS

Audit & team workspace

We wrote the standard

A new category — and a compounding moat

Context layer, not search

Precomputed temporal graph

Open format · closed ranking

FAQ

Pricing

Free

Team

Business

Enterprise

Load your first task context

You opened a task.
Here's everything you need to know.