One platform. Framed for your role.
The same supervised runtime, reframed for the seat you sit in. Pick your role and see how JuhJuh works for you.
Know what AI costs. Control what it ships.
JuhJuh gives you token-level cost attribution per Task, five independent safety gates before production, and a complete audit trail your compliance team can actually use.
Token-Level Cost Attribution
Every AI execution logs input tokens, output tokens, model used, and USD cost. Costs roll up to Tasks, projects, and teams automatically. You answer the board's ROI question with a dashboard, not a spreadsheet.
Cost tracked per Task, per project, per teamFive-Layer Safety Model
Prompt Review, Branch Isolation, Diff Review, PR Approval, and Pipeline Gate form five independent checkpoints. Each gate requires explicit human action before code advances. No AI output touches production without passing all five.
5 independent safety gatesFull Audit Trail
Every action records who initiated it, what changed, what it cost, and who approved it. Your compliance team gets timestamped evidence for every AI-generated change. Exportable logs map to SOC 2 control requirements.
Every action logged with who, what, when, cost4-Level Access Control
Owner, Admin, Member, and Viewer roles scope to both workspaces and projects. You decide who configures AI agents, who executes, and who approves PRs. Permission boundaries enforce separation of duties.
4 RBAC levels across workspaces and projectsWorkspace-Wide AI Governance
Agent instructions cascade from workspace to project to team to developer. Set security rules at the workspace level and they apply to every execution across every project. No team can bypass what you define at the top.
4-level instruction cascadeExecutive Analytics
Cycle time breakdowns, execution success rates, and cost-per-Task trends on a single dashboard. You see which projects benefit most from AI investment and which need process changes. Data for quarterly business reviews, not just engineering stand-ups.
Cost, velocity, and success rate per projectAgent Governance
11 specialist AI agents operate under your workspace rules. Every agent action logged. Trigger rules and skills managed from a central dashboard. Anti-hallucination safeguards built into every agent prompt.
11 specialist agents with full audit trailDeployment and Vault Controls
One-click deployments with semantic versioning and full rollback history. Encrypted vault with snapshot diffing for secrets governance. Auto-discovery of environment variables across your infrastructure.
Encrypted vault with versioned snapshotsBrand Voice Governance
Define your workspace writing style across 6 core traits and 15 tone contexts. Every content-producing execution follows your voice guidelines. Consistency across teams without style guides nobody reads.
6 voice traits and 15 tone contextsYou pull credit card statements, estimate per-developer usage from subscription plans, and build a spreadsheet that is outdated by the time you present it. No one trusts the numbers.
You open JuhJuh's analytics dashboard. Token costs are attributed to every Task and project automatically. You export a report showing cost-per-PR by team. The CFO gets exact numbers in five minutes.
You explain that developers use AI tools in their IDEs and code goes through normal PR review. Auditors ask how you know which code was AI-generated. You cannot prove it.
You show the audit trail: every AI execution, the prompts used, the diffs generated, who reviewed them, who approved the PR, and whether CI passed. Five gates, all logged. Auditors close the finding.
AI development your board approves.
Full cost visibility. Five safety layers. One audit trail.
Ship more with the team you have.
JuhJuh connects Task creation to pull request in one pipeline. Your team stops context-switching between tools and starts shipping features.
Idea-to-PR Pipeline
A Task syncs from Jira or Linear. AI analyzes complexity and affected files. Prompts generate automatically. Code executes in isolated branches. Diffs go through human review. PRs land with structured descriptions. One pipeline, every stage, zero manual handoffs.
8 pipeline states, zero context-switchingAuto-Play Execution
Queue an Epic's child Tasks for sequential execution. Each Task inherits the full context of completed siblings. The tenth Task knows what the first nine shipped. Wake up to a board full of PRs ready for review.
Sequential execution with shared sibling contextMulti-Repo Orchestration
Connect multiple repositories to a single project. Service dependency mapping shows which repos a change affects. Branch isolation means parallel execution across repos without branch conflicts. One feature, three repos, one coordinated workflow.
7 service dependency types trackedVelocity Analytics
Cycle time from Task creation to merged PR, broken down by stage. Execution success rates per project and per team. You see where the pipeline stalls and fix the bottleneck, not the symptom.
Cycle time, success rate, and throughput per teamShared Prompt Templates
Versioned prompt templates with placeholder substitution and include directives. One engineer writes a good API endpoint template. Every subsequent API Task uses it. Senior knowledge scales to the whole team without meetings.
75 prompt templates with version control200+ Integrations
Jira, Linear, GitHub, GitLab, Bitbucket, Slack, Salesforce, Shopify, Stripe, and 190 more across 23 categories. Two-way sync keeps your existing tools. JuhJuh orchestrates the workflow between them.
200+ integrations across 23 categoriesAutonomous AI Agents
11 specialist agents handle research, code analysis, security audits, and performance monitoring. Trigger rules fire on schedule or on Task events. Skill tracking improves agent performance over time.
11 specialist agents with trigger rulesOne-Click Deployments
Auto-generated deployment configuration from project settings. Semantic versioning, real-time log viewer, service controls, auto-firewall, and encrypted vault with secrets management. Ship from dashboard to production.
Config-driven deploys with semantic versioningYou assign three developers. They coordinate through Slack threads over two days. The frontend developer waits for the API contract. The types package update gets forgotten until CI fails. Three PRs merge in the wrong order. Hotfix by Thursday.
You create an Epic with three child Tasks ordered by dependency. Auto-Play executes them sequentially. The frontend Task receives the actual API schema from the backend execution. Three PRs, correct order, reviewed by lunch.
You compare story points before and after rollout. The numbers are noisy. Team composition changed. Task complexity varies. You present a chart that proves nothing and satisfies no one.
JuhJuh shows cycle time per Task type, execution success rates by team, and cost per merged PR. You show that API Tasks complete faster through the pipeline. You have data, not anecdotes.
From Task to pull request. One pipeline.
Eight states. 200+ integrations. Measurable velocity.
Your standards. Every AI execution.
200+ integrations, a knowledge graph with 14 categories, and cascading agent instructions turn your team's best practices into every AI execution's default behavior.
4-Level Agent Instructions
Workspace, project, team, and developer instructions cascade with inheritance. Define your error handling pattern at the project level and every execution follows it. Override at the team level for backend-specific conventions. Standards flow down. Exceptions stay scoped.
4-level cascade14 Knowledge Categories with Hybrid Vector Search
Architecture decisions, domain patterns, API contracts, migration history, and 10 more categories store institutional knowledge. 8-signal hybrid search with semantic matching and graph-based ranking. Your agents know about last month's library migration because the knowledge graph connects it.
14 categories with 8-signal hybrid searchVersioned Prompt Templates
Templates use placeholder substitution and include directives to inject project context, coding standards, and architectural patterns into every prompt. Write a good template once. Every execution inherits it. Version history shows what changed and when.
75 templates with version controlLine-by-Line Diff Review
Every AI-generated change surfaces as a structured diff before touching your codebase. Approve individual files, reject sections, or iterate with follow-up prompts. Nothing merges until you say it meets your standards.
File-level approve/reject with rollback6-Persona AI Review
Architect, frontend, backend, security, QA, and PM personas review the same Task from different angles before a single prompt is written. The security persona catches auth gaps. The QA persona flags missing test cases. You get six perspectives in seconds.
6 AI personas review every TaskKnowledge From Every Execution
Learnings from completed Tasks feed back into the knowledge base. When an execution reveals a new pattern or an undocumented dependency, capture it in one click. Future executions inherit that knowledge automatically. Your platform gets sharper with every Task shipped.
Automatic knowledge extractionSpecialist Agents Enforce Standards
Security-auditor and code-analyzer agents review every Task from security and code quality angles. They flag auth gaps, leaked secrets, N+1 queries, and style violations before code reaches your branch. Standards enforcement is automatic. Your review time drops.
Security and code-quality agents on every TaskBrand Voice Enforcement
6 core traits and 15 tone contexts define your team's writing style. Every piece of content the AI produces passes through a humanizer that enforces your voice. Documentation, commit messages, and user-facing copy all sound like your team wrote them.
6 traits across 15 tone contexts200+ Integrations
Your agents connect to the tools your team already uses. 200+ integrations across 23 categories mean pipelines pull context from your issue tracker, deploy through your CI, and post updates to your channels. No custom glue code. One catalog. Every tool accessible.
200+ integrations across 23 categoriesThey ask three different teammates how to prompt the AI for your codebase. Each gives different advice. Their first PR uses the wrong ORM pattern. You spend an hour in code review explaining conventions that are not written down anywhere.
Project-level agent instructions define your ORM patterns, error handling, and test conventions. The knowledge base contains architecture decisions and migration history. The new developer's first AI execution follows your standards because the platform enforces them.
You reject PRs and add comments explaining the pattern. The developer re-prompts the AI. Sometimes it works, sometimes it does not. You spend more time reviewing AI output than writing your own code.
You add the repository pattern to project-level agent instructions and create a knowledge entry with examples. Every AI execution receives this context. The next 50 Tasks follow the pattern without you reviewing each one.
Your team's standards, enforced everywhere.
14 knowledge categories. 75 versioned templates. Standards that cascade.
One platform. Framed for your role.
Whichever seat you sit in, JuhJuh is the same supervised runtime underneath.
