Skip to main content
AI Infrastructure

Your AI Workforce, Fully Orchestrated

Build, deploy, and manage AI agents that work as a team. Define skills, assign workflows, and monitor everything from a single dashboard.

Create, configure, and orchestrate AI agents as your digital workforce

Built for real clients·Own the code forever
01 · The problem

Managing AI Agents Is Chaos

  • AI agents scattered across tools with no central management
  • No visibility into agent performance, costs, or reliability
  • Building multi-agent workflows requires custom engineering
  • Knowledge stays siloed—agents can't share context
02 · The solution

One Platform to Rule Your AI Workforce

  • Central dashboard to create, configure, and monitor all agents
  • Visual workflow builder for multi-step agent orchestration
  • Shared knowledge bases with vector search and graph memory
  • Real-time cost tracking and performance monitoring
03 · What's inside

Features shipped in Quibcl.

Agent Builder

Define agents with system prompts, model selection, tool access, and MCP server integrations—all through an intuitive interface.

Team Organization

Group agents into functional teams with shared context and coordinated workflows.

Visual Workflow Builder

Design multi-step agent workflows with a drag-and-drop builder. Define dependencies, branching logic, and agent spawning.

Knowledge Management

Build knowledge bases with vector embeddings and graph memory. Agents answer using your organization's own data.

Skill Library

Create reusable skills and assign them across agents. Build once, deploy everywhere.

Real-Time Monitoring

Track agent sessions, costs, and performance in real time with live dashboards and audit logs.

04 · Who it's for

Teams using Quibcl.

AI-First Companies

Manage your entire fleet of AI agents from one platform instead of juggling dozens of tools.

Before

Agents scattered across platforms, no central visibility or cost control

After

Unified dashboard with org-wide agent management, cost tracking, and audit trails

Enterprise Operations

Orchestrate complex business processes with multi-agent workflows.

Before

Manual coordination of AI tools across departments with no shared context

After

Automated multi-step workflows with shared knowledge bases and team coordination

AI Development Teams

Rapidly prototype and deploy AI agent architectures with built-in best practices.

Before

Weeks of custom engineering for each new agent deployment

After

Configure and deploy new agents in minutes with reusable skills and workflow templates

05 · How to get it

A starting point, not a checkout.

Quibcl is one of the patterns we've already built. On a free discovery call we scope the one outcome you're after, decide whether Quibcl fits or whether a setup or custom build serves you better, and give you a fixed price before any work starts. Whatever we deploy, the code and the data are yours. Setup engagements start from $500.

06 · The fine print

About Quibcl.

What AI models does Quibcl support?

Any model accessible via API—OpenAI, Anthropic Claude, Google Gemini, and local models via Ollama. Each agent can use a different model based on the task.

How is this different from just using an AI API?

Quibcl adds the management layer: agent configuration, team coordination, workflow orchestration, knowledge bases, cost tracking, and audit logs. It's the difference between having employees and having an HR system.

Can agents share knowledge and context?

Yes. Knowledge bases with vector embeddings and graph memory are shared across agents within your organization. Agents can also spawn sub-agents and pass context through workflow steps.

Is my data secure?

Self-hosted deployment means your data never leaves your infrastructure. All agent interactions are logged with full audit trails. Role-based access control ensures the right people see the right data.

How many agents can I run?

It supports up to 20 agents out of the box and scales to hundreds with a custom configuration. The platform is designed for horizontal scaling.
07 · Technical requirements

What you need.

SELF · HOSTED

Run it yourself

  • Node.js 22+ or Docker
  • PostgreSQL 17+ with pgvector
  • Redis 7+ for job queues
  • 8GB RAM minimum, 16GB recommended
  • Optional: GPU for local AI models via Ollama
INTRAVERSE · SETUP

Or we set it up for you

  • Modern web browser
  • Internet connection
  • AI model API keys (OpenAI, Anthropic, etc.)

No technical setup on your side. We configure it on your systems and hand it over.

08 · Get started

See if Quibcl fits your outcome.

Book a free discovery call. We'll scope the one outcome you're after and tell you whether Quibcl, a Claude setup, or a custom build is the right fit. You own whatever we deploy.