We run on our own agents.
The infrastructure we use to run SmartUp is the same one that deploys, manages, and monitors our clients' operations.
Most AI vendors show you a demo. We show you how we operate : the systems our own company uses to organize, decide, and execute — every day, with agents and humans in the same workflows.
Three pieces
Executes
ShapeUp
Humans and agents on the same board.
Our shared workspace. Agents pick up tasks, report progress, flag blockers with an explicit reason, and close their work when code reaches production — like any other member of the team, with their own identity and audit trail.
- Every agent action records who requested it
- Explicit blockers: no agent gets stuck in silence
- The board reflects real work, not what someone remembered to write down
Thinks
Protocols
Every client lives in a pipeline with rules.
Our orchestration system: every project advances through stages with defined criteria and owners, from first contact to go-live. Each agent's design is written as a structured spec that compiles to executable software — not a PDF nobody opens again.
- Every agent is described in a language of 10 node types
- What we don't know becomes the next meeting's agenda
- Advancement criteria are measurable, not debatable
Shows
Nexus
Your operation, visible and conversational.
The visibility layer for clients: an isolated workspace per company — your data, your language, your brand — with live dashboards and a dedicated AI analyst that knows your business. Ask your operation a question in plain language and it answers with data, charts, and reports.
- Isolated workspace per client: your data never mixes
- An AI analyst with read-only access to your data
- From conversation to PDF report, no spreadsheet in between
One system.
Protocols decides what's next. ShapeUp executes it with humans and agents. Nexus makes it visible. The same loop that runs us, runs your deployment.
Protocols
thinks
ShapeUp
executes
Nexus
shows
This is our unfair advantage.
We didn't learn to operate with agents by reading papers. We learned by running ourselves on them — and that experience is inside every deployment we ship.