Agents + MCPs. By 33Labs
Bizzy connects to your tools, takes on real workflows, and gives your team agents they can trust.
Behind the scenes, its routing layer sends every task through the right model and MCP for the job, so you keep the output and lose the waste.
Agents MCPs Routing Evaluation
How Bizzy works
Bizzy is the operating layer for AI work: it connects the context, chooses the route, and proves what happened.
Get a routing auditWe find the workflows, agents, and MCPs creating real leverage, and the places your team is paying too much for the same output.
Bizzy connects to the tools and context you already use. No forced migration, no new dashboard just to get started.
Each task is routed to the right model and toolchain, then measured for quality, latency, and spend. Your team keeps the output and loses the waste.
Trust, built in
Built by 33Labs — a decade of security engineering behind every deployment.
Anything that touches a client or a dollar waits for your OK before it goes out.
See the task, the selected model, latency, spend, and outcome — not a black box.
Bizzy works across your existing agents, MCPs, models, and business tools instead of asking you to replace them.
No. Bizzy sits alongside your existing models, coding tools, agents, MCPs, and business systems. We connect what works and improve the way tasks move through it.
Bizzy gives your team agents that can take on real workflows, then routes each task to the right model and toolchain. It can handle operational work, coding workflows, and MCP-connected tasks.
Most tools give you a model or an agent. Bizzy adds the operational layer around it: context, MCP connections, routing, measurement, and approval rules built around real work.
Anything client-facing or money-related waits for your OK before it goes out. Ambiguous cases go to a human. You approve; it never acts on its own.
For new builds, we scope the implementation. For teams already spending on AI, we can start with a token audit and price around the measurable value Bizzy creates. Exact numbers are always in writing first.
We review a representative slice of your AI workloads, identify where expensive models are doing work cheaper routes can handle, and show the opportunity before asking you to change anything.
Case studies
The businesses, the systems, and the lessons along the way. Explore the full stories behind the work.
Personal development & high-performance coaching
The systems behind a personal development and high-performance coaching company.
Global technology company
Building an AI-native workforce inside an approximately $3 billion technology company.
Legal technology platform
From financial documents to a reviewable divorce case file.