Most AI use cases get approved on a slide. Acro makes them earn it: it maps the process, calculates what the use case is worth and what it will cost to build, and runs the AI agent or workflow on realistic mock data so everyone can see it work before a single live system is touched.
Updated October 2026
Acro is Clustr Innovations' value engineering and agent simulation platform. It turns a business process into a costed AI use case, classifies each step as AI, automation, human review or a capability the client already owns, calculates ROI against the manual baseline, and simulates the AI agent or workflow on mock data before it is built for production.
| Used for | Deciding which AI use cases are worth building, and proving them before production |
|---|---|
| Business case | ROI, build cost, payback and ramp, with every assumption printed beside the result |
| Step classification | AI-powered, automated, human-in-the-loop, or native to a platform the client already licenses |
| Build types | AI agents, AI workflows and automations |
| Simulation | Mock data only, through branded nodes for systems such as Oracle, Salesforce and Claude |
| Handoff targets | Microsoft Copilot Studio, Claude Managed Agents, the Claude Agent SDK, ChatGPT and n8n |
| Template library | AI agents and workflows for finance and ERP, commercial real estate, and general business |
Acro takes a use case through five steps: describe the process, classify every step, calculate the business case, simulate the build on mock data, and hand it off. Nothing reaches production until the first four hold up.
A business owner describes the work in plain language, or starts from a template. Acro asks clarifying questions, records the manual steps, how long each takes and how often it runs, and drafts the build.
Each step is marked as AI-powered, automated, human-in-the-loop, or native: something a platform the client already pays for can do today. That last label stops teams building what they already own.
Acro compares the manual baseline with the AI version: hours returned, error cost, build cost including testing, security review and training, maintenance, ramp-up time and payback. Every assumption is editable and printed beside the numbers, so finance can check the math.
Press play and mock data flows through the agent or workflow, node by node. Anyone can open a step and inspect its input and output. No live system, customer record or production credential is involved.
Once approved, Acro produces the build report and the instructions for the platform the client will run it on, from Microsoft Copilot Studio and Claude to ChatGPT and n8n.
Acro is the rehearsal between an idea and a build. The decision to build is made on a business case and a working simulation, not a slide.
A team brings a process it wants AI to help with.
Steps, minutes per run, runs per month, people involved.
ROI, build cost and payback, with assumptions shown.
The agent runs on mock data, every step inspectable.
Owners and finance decide on evidence, not a demo video.
Handed off to Copilot Studio, Claude, ChatGPT or n8n.
A team leaves Acro with evidence a business owner can defend and a design an engineer can build from: the business case, a step-by-step classification, a recorded simulation and a handoff for the target platform.
ROI, build cost, maintenance, ramp-up and payback against the manual baseline, with the assumptions listed so the numbers can be challenged.
Which steps need AI, which need plain automation, where a person must stay in the loop, and what the client's existing platforms already do.
The agent or workflow running end to end on realistic mock data, with before and after views of the process.
A build report and platform instructions for the target runtime, so the production build starts from an approved design.
Updated October 2026
Start from the manual process: minutes per run, runs per month, the loaded cost of the people doing it and the cost of errors. Compare that with the AI version, then subtract the full build cost, including testing, security review and training, plus ongoing maintenance and a realistic ramp-up period. Acro does this for every use case and shows each assumption beside the result.
Yes. Acro simulates the agent on realistic mock data that flows through representations of your systems, so stakeholders can inspect every step's input and output before anything is connected to live data.
No. Acro classifies each step as AI-powered, automated, human-in-the-loop, or native to a platform you already own. Often the honest answer is a simple automation, or a feature you are already paying for.
Microsoft Copilot Studio, Claude Managed Agents, the Claude Agent SDK, ChatGPT and OpenAI, and n8n. The design stays platform-neutral until the handoff.
Acro is where a use case is designed, costed and rehearsed. The production build happens on the platform you choose, with Clustr or your own team, starting from the approved design.
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