Question 1

What's the difference between OpenOperative and an AI consulting firm?

Accepted Answer

Consultants advise. We build and deploy. The engagement ends when the system is running in production — not when the deck is delivered.

Question 2

Do you replace our existing tools and platforms?

Accepted Answer

We default to what you already have. Where gaps exist, we bring in best-in-class tooling — selected for the requirement, not for preference.

Question 3

How much control does our team retain over agent decisions?

Accepted Answer

Full control. Every agent operates within the decision boundaries and permissions you define — nothing beyond them.

Question 4

What happens when an agent gets something wrong?

Accepted Answer

It escalates to a human rather than guessing. Every system ships with exception handling, audit logs, and defined escalation paths.

Question 5

How do you handle our proprietary data?

Accepted Answer

It stays in your environment. We don't use client data to train models, and every integration is built within your existing security and governance frameworks.

Question 6

We already have an internal AI team. How do you work alongside them?

Accepted Answer

As a specialist build partner, not a replacement. Full documentation and knowledge transfer are standard — your team owns everything we deliver.

Question 7

Which AI models do you use?

Accepted Answer

Whichever performs best for your requirements. We are model-agnostic — no vendor preference, only performance preference.

Question 8

How long does a typical engagement take?

Accepted Answer

First production-ready system: 6 to 12 weeks depending on integration complexity. Scoped precisely during the System Audit.

Question 9

What does the System Audit cover?

Accepted Answer

Your workflows, integration landscape, and highest-value automation opportunities — with a clear build plan and success criteria at the end. Not an open-ended discovery phase.

Question 10

How do you measure success?

Accepted Answer

We agree on measurable outcomes before we build. If we cannot define success in concrete terms upfront, we do not take the engagement.

AI Agents for Business Operations

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Insights

Tutorials

Which Workflows Are Ready for an AI Agent? An Enterprise Readiness Scorecard

Build, Buy, or Partner for an Enterprise AI Agent? A Decision Framework

From Workflow Audit to Production: An Enterprise AI Agent Implementation Roadmap

What Does an Enterprise AI Agent Cost? A TCO and ROI Framework Beyond Token Fees

Enterprise AI Agent Security and Governance: A Practical Control Framework

How to Evaluate and Monitor AI Agents in Production

Integrating AI Agents with CRM, ERP, Help Desk, and Internal Tools

AI Agent vs Chatbot, Copilot, RPA, and Workflow Automation

All

Insights

Tutorials

Which Workflows Are Ready for an AI Agent? An Enterprise Readiness Scorecard

Build, Buy, or Partner for an Enterprise AI Agent? A Decision Framework

From Workflow Audit to Production: An Enterprise AI Agent Implementation Roadmap

What Does an Enterprise AI Agent Cost? A TCO and ROI Framework Beyond Token Fees

Enterprise AI Agent Security and Governance: A Practical Control Framework

How to Evaluate and Monitor AI Agents in Production

Integrating AI Agents with CRM, ERP, Help Desk, and Internal Tools

AI Agent vs Chatbot, Copilot, RPA, and Workflow Automation

All

Insights

Tutorials

Which Workflows Are Ready for an AI Agent? An Enterprise Readiness Scorecard

Build, Buy, or Partner for an Enterprise AI Agent? A Decision Framework

From Workflow Audit to Production: An Enterprise AI Agent Implementation Roadmap

What Does an Enterprise AI Agent Cost? A TCO and ROI Framework Beyond Token Fees

Enterprise AI Agent Security and Governance: A Practical Control Framework

How to Evaluate and Monitor AI Agents in Production

Integrating AI Agents with CRM, ERP, Help Desk, and Internal Tools

AI Agent vs Chatbot, Copilot, RPA, and Workflow Automation