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Solutions · 02

AI Agents & Intelligent Automation

Connect AI to the knowledge, systems, decisions, and workflows that shape daily operations, while keeping people in control of important actions.

What problem does this solve?

High-volume work still depends on people reading documents, answering repetitive questions, moving information between systems, and coordinating approvals through email. Traditional automation handles fixed rules but struggles when work includes language, documents, judgment, or exceptions.

Who is it for?

Operations and service leaders reducing repetitive work and cycle time.

Digital and AI teams building production-grade agents.

Knowledge owners improving access to trusted information.

Customer service leaders exploring voice and conversational channels.

Application owners connecting AI with ERP, CRM, Microsoft, and workflow systems.

How Ignatiuz delivers it

1

Identify the process, decisions, data, and exceptions before designing the agent.

2

Prototype the hardest or riskiest step using representative data.

3

Connect the agent to approved knowledge and business systems.

4

Define Human in the Lead checkpoints, fallback paths, and escalation rules.

5

Test accuracy, task completion, security, cost, and user experience.

6

Deploy with monitoring, evaluation, support, and continuous improvement.

Common use cases

Enterprise agents that support multi-step operational work.

Knowledge assistants grounded in approved documents and data.

Voice agents for structured intake, routing, and service scenarios.Docume

Document and process automation for invoices, claims, orders, and requests.

Workflow automation across Microsoft, Salesforce, ERP, and line-of-business systems.

Platforms involved

Microsoft Copilot Studio, Azure AI Foundry, Microsoft 365, SharePoint, Power Platform, and Azure services.

OpenAI or Claude models selected according to the use case and client environment.

Salesforce, Dynamics 365, Business Central, ERP, CRM, APIs, databases, and legacy applications.

IGNA where its product capabilities shorten the route to deployment.

Security & Governance

Human in the Lead

Agents are designed with least-privilege access, approved sources, logging, reviewable actions, escalation rules, and human approval where consequences are material. Testing covers unsafe requests, unsupported answers, data exposure, and failure paths.

Frequently Asked Questions.

What is the difference between an AI agent and a chatbot?
A chatbot mainly answers questions. An enterprise agent can also use approved tools, update systems, support decisions, and move work through defined steps with controls.
Yes, when the applications provide suitable APIs, connectors, files, databases, or other supported integration methods.
Human review is placed where risk, ambiguity, policy, financial impact, or customer consequences require a person to remain in the lead.

Ready to talk about AI Strategy and Readiness?

A readiness workshop is the fastest way to find out if this is the right starting point.

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