Agentic Workforce that works

We re-use proven solution designs based on the type of business problems, LLM-based AI agents can solve autonomously:

Contract based service business
Insurance, Banking, Telco, Utilities, …

Document based transactional business
Logistics, Transportation, Hospitality & Tourism, Legal and Accounting Services, Investment & Asset Management, …

Catalogue or 'bill of material' based aggregator business
Retail, Consumer Goods, Automotive, Manufacturing, Pharma, …

We leverage common context and knowledge bases across different agents. Thus, we achieve fast implementation, enterprise-grade quality, and sizeable value creation.

Examples of well-suited e2e-processes for agentic automation are

• Customer Relation from acquisition and onboarding to hyper-personalized up/cross-selling
• Offer-to-Contract and Order-to-Cash
• Customer Service
• Purchase-to-Pay
• Corporate Services with HR, IT, Finance, etc.
• Regulatory Control

To make AI work for you and produce P&L impact at scale, a transformation program is required:

1. AI first Business Strategy
2. AI Use Case Portfolio Design & Value Engineering
3. Transformation Planning & Management
4. AI Capability Setup and Development
5. AI Solution Pioneering
6. Scaled AI Solution Implementation
7. Agent Operations & Quality Management
8. Value Control and Optimization

Use Case Context
Customer requests on issues with digital self-service. Knowledge base of 170 thousand Jira tickets with unstructured information.

Use Case Value
Automate request resolution: AI agents provide description of issue resolution and trigger appropriate system actions.

Use Case Design
Customer request as prompt for search in knowledge base: AI agents identify intent, route to appropriate knowledge space, and provide accurate answer with source.

AI Challenge
Large data volume requires several days of ingestion, main knowledge hidden in unstructured comments fields with cryptic lingo, 'needle in a haystack' problem.

AI Solution
Matching agent solution with high-performance ingestion (qwen3 based); programmatic prompt optimization (DSPy) and use-case specific knowledge space curation to address bad data quality.

AI Platform
EggAI multi-agent platform with composable open source stack on Kubernetes, deployed on Azure.

Go-To-Production
Four two-week sprints incl. platform setup and user testing.

Value Generation Path
Re-use and customization of solution for 18 additional use cases and knowledge spaces, integration of legacy system calls as agent skills for autonomous service provisioning, orchestration of use cases for end-to-end process automation.