Agentic Workforce that works
Deploy teams of specialized AI agents that perform tasks, make decisions, and execute workflows autonomously in collaboration with humans.
– From work augmentation to process automation
– From activity-based to outcome-based team management
– From innovation project to organizational scale
Industry and Process Focus for AI automation
Agentic solutions that work are specific to your industry, processes and your data.
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
Transformation Path
We deeply engage with your business and IT organization to change your way of working and enable your people.
To make AI work for you and produce P&L impact at scale, a transformation program is required:
- AI first Business Strategy
- AI Use Case Portfolio Design & Value Engineering
- Transformation Planning & Management
- AI Capability Setup and Development
- AI Solution Pioneering
- Scaled AI Solution Implementation
- Agent Operations & Quality Management
- Value Control and Optimization
Reference Use Case
Customer Support Agents
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 haystick’ 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
Further steps: 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.
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