Discovery
We identify the business problem, target users, data sources, feasibility, compliance and economic logic of the use case.
Definition of Done
- Clear problem statement & target users
- Expected value + feasibility
- Risk view & pilot scope
AI PROJECTS & IMPLEMENTATION
Turn prioritized use casesinto productive solutions.
We design, build and integrate AI solutions that solve real business problems — from AI assistants and knowledge management to document AI, agents and workflow automation. From business case to productive implementation.
Discovery to scaleDefinition of DoneClear architectureMeasurable outcomes
The standard
Not a prototype for a presentation.A productive solution that improves real work.
Workshops and ideas are the beginning. Value emerges when solutions are integrated into daily workflows, connected to systems and adopted by users.
THE SEVEN-STEP PROJECT MODEL
Seven steps. Clearly separated. Each with its own Definition of Done.
AI projects fail when discovery, implementation, rollout and operations are mixed together. Our model separates each phase and defines what must be true before moving on.
DiscoveryStep 01
PilotStep 02
Pilot
We build the tightest solution for the most expensive pain point and make it productive in a controlled environment.
Pilot reviewStep 03
Pilot review
We assess whether the pilot creates enough value to justify rollout, iteration or termination.
RolloutStep 04
Rollout
We integrate the solution into broader workflows, systems and teams.
OperationsStep 05
Operations
We establish the operational setup for stable usage, monitoring and support.
IterationStep 06
Iteration
We improve the solution continuously based on usage, feedback, model changes and new requirements.
ScaleStep 07
Scale
We expand the solution to more users, teams, locations, data sources or related use cases.
THREE PROJECT PHASES
Strategy · Implementation · Maintenance.
Every AI project needs strategic clarity, technical delivery and ongoing improvement — cleanly separated.
01
Strategy & potential
Use case, business value, target process, feasibility, compliance and roadmap are defined. Output: a clear scope with value hypothesis and success criteria.
02
Implementation
Architecture, backend (Python/TypeScript), workflow design, ERP/CRM/doc integration, model & prompt setup, testing & deployment. Output: a working, integrated AI solution.
03
Maintenance & improvement
Monitoring, model updates, performance & cost optimization, incident response, feature improvements. Output: a stable, scalable system.
FIVE USE-CASE CLUSTERS
Five AI solution clusters. One implementation approach.
We bundle related use cases into clusters — each with its own architecture patterns, data requirements and best practices. We build faster, reduce risk and reuse proven components.
AI assistants
AI assistants
Chat and voice assistants for employees, customers or internal teams — role-based prompts, knowledge connection, tool access, audit logging.
Knowledge management
Knowledge management
RAG systems that answer questions from existing company knowledge — with reranking, citation trail and permission-aware access.
Document AI
Document AI
Extraction, classification and normalization from PDFs, emails, scans and forms — with validation, human review and export to ERP/CRM.
Agents
Agents
Multi-step agents with tools, approval gates, fallback logic and audit trail — automating complex knowledge workflows with human control.
Automation
Automation
AI-supported workflows that connect systems and reduce manual handovers — email-to-ERP, ticket classification, OCR pipelines, reporting.
EXAMPLE PROJECTS
Projects we implement.
A selection of typical AI projects — from internal knowledge bases to controlled agent workflows.
A
Internal knowledge assistant
A secure AI assistant that answers employee questions from internal documents, policies and knowledge bases with citations.
B
Document-extraction pipeline
An AI workflow that extracts information from PDFs, emails or scans and prepares it for downstream systems.
C
Sales email assistant
An assistant that helps sales teams draft personalized emails, summarize customer context and prepare follow-ups.
D
HR onboarding assistant
A chatbot that answers new-employee questions from internal guidelines, documents and HR processes.
E
Email-to-ERP automation
A workflow that reads incoming emails, extracts relevant information and prepares structured data for ERP processing.
F
Agentic research workflow
A controlled multi-step agent that gathers information, summarizes findings and delivers structured outputs with approval gates.
DISCUSS YOUR PROJECT
Build your first productive AI use case.
Whether document automation, an internal AI assistant, knowledge management or agentic workflows — we take you from business case to productive solution.
CONTACT
Let's get in touch.
Request a demo, book a workflow audit, or just say hello — we reply within one business day.
Or reach us directly at: info@ai-z-group.com
AI-Z GmbH · Königstraße 26 · 70173 Stuttgart · Germany
+49 157 52105947