Discovery before delivery
First validate value, feasibility and assumptions. Then build reliability, security, integration and operations for production.
AI implementation
AI implementation from a validated idea to a running system.
The Implementation Sprint builds a clearly scoped first version, tests it with realistic cases and documents operations, limits and responsibilities.
The real task
Treat discovery and delivery as different operating modes, and make the solution only as complex as its value and risk justify.
Augmentation before autonomy
A human in the loop can be more economical than an expensive attempt to fully automate rare cases from day one.
Monitor strategic value too
Alongside technical metrics, regularly review usage, ongoing costs and actual business value.
What you receive
AI Implementation Sprint
Build, test, hand over and launch a simple AI solution that is already understood, within a clearly scoped sprint.
Working Solution
For example, a simple Q&A assistant, a knowledge solution or a clearly defined workflow with AI steps.
Knowledge & Instructions
Structure the knowledge base, prompts, rules and system boundaries so that quality can be improved transparently.
Lightweight Evaluation
Define relevant test cases, expected errors, human approvals and initial quality criteria.
Handover & Launch Notes
Clearly document responsibilities, known limitations, usage guidance and the next steps for expansion.
Case Studies
Measurable workload reduction in daily operations.
B2B event organizer · Production system
> 1,000 hoursFirst-level support time saved across more than 12,000 chats and 50,000 answered messages at 16 events.
Choose the AI Implementation Sprint when
- The problem, users and desired outcome are already clear
- The first version can be deliberately small and complete
- An assistant, chatbot or workflow does not require a large platform
- An accountable owner and real test users are available
What you will have afterward
- A first working version for the defined use case
- A limited scope that can be tested completely
- A simple, maintainable solution without an unnecessary platform
- Real test results and a clear handover to the owner
Our methodology
Proven frameworks for reliable AI systems in production.
Discovery vs. Delivery
Separates validation of value and feasibility from building reliable production systems.
80% Fallacy
Reveals the rare cases that determine effort and operational risk.
Five Scaling Goals
Assesses scaling across quality, cost, speed, reach and control.
S-O-T Monitoring
Connects system quality, operational usage and strategic value in production.
Value Threshold + Cost Cap
Defines minimum value and maximum cost as clear criteria for stopping or expanding.
Augmentation before Automation
Secures early value through human control before expanding autonomy.
Frequently asked questions
Quick answers.
Which solutions are suitable?
Simple, clearly scoped assistants, knowledge solutions and workflows. Complex platform development or large integration programs are handed over to partners.
Is custom code necessary?
The decision follows requirements for quality, volume, integration, maintainability and cost. Depending on the context, a controlled no-code workflow or a custom implementation may be appropriate.
What happens after launch?
You receive a clear handover. For small systems we have built, an optional Managed AI System engagement can be arranged.
Get in touch
Build the smallest complete version you can learn from.
Tell us the target users, desired workflow, existing data sources and most important success metric. We will tell you whether the solution is sufficiently scoped.