Which model do you use?

Depends on requirements. We deploy on Claude, GPT-4 class models, or open-weight Mistral / Llama for data-sovereignty-strict customers. The model choice is part of discovery, not a default.

Stores with a structured knowledge base typically see 70 to 85 percent of customer queries resolved without human escalation. The remaining queries escalate to human agents with full conversation context attached.

Will it replace our human team?

No, it makes them more effective. Routine queries deflect to the bot; complex queries reach humans faster with context. Most stores see a 40 to 60 percent reduction in routine ticket volume.

What about hallucinations?

We use retrieval-augmented generation (RAG) so responses are grounded in your knowledge base, not the model parameters. Responses cite the source document. Confidence thresholds escalate uncertain queries to humans automatically.

Can we update the knowledge base ourselves?

Yes. The bot pulls from a structured source (Notion, Confluence, your own CMS) that your team maintains. Changes propagate to the bot within minutes.

AI Automation for Ecommerce

KI-Kundenservice-Chatbot

KI-Kundenservice-Chatbot. Auf Ihrer Wissensdatenbank trainiert, eskaliert an menschliche Agenten, Multi-Channel-Bereitstellung.

Timeline: 4 to 8 weeks
Training: Your knowledge base
Channels: Web + WhatsApp + email
Handoff: Intelligent escalation

Overview

What it is.

KI-Kundenservice-Chatbots, die in E-Commerce-Shops eingesetzt werden. Auf Ihrer Wissensdatenbank, FAQ, Rückgabebedingungen und Produktkatalog trainiert. Eskaliert intelligent an menschliche Agenten, wenn das Vertrauen niedrig ist. Multi-Channel: Website-Widget, WhatsApp, Messenger, E-Mail. Die meisten Bereitstellungen dauern je nach Größe der Wissensdatenbank und Anzahl der Kanäle 4 bis 8 Wochen. Wir messen die Abwehrquote, die Kundenzufriedenheit und die Qualität der Übergabe an Menschen.

What you get

Deliverables.

AI chatbot trained on your knowledge base and product catalogue
Web widget for your storefront
WhatsApp and Messenger channel integration
Email response automation
Intelligent escalation to human agents based on confidence and topic
Conversation analytics and quality reporting
Knowledge base maintenance workflow for your team
Two weeks post-launch tuning

Honest limits

What it isn't.

Chatbot quality depends on knowledge base quality; we surface gaps before launch
Some markets require disclosure that the agent is AI; we configure compliance
Voice support is a separate engagement

Not sure if it's a fit? Email us. We'll tell you straight.

How it works

From inquiry to handover.

01
1. Discovery
Audit your existing knowledge base. Map customer-service topics. Confirm channels.
02
2. Foundation
Knowledge base ingested. Model fine-tuned or RAG configured. Escalation rules drafted.
03
3. Build
Channels wired up. Conversation flows built. Internal team test.
04
4. Soft launch + tuning
Limited customer rollout. Two weeks of tuning based on real conversations. Full rollout.

Frequently asked

Questions we get from people about to book.

Which model do you use?: Depends on requirements. We deploy on Claude, GPT-4 class models, or open-weight Mistral / Llama for data-sovereignty-strict customers. The model choice is part of discovery, not a default.
How accurate is it?: Stores with a structured knowledge base typically see 70 to 85 percent of customer queries resolved without human escalation. The remaining queries escalate to human agents with full conversation context attached.
Will it replace our human team?: No, it makes them more effective. Routine queries deflect to the bot; complex queries reach humans faster with context. Most stores see a 40 to 60 percent reduction in routine ticket volume.
What about hallucinations?: We use retrieval-augmented generation (RAG) so responses are grounded in your knowledge base, not the model parameters. Responses cite the source document. Confidence thresholds escalate uncertain queries to humans automatically.
Can we update the knowledge base ourselves?: Yes. The bot pulls from a structured source (Notion, Confluence, your own CMS) that your team maintains. Changes propagate to the bot within minutes.

Ready to start?

Tell us about your project.

A member of our team reads every message. Replies within one business day. No sales call required.

See all services

AI Automation for Ecommerce

KI-Kundenservice-Chatbot

KI-Kundenservice-Chatbot. Auf Ihrer Wissensdatenbank trainiert, eskaliert an menschliche Agenten, Multi-Channel-Bereitstellung.

Timeline: 4 to 8 weeks
Training: Your knowledge base
Channels: Web + WhatsApp + email
Handoff: Intelligent escalation

Overview

What it is.

What you get

Deliverables.

AI chatbot trained on your knowledge base and product catalogue
Web widget for your storefront
WhatsApp and Messenger channel integration
Email response automation
Intelligent escalation to human agents based on confidence and topic
Conversation analytics and quality reporting
Knowledge base maintenance workflow for your team
Two weeks post-launch tuning

Honest limits

What it isn't.

Chatbot quality depends on knowledge base quality; we surface gaps before launch
Some markets require disclosure that the agent is AI; we configure compliance
Voice support is a separate engagement

Not sure if it's a fit? Email us. We'll tell you straight.

How it works

From inquiry to handover.

01
1. Discovery
Audit your existing knowledge base. Map customer-service topics. Confirm channels.
02
2. Foundation
Knowledge base ingested. Model fine-tuned or RAG configured. Escalation rules drafted.
03
3. Build
Channels wired up. Conversation flows built. Internal team test.
04
4. Soft launch + tuning
Limited customer rollout. Two weeks of tuning based on real conversations. Full rollout.

Frequently asked

Questions we get from people about to book.

Which model do you use?: Depends on requirements. We deploy on Claude, GPT-4 class models, or open-weight Mistral / Llama for data-sovereignty-strict customers. The model choice is part of discovery, not a default.
How accurate is it?: Stores with a structured knowledge base typically see 70 to 85 percent of customer queries resolved without human escalation. The remaining queries escalate to human agents with full conversation context attached.
Will it replace our human team?: No, it makes them more effective. Routine queries deflect to the bot; complex queries reach humans faster with context. Most stores see a 40 to 60 percent reduction in routine ticket volume.
What about hallucinations?: We use retrieval-augmented generation (RAG) so responses are grounded in your knowledge base, not the model parameters. Responses cite the source document. Confidence thresholds escalate uncertain queries to humans automatically.
Can we update the knowledge base ourselves?: Yes. The bot pulls from a structured source (Notion, Confluence, your own CMS) that your team maintains. Changes propagate to the bot within minutes.

Ready to start?

Tell us about your project.

A member of our team reads every message. Replies within one business day. No sales call required.

See all services

KI-Kundenservice-Chatbot

What it is.

Deliverables.

What it isn't.

From inquiry to handover.

1. Discovery

2. Foundation

3. Build

4. Soft launch + tuning

Questions we get from people about to book.

Tell us about your project.

KI-Kundenservice-Chatbot

What it is.

Deliverables.

What it isn't.

From inquiry to handover.

1. Discovery

2. Foundation

3. Build

4. Soft launch + tuning

Questions we get from people about to book.

Tell us about your project.