Conversational AI Developer.

Builds voice and chat assistants that resolve enquiries rather than frustrate users.

Remote & on-site DACH-wide
Conversational AI Developer — enquire

A Conversational AI Developer builds dialogue systems for customers and employees: chat assistants with an LLM core, voice bots for telephony, self-service journeys with backend integration. Modern systems combine generative flexibility with controlled processes — phrasing freely where it helps, guiding strictly where it counts.

The generational shift is real: classic intent bots with rigid decision trees frustrate users, while pure LLM chats hallucinate commitments. The skill lies in the hybrid architecture — and in the humility to let the bot stop where the human is better.

01

Customer service assistant

An assistant resolves standard enquiries end-to-end — order status, address changes, appointment booking — and hands over complex cases to employees with context.

02

Voice bot for the hotline

Telephone pre-qualification and routine processes using natural language — including a clean handover and conversation summary for the team.

03

Internal employee assistant

HR questions, IT enquiries, policy information in Teams or Slack — with RAG integration to internal sources and respect for access rights.

04

Modernise a legacy bot

An existing intent bot is migrated to an LLM hybrid architecture — better resolution rate, less maintenance effort, measurably compared.

Core competencies
Dialogue designLLM integration & guardrailsBackend/API integrationEscalation designConversational analytics
Tools & frameworks
Voiceflow/Botpress/RasaTelephony stacks (SIP, STT/TTS)Teams/Slack appsRAG integrationContact centre platforms
Plus factors
UX writingMultilingualismAccessibility
How do you prevent the assistant from inventing binding commitments?
Hallucinated discounts or contract information are a business risk — the answer requires architecture (controlled answer sources), not just prompts.
What resolution rate did your last bot achieve — and how was it measured?
Containment measured honestly (resolved vs. abandoned) separates professionals from demo builders.
When do you hand over to a human — and what does the employee receive?
Escalation design determines customer satisfaction: context handover instead of ‘please explain everything again’.
How do you test a dialogue before go-live?
Real user phrasings, adversarial tests, regression suites — conversational AI requires the same testing discipline as software.
What does a Conversational AI Developer cost as a freelancer?
According to our market observation, hourly rates in the DACH region in 2026 range between 95 and 145 euros — voice/telephony experience sits at the upper end.
What sets modern assistants apart from the chatbots of recent years?
Classic bots only understood predefined phrasings and led users through rigid menus. Modern systems understand free-form language, access knowledge and backends in a controlled manner — and thereby genuinely resolve far more enquiries.
Will an AI assistant replace our service team?
No — it takes on routine tasks and frees the team for complex cases. Realistically, good assistants resolve a substantial share of standard enquiries on their own; the rest is handed over to people with better context.
How long does it take to introduce a customer service assistant?
A focused assistant for the most common enquiries can be piloted in six to ten weeks — what matters are clean backend interfaces and realistic test data from genuine customer requests.

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