Telecommunications
Intelligent dialogues for support, orders and complaints — they automate a large share of customer service requests.
AI dialogue systems
Conversational AI Development
Conversational AI development means building dialogue systems that understand and resolve requests in natural language – in chat, on the phone or in messengers. Context Studios, an AI-native development studio in Berlin, builds chatbots and voicebots that use your company knowledge, operate your systems and hand over cleanly to your team when needed.
Natural language understandingMultilingualAutomated first responsesAvailable 24/7
- Workshop
- Setup
- Sprint
- Build & Support
Fixed price after scoping · proposal within 48 h
Last updated:
(01)
What is conversational AI?
AI technology
Conversational AI refers to AI systems that hold natural-language dialogues in text and speech. Unlike rule-based chatbots, they recognise intent, context and nuance, draw on company knowledge via RAG and complete tasks through connected systems – such as bookings, status checks or handing a conversation over to your staff.
- Specialisation
- Chatbots, voicebots, dialogue management, NLU/NLG
- Technologies
- GPT, Claude, Rasa, Dialogflow, Voiceflow
- Target group
- Companies with high customer contact volumes
- Project duration
- Goal: first MVP in about 4–6 weeks, omnichannel systems typically 3–5 months
- Compliance
- GDPR, accessibility (WCAG 2.1), German Telecommunications Act
Chatbot developmentAI voice assistantAI assistant developmentAI for customer serviceLLM development
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What do our dialogue systems deliver?
From intent recognition to omnichannel deployment — dialogue systems at enterprise level
(01)
LLM-based dialogue management
Natural language understanding based on modern LLMs: our systems capture intent, context and nuance for human-like dialogues.
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Context memory
Voicebots with context memory: the dialogue remembers earlier statements and preferences for coherent long-running conversations.
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Omnichannel integration
Multi-channel deployment across website, app, WhatsApp, phone and more — with a consistent experience on every channel.
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Knowledge access via RAG
A RAG architecture connects dialogues with your company knowledge for fact-based answers backed by sources.
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Sentiment analysis & escalation
Sentiment analysis detects customer mood and triggers automatic escalation when needed — for empathetic, situation-appropriate responses.
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Voicebot development
Voice integration with speech-to-text and text-to-speech for telephony and voice assistants — natural spoken dialogues included.
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How is an AI dialogue system built?
- (01)
Consultation
Free 30-minute initial call via video. We get to know your customer communication, identify suitable dialogues and give you a first assessment of feasibility and timeline.
Day 1 - (02)
Proposal & planning
Detailed breakdown of dialogues and integrations, a written proposal with scope, timeline and fixed price, plus a technical architecture plan.
Days 2–3 - (03)
AI-accelerated development
Agile development with weekly demos. Goal: a working MVP in about 4 weeks, with production-ready code, test dialogues and automated tests.
Weeks 1–4 - (04)
Launch & support
Production deployment with complete documentation and 30 days of free bug fixing from final delivery. Maintenance and further development by agreement.
Week 4+
Frequently asked questions about AI dialogue systems
(01)What distinguishes conversational AI from a classic chatbot?
Classic chatbots follow rigid rule trees: as soon as a question strays from the intended path, they get stuck. Modern dialogue systems use language models for genuine language understanding. They recognise intent and context, ask follow-up questions, draw on your company knowledge and hold natural conversations without predefined dialogue paths – even when requests are phrased in unusual ways.
(02)Which channels can a dialogue system serve?
We deploy dialogue systems on websites, apps, WhatsApp, email and internal tools such as Slack or Microsoft Teams. Voice channels are covered too: telephony, voice assistants and in-app voice with speech-to-text and text-to-speech. The logic sits in one central place, so knowledge, tone and rules stay the same on every channel and you maintain changes in only one spot.
(03)What is a typical automation rate?
That depends heavily on the use case and the quality of the knowledge base. Standard requests such as order status, opening hours or contract data can usually be automated well, advice-heavy cases less so. That is why we agree on a realistic target together, measure the automation rate in live operation from day one and extend the system step by step where it pays off.
(04)Can a dialogue system also carry out transactions?
Yes. Via APIs we connect the dialogue system to your CRM, helpdesk, ERP or booking systems. It can then create appointments, check orders, open tickets or update address data, for example. We secure critical actions with permissions, confirmation steps and logging, so every transaction remains traceable and nothing happens without approval.
(05)How long does it take to develop a dialogue system?
The goal is a first MVP for one channel in about 4–6 weeks, so you can test early with real conversations. Complete systems with voice, several channels and complex integrations usually take three to five months. We set the exact timeline after scoping, depending on your knowledge base, interfaces and approval processes.
(06)How is conversation quality monitored and improved?
We measure satisfaction, resolution rate, escalation rate and abandoned conversations, and we review dialogues regularly. Conspicuous conversations land in a review view where your team can correct answers. These corrections feed into the knowledge base, prompts and test cases, so the dialogue system improves based on data without you having to retrain any models.
(07)Can existing FAQ content be used as a knowledge base?
Yes. Existing FAQs, manuals, product data and helpdesk articles can be used directly as a knowledge base. We index them via RAG so the system bases its answers on your content and can cite the source. When you update a document, the change flows into the answers automatically – with no new training and no manual maintenance inside the bot.
(08)Can an AI dialogue system be used in a privacy-compliant way?
Yes. We rely on GDPR-compliant architectures with hosting in the EU, data encryption, role-based permissions and clear deletion periods for conversation data. Personal data can be masked before the language model processes it. On request, we run open-source models in your own infrastructure so that no conversation content is sent to external providers.
(09)How does the handover to human agents work?
The dialogue system uses rules, sentiment and uncertainty to recognise when human help is needed. It then hands the conversation over to a member of your staff, together with the history, the identified concern and any data already collected – for example in the helpdesk or via a callback. Your customers do not have to repeat anything, and your team picks up at exactly the right point.
(10)What does a dialogue system cost to run?
Running costs depend on conversation volume, channels and hosting; API costs depend on the model and volume. With model routing and caching we keep them predictable. We quote development with omnichannel, voice and integrations per project: fixed price after scoping, proposal within 48 hours. A fixed-price workshop is a good way to get started.
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Technology stack
(01)
AI & ML
Anthropic ClaudeOpenAI GPTGoogle GeminiOpen-Source LLMs (Llama, Qwen, DeepSeek, Mistral)ConvexRAG & Vector DBs (Pinecone, Weaviate)MCP (Model Context Protocol)Hugging Face TransformersComputer Vision (YOLO, SAM)ElevenLabs (Voice AI)Google Veo (Video AI)
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Web & Mobile
Next.js 16 & React 19TypeScriptReact Native & ExpoTailwind CSS v4Shadcn/uiVercel Edge Runtime
(03)
Backend & Data
Node.js & Hono (Edge)PythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI 3.1
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DevOps & Infrastructure
Vercel & AWSDocker & KubernetesCI/CD-Pipelines (GitHub Actions)OpenTelemetry & GrafanaLangfuse (LLM Monitoring)
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Dialogue systems by industry
Banking
Advisory dialogues for banking, insurance and investment — in line with regulatory requirements.
Healthcare
Patient dialogues for appointment booking, symptom checks and aftercare — noticeably relieving medical staff.
Human resources
Candidate dialogues, onboarding assistance and employee service for more efficient people operations.
Retail
Purchase advice, product search and after-sales service through dialogue — boosting conversions in e-commerce.
Tourism & hospitality
Booking assistance, travel information and real-time support — a 24/7 dialogue service for the travel industry.
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Example projects
Examples we can build for you
Customer service
AI-powered support agent
A natural-language support agent connected to a knowledge base that answers customer dialogues with context awareness and handles requests automatically.
Automated first response · Multilingual · Available 24/7
Knowledge management
RAG-based document system
A dialogue-based search across thousands of documents via RAG that noticeably speeds up finding information.
Source-based answers · Fast search · Scalable
Process automation
Workflow automation with AI agents
AI dialogues that collect information and steer processes — for end-to-end workflow coordination with less manual handling time.
End-to-end automated · Fewer errors · Time savings
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AI dialogue systems — consultation in Berlin
- Founder AI-native since
- 2024
- Phone
- +49 30 96610664
- info [at] contextstudios [dot] ai
Related pages
Dialogue systems for your company
Create natural-language dialogue experiences for your customers. Discuss your use case in a 30-minute call directly with the founder.
- First call: 30 minutes by video
- Directly with the founder, Michael Kerkhoff
- Free of charge, no obligation
Context Studios · Kaiser-Friedrich-Str. 6 · 10585 Berlin