Management & leadership
Executive assistants that summarise meetings, prepare decision papers, consolidate reports from different sources and keep track of ongoing projects. Executives gain time for decisions instead of preparatory work.
AI Assistant Development
AI Assistant Development
AI assistant development means building digital copilots that complete tasks for your team: creating documents, pulling data from your systems, coordinating appointments and summarising knowledge. Context Studios, an AI-native development studio in Berlin, develops such assistants with context memory, role-based permissions and connections via MCP.
Beyond chat: active supportSystem access via MCPPersonalised per userLearns over time
- Workshop
- Setup
- Sprint
- Build & Support
Fixed price after scoping · proposal within 48 h
Last updated:
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AI technology
AI Assistant Development
An AI assistant is a digital copilot based on large language models that actively carries out tasks instead of only answering questions. It accesses systems such as email, calendar or CRM with the user's permissions, remembers context across sessions and suggests next steps.
- Specialisation
- Personal AI assistants, task automation, proactive support
- Technologies
- Claude, GPT, MCP, LangGraph, Convex, Vercel AI SDK
- Target group
- Knowledge workers, managers, business units, executive assistants
- Project duration
- Goal: about 4–10 weeks depending on complexity and depth of integration
- Compliance
- GDPR, per-user data isolation, audit trail, EU AI Act
What is an AI assistant?Chatbot developmentAI agent developmentAI voice assistantConversational AI
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What distinguishes AI assistants from chatbots?
Six capabilities for active task support
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Actively completing tasks
The assistant carries out tasks — not just conversations. It creates documents, fills in forms, updates databases, sends emails and coordinates between systems. Like an experienced personal assistant who takes real work off your hands.
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Context memory across sessions
It remembers earlier conversations, your preferences, ongoing projects and open tasks. No need to explain the context again — the assistant knows where you stand and picks up right away.
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Personalisation per user
Every user gets an individually configured assistant: adapted to role, department and communication preferences. A marketing assistant speaks differently from a finance assistant — and has access to different tools.
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Proactive suggestions
Instead of just waiting for requests, it acts proactively: it reminds you of deadlines, suggests next steps and prepares the basis for decisions. That is the difference between a reactive tool and a real assistant.
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Deep system integration via MCP
Via the Model Context Protocol we connect the assistant to CRM, project management, email, calendar and industry-specific software. It works with your actual data and processes rather than general knowledge.
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Intelligent summaries
Long documents, email threads, meeting notes and data reports are condensed to the essentials – with key statements and recommended actions. You save reading time and keep important information in view.
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How is your AI assistant built?
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Consultation
Free 30-minute initial call via video. We get to know your workflows, identify tasks for the assistant and give you a first assessment of feasibility and timeline.
Day 1 - (02)
Proposal & planning
You receive a written proposal with scope, integrations, role model, timeline and fixed price.
Days 2–3 - (03)
AI-accelerated development
Agile development with weekly demos. Goal: a working assistant for a pilot group in about 4 weeks, with production-ready code and automated tests.
Weeks 1–4 - (04)
Launch & support
Rollout with complete documentation, training for the pilot group and 30 days of free bug fixing from final delivery. Maintenance and further development by agreement.
Week 4+
Frequently asked questions about developing AI assistants
(01)What is the difference between an AI assistant and an AI chatbot?
A chatbot mainly answers questions in a dialogue. An assistant goes further: it actively carries out tasks, accesses your systems, creates documents, analyses data and makes proactive suggestions. It also has a persistent memory – it knows your preferences across sessions and gradually gets better with your feedback.
(02)Can every employee have their own AI assistant?
Yes, our architecture is designed for personalised assistant instances. Every employee gets an individually configured assistant with its own memory, role-specific tools and personal settings. The core logic and security rules are maintained centrally, while each user's configuration is independent and kept separate from other instances.
(03)How does the assistant learn how I work?
Through three mechanisms: first, explicit configuration during setup with role, tasks and preferences; second, implicit learning from interactions such as frequent requests, corrections and feedback; third, system context from connections to calendar, email and projects. As a rule, the assistant understands your way of working much better after a few weeks of use.
(04)Which tasks can the assistant take on?
Typical tasks include meeting summaries, email drafts, data analyses, reports, scheduling, research assignments, document creation, CRM updates, project status reports and decision papers. In principle, any task that processes digital information and does not strictly require human judgement is suitable. For critical actions, a person confirms before the assistant executes.
(05)How is data security ensured?
Every assistant has isolated access: it only sees the data the respective user is authorised to see. Conversation history and memory data are encrypted and separated per user, with no cross-visibility between instances. All access is logged and auditable. Hosting in the EU or in your own infrastructure is possible.
(06)What do AI assistants cost per employee?
Running costs depend on the model, usage volume and integrations; API costs depend on the model and volume. With model routing and caching we keep them predictable. We measure the benefit together based on the working time saved in the pilot group. We quote development per project: fixed price after scoping, proposal within 48 hours.
(07)Can the assistant also be controlled by voice?
Yes, we can integrate voice input and output. The assistant understands spoken instructions via speech-to-text, for example with Whisper, and can read answers aloud via text-to-speech. This is especially practical on the go, in the car or in meetings. We process voice data under the same data protection rules as text input.
(08)How long does it take to introduce an AI assistant?
Goal: technical development takes about 4–10 weeks depending on the depth of integration. This is followed by an introduction phase with a pilot group in which we refine prompts, tools and permissions based on real usage – typically two to four weeks. The assistant can usually take on its first tasks during the pilot phase.
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Assistant technology stack
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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
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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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AI assistants for different areas
Sales
Sales assistants take over conversation preparation: summarising customer history, suggesting suitable products, drafting follow-up emails and updating CRM entries – with no extra effort for the sales team.
Legal & compliance
Legal assistants for contract analysis, legal research and compliance checks. The assistant knows your contract templates, standard clauses and industry-specific regulations — and prepares initial assessments for your lawyers.
Product management
Product assistants analyse user feedback, categorise feature requests, summarise competitor updates and draft release notes. All information sources consolidated, with recommended actions included.
Research & development
Research assistants search specialist literature systematically, summarise relevant papers, create experiment documentation and keep the state of the art up to date. Gathering information becomes much faster than by hand.
Human resources
HR assistants for applicant management, employee requests and onboarding support. Frequent HR questions are answered automatically, contract templates prepared and new employees guided through their first weeks — personalised by department.
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Example projects for AI assistants
Examples we can build for you
Management
Executive copilot for management
An assistant that prepares appointments, summarises meeting notes, tracks open items and creates decision papers from CRM and project tools.
Meeting preparation · Task tracking · Calendar and email integration
Sales
Sales assistant with CRM integration
A copilot that summarises the history before customer meetings, drafts follow-ups and prepares the CRM entries after the conversation.
Meeting preparation · Follow-up drafts · CRM maintenance
Human resources
HR assistant for employee questions
An assistant that answers frequent questions about leave, policies and onboarding from your documents and hands complex cases over to the HR team.
Source-based answers · Handover to HR · Role-based permissions
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AI assistants — consultation in Berlin
- Founder AI-native since
- 2024
- Phone
- +49 30 96610664
- info [at] contextstudios [dot] ai
Related pages
Your personal assistant is waiting
In a free 30-minute call directly with the founder, we show you which assistant offers the biggest lever for your team.
- 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