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Automation with AI

AI Automation: Hand Routine Work to AI Agents

AI automation takes over recurring tasks that overwhelm classic rules: understanding emails, reading documents, triaging requests and moving data between your systems. Context Studios, an AI-native development studio in Berlin, builds these AI agents and workflows, connects them to your existing tools and lets your team decide on uncertain cases.

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Steel boat lift seen from below under an overcast sky, the trough's lifting gate painted teal – a symbol of processes that run reliably on their ownAI-generated image
Less routine work for your teamn8n, Make, custom AI agentsGoal: pilot process in approx. 2–3 weeksGDPR-compliant, on-premise possible
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Last updated:

(01)

What is AI automation and how does it differ from RPA?

AI service

AI automation is the use of artificial intelligence to automate business processes that go beyond the rigid if-then rules of classic RPA. AI-powered systems also understand unstructured content such as emails, free-text documents and images, make reasoned decisions and hand uncertain cases over to people.

Specialisation
Intelligent process automation with LLMs and AI agents
Technologies
n8n, Make, LangGraph, Claude, GPT, Temporal, Apache Airflow
Target group
Mid-sized companies and enterprises with many recurring processes
Project duration
Typically 4–12 weeks; goal for the pilot process: approx. 2–3 weeks
Compliance
GDPR-compliant, audit logging in line with German GoBD rules, on-premise possible

AI Workflow AutomationAI Document ProcessingAI Chatbot DevelopmentAI Agent DevelopmentAI Integration

(02)

Which processes do we automate with AI?

From simple workflows to multi-step decision processes: we automate what slows your teams down.

(01)

Workflow automation

End-to-end automation of multi-step business processes: from data capture through checks and decisions to hand-over to the target system, orchestrated and logged in a traceable way.

(02)

Document processing

Automatic extraction, classification and processing of invoices, contracts and forms, with plausibility checks and human approval when the system is uncertain.

(03)

Decision automation

AI supports decisions such as pre-checks, risk assessments or quality controls. Clear thresholds define what runs automatically and what a person approves.

(04)

Real-time monitoring

Monitoring of all automated processes with anomaly detection, alerting and audit logs, so errors are noticed before they spread.

(05)

Seamless integration

Connection to SAP, Salesforce, HubSpot, Microsoft 365, Slack and other systems via REST APIs, webhooks or direct database access.

(06)

Measuring impact

Dashboards show what the automation delivers: processed cases, escalation rate, throughput times and the working time saved on paper.

(03)

How does an automation project work?

From process analysis to live operation, in four clearly defined steps.

  1. (01)

    Initial call and process analysis

    Free 30-minute initial call by video. We look at your processes, identify candidates for automation and give you a first assessment of feasibility and benefit.

    30 minutes
  2. (02)

    Proposal and planning

    Detailed breakdown of the automation steps, technical architecture plan, milestones and a written fixed-price proposal.

    after scoping
  3. (03)

    Pilot process and development

    Agile development with weekly demos. Goal: a first pilot process that runs alongside the existing workflow and is validated with real cases.

    Goal: approx. 2–4 weeks
  4. (04)

    Go-live and operation

    Production deployment with complete documentation and 30 days of free bug fixing from final delivery. Maintenance and further development by agreement.

    afterwards
(04)

In which industries does automation with AI pay off?

Financial services & insurance

Credit pre-checks, claims triage, KYC validation, compliance reporting and invoice processing. Automation takes load off standard cases and documents every step for regulatory requirements.

Tax advisory & auditing

Receipt capture, pre-posting according to the German SKR03/04 charts of accounts, automatic client assignment and DATEV integration. AI-powered document processing frees up capacity in tax firms for advice instead of data entry.

E-commerce & retail

Returns management, product categorisation, classification of customer requests, inventory optimisation and dynamic pricing. The automation grows with your order volume.

Healthcare & pharma

Patient intake, pre-processing of medical reports, appointment management and billing. GDPR-compliant AI systems relieve medical staff of administrative tasks; medical decisions stay with professionals.

Logistics & supply chain

Inventory planning, creation of shipping documents, shipment tracking, customs documentation and supplier management. Intelligent automation helps to spot bottlenecks earlier and bundle routine communication.

Law firms

Contract analysis, due diligence support, document classification, deadline management and compliance monitoring. AI agents search large document collections and flag relevant clauses, risks and deviations for review by your lawyers.

Frequently asked questions about AI automation

(01)What is the difference between RPA and AI automation?
RPA works with fixed rules: click here, copy that, paste there. That works for simple, structured processes. Automation with AI goes further: AI agents understand context, process unstructured data such as emails, free text and different document formats, and make reasoned decisions. If the format of an email changes, the system does not break but still recognises what it is about.
(02)Which processes can be automated with AI?
In principle, any process that repeats itself and has clear inputs and outputs. Good candidates are document processing, email management, data extraction, reporting, customer requests, invoice checks and quality controls. In the free initial call we look at your workflows and name the processes where volume, data and benefit fit together best.
(03)Will employees be replaced by AI?
No, they are relieved. Automation takes over routine tasks nobody enjoys: typing up data, sorting emails, reconciling invoices. Your employees gain time for customer relationships, exceptions and improvements. Important decisions remain with people, because the system deliberately escalates uncertain cases to your team.
(04)How secure is my data?
All systems are operated in compliance with the GDPR and, if required, entirely on your own infrastructure. We can use open-weight models so that your data never leaves your servers. Encryption, role-based access control and audit logs are standard. For regulated industries we additionally set up logging that takes the German GoBD requirements into account.
(05)Do I have to replace my existing systems?
No. The AI agents are integrated into your existing infrastructure, such as SAP, Salesforce, HubSpot, email, Slack, Teams or any application with an API. We build bridges, not islands. The connection is made via REST APIs, webhooks or direct database connections, depending on what your system landscape offers and what your IT approves.
(06)What does an automation project with AI cost?
The effort depends on the number of process steps, the systems to be connected, data quality and the requirements for approvals and logging. On top come running costs for model calls and hosting, which depend on volume. That is why we start with a short scoping of the pilot process: fixed price after scoping, proposal within 48 hours.
(07)How long does implementation take?
We always start with a pilot process; the goal is for it to run with real cases after approx. 2–3 weeks. For a single process including testing and optimisation we typically plan 4–6 weeks, for more complex projects with several processes typically 8–12 weeks. We agree the binding schedule together after scoping.
(08)Which AI models do you use for automation?
We are model-agnostic and choose the right model for each task: Claude, for example, for complex text processing and reasoning, GPT for generative tasks, specialised OCR models for document recognition. Orchestration runs via LangGraph or n8n. Where data protection requirements are strict, we use open-weight models such as Llama, Mistral or Qwen that run entirely on your infrastructure.
(09)What happens if the AI makes a mistake?
Every automation system gets human-in-the-loop mechanisms: when confidence is low or a case is unusual, the AI automatically escalates to a responsible person. All decisions are logged and remain traceable. Corrections from your team feed into prompts, rules and test cases, so recurring errors are fixed in a targeted way.
(10)Can our employees control the automation themselves?
Yes. Via an admin dashboard, your employees adjust rules and thresholds, pause processes or add new variants without any programming knowledge. They can see at any time which cases were handled automatically and which were escalated. For changes to the AI logic itself, we are available as a development partner by agreement.
(11)How much effort does the rollout mean for our team?
Manageable. The automation first runs in parallel with the existing process while your team keeps working as usual. Only after a validation phase does the AI take over tasks step by step, each time with approval from your specialists. What we mainly need are contacts from the business side who provide sample cases and review results.
(12)How quickly does the automation pay for itself?
That depends on volume and time spent per case. We calculate it transparently before the project starts: minutes saved per case times cases per month, compared with project and operating costs. A worked example: 500 cases a month with 15 minutes saved each add up to 125 hours per month on paper. We measure the actual savings in the dashboard after go-live.
(05)

What do we automate with?

(01)

AI models

ClaudeGPTGeminiLlamaMistralQwenDeepSeekOCR & Document AI
(02)

Orchestration

n8nMakeLangGraphTemporalApache AirflowMCP (Model Context Protocol)
(03)

Integration

REST & WebhooksSAPSalesforceHubSpotMicrosoft 365SlackDATEV
(04)

Operations

DockerKubernetesOn-PremisePostgreSQLVector DBsMonitoring & Audit-Logs
(06)

Examples of automation projects

Examples we can deliver with you. Stated results are targets.

Customer service

AI-powered support agent

An AI agent for customer service that understands requests in natural language, accesses internal knowledge bases and suggests answers or sends them automatically, around the clock.

Goal: automated first response · Multilingual · Available 24/7
Finance & accounting

Invoice intake with approval workflow

Incoming invoices are read, checked against purchase orders and pre-posted. Deviations go to the responsible person with an explanation; everything else flows into the ERP.

Goal: less manual data entry · Approval for deviations · Audit log per document
Process automation

Email triage for sales

A workflow that classifies incoming emails, extracts key data, creates records in the CRM and routes urgent requests to the right team.

Goal: faster routing · CRM integration · Human-in-the-loop
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Automation with AI from Berlin-Charlottenburg

Founder AI-native since
2024
Address
Kaiser-Friedrich-Str. 6, 10585 Berlin
Email
info [at] contextstudios [dot] ai
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Which of your processes are worth automating?

Free process analysis in a 30-minute call directly with the founder: we name your best candidates for automation and estimate the expected benefit. No obligation.