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Automate processes

AI Workflows & Integration. AI sits exactly where it actually takes work off your hands.

Proposal within 48 h How we work on it
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Reviewed: September 2026

AI Workflows & Integration
AI Workflows & Integration
AI Workflows & Integration

AI Workflows & Integration: Integration of AI models into your business processes.

Artificial intelligence transforms how companies work. We integrate AI models (Claude, GPT, Gemini) with guardrails, evaluations, and observability into your workflows. From RAG pipelines through content generation to intelligent assistants – with prompt versioning, hallucination protection, and strict cost control. GDPR-compliant data processing and audit logging are integrated from the start.

Who it's for

Ideal for companies that want to build AI into existing workflows, where it takes work off their hands. Particularly valuable for marketing, content, and operations teams that want to increase their productivity through AI support – without compromising on quality, security, and costs.

Key Features

  • Intelligent
  • Continuously improving
  • Cost-effective solution
  • Fully customized
  • Protected with Guardrails
  • Observable & Traceable
  • Compliance ready
  • Claude
  • LangChain
  • LangSmith
  • GPT
  • Gemini
  • Python
  • Pinecone
  • Ragas

We select the optimal tech stack for your specific requirements

How we work on it

  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support
(01)

Setup

We set up one clearly bounded system and hand it over ready to use.

1–2 weeks · fixed price after scoping
(02)

Build & Support

We build the project out and stay alongside you once it is live.

after scoping, ongoing · fixed price after scoping; support billed monthly

Included

(01)

Selection of a clearly defined use case with a measurable goal

(02)

Integration of a suitable model (e.g. Claude, GPT or Gemini) into your workflow

(03)

Guardrails and validation rules against faulty outputs

(04)

Evaluation with your own test cases before go-live

(05)

Prompt versioning, cost limits and audit logging

(06)

Handover with documentation

(07)

30 days of free bug fixing from final delivery

Not included

(01)

Usage costs of model providers (contract directly with the provider)

(02)

Training or fine-tuning your own models

(03)

Further use cases beyond the agreed scope (separately after scoping)

(04)

Ongoing support after the 30 days of bug fixing – available as Build & Support

How it runs

(01)

Use case & test cases

We define the use case, data sources and success criteria and collect test cases from your day-to-day work.

W1
(02)

Implementation

Connect the model, build prompts and guardrails, evaluate against the test cases.

W1-2
(03)

Go-live & handover

Cost limits, logging, onboarding your team and documentation.

W2
(04)

Expansion

Optional: further use cases, RAG pipelines or assistants with ongoing evaluation, scope after scoping.

Build
(01)

CRM System

Related Use Cases
(02)

Marketing Automation

Related Use Cases
(03)

Customer Support

Related Use Cases
(04)

Crafts & Construction

Relevant Industries
(05)

Healthcare & Care

Relevant Industries
(06)

Gastronomy & Hospitality

Relevant Industries

In your city

AI Workflows & Integration in 44 cities: city pages with local use cases and industries.

All locations

(01)

AI workflow automation

Topic page
(02)

AI automation for business processes

Topic page
(03)

AI agent development

Topic page
(01)How do you handle hallucinations and faulty outputs?
We implement multi-layered guardrails: Content filters for unwanted content, PII detection to protect personal data, output validation against defined schemas, and automated evaluations with Ragas against ground-truth datasets. Critical flows additionally receive human-in-the-loop checkpoints.
(02)How are AI costs controlled?
Through multiple measures: Token budgets per user/department, intelligent provider routing (cheaper models for simple tasks), response caching for recurring queries, adaptive truncation for long inputs, and batch inference for non-time-critical tasks. You receive cost dashboards with alerting on budget overruns.
(03)How is GDPR compliance ensured?
All flows go through PII detection before the API call. Sensitive data is masked or processed in local models. We use GDPR-compliant providers with audit logs for all LLM calls and create documentation for your data protection impact assessment.
(04)How is AI output quality measured?
With systematic evaluations: We create ground-truth datasets for your use cases, run automated evals with Ragas/LangSmith, and track metrics like faithfulness, response relevancy, and context precision. For RAG systems, we additionally measure retrieval quality.
(05)How do updates and prompt changes work in production?
Prompts are versioned and deployed via CI/CD. Changes go through automated evals against baseline datasets. New versions are rolled out via canary deployment and automatically rolled back on quality issues. Feature flags enable A/B tests of different prompt variants.
View all frequently asked questions

Ready for your project?

Talk to us for 30 minutes with no obligation, or write to us directly.

Book a call Proposal within 48 h

Fixed price after scoping · proposal within 48 h