AI Services
·FOUR SERVICE BUILDING BLOCKS

AI solutions that fit your processes.

We build AI assistants, automations and data pipelines so they are securely integrable, traceably governable and usable for real business processes.

Custom AI Engineering

Architecture design, AI backends, data pipelines, ERP/CRM integration, custom models.

LLM & Assistants

Model selection, RAG, knowledge assistants, role-based access, prompt architectures.

Process Automation

Document processing, workflow engines, API integrations, decision support.

AI Governance

Access control, monitoring, on-prem options, EU AI Act readiness, audit-ready documentation.

·OUR AI SOLUTIONS

AI solutions for your industry.

From clinical documentation to fraud detection: concrete, ready-to-use AI, GDPR-compliant and hosted in Germany. MiRA is one of our available solutions; we build others for your use case.

MiRA

Available solution
Hospitals & health centers · Medical Intelligence Recording Assistant

Clinical AI documentation. MiRA listens to every doctor-patient conversation and creates a structured medical report in under 60 seconds. Fully on-premises, no audio ever leaves your hospital.

See MiRA in detail →
Law firms

Contract & document AI

Clause extraction, risk scoring and full-text search across contracts and files. Rapid review against template contracts.

Insurers

Claims & document processing

Automatic intake, classification and anomaly or fraud detection in claims.

Public sector

Secure knowledge assistants

RAG assistant with role-based access and source citations, plus GDPR anonymization. Fully auditable.

Industry

Anomaly detection & forecasting

Early warning from sensor and machine data, plus demand and sales forecasts on your own data.

Banks

Compliance & fraud detection

Rule-checking against GDPR, NIS 2 and the EU AI Act, anomaly detection in transactions and document AI.

Other industries

Custom AI solution

Your industry not listed? We build GDPR-compliant AI solutions for your specific use case.

Discuss an AI service →
·AI TEST AUTOMATION

Test AI before it creates risk.

We test ML models, LLMs and data pipelines automatically for quality, security, drift, bias and verifiability, before go-live and in operation.

ONE TEST FRAMEWORK ML Models LLMs Data Foundation

ML Models

Performance, drift, and robustness as a continuous process, not a one-off acceptance test.

  • Performance & generalization
  • Stability
  • Drift detection
  • Overfitting / Underfitting
  • Robustness
  • Versioning
  • Documentation

LLMs

Systematically secure against hallucinations, prompt injection, output inconsistency, and data exposure.

  • Prompt tests
  • Hallucination checks
  • Prompt-Injection
  • Data exposure
  • Access control
  • Output guardrails
  • Logging

Data Foundation

Completeness, bias, and distribution shifts as the basis for any reliable model output.

  • Completeness
  • Outliers & inconsistencies
  • Label quality
  • Distribution shifts
  • Bias risks
  • Data drift
  • Versioning
·SERVICE BUILDING BLOCKS

What AI Test Automation concretely checks.

Six areas where classic software tests aren’t enough, and how we make them measurable.

Data quality

Completeness, consistency, outliers, and faulty labels.

Model Drift

Gradual changes in inputs and model performance in production.

Robustness

Behavior with unusual or slightly modified inputs.

Bias & Fairness

Systematic bias in data and model decisions.

LLM Security

Prompt injection, data leakage, and disallowed output patterns.

Reproducibility & Audit

Comparable model and data states, audit-proof test evidence.

·METHODOLOGY

From data foundation to production.

Structured approach, from risk classification to continuous monitoring in production.

01Scope & Risk

Use case, model type, data sources, risk class, test goals.

02Test design

Test cases, metrics, thresholds, adversarial scenarios.

03Validation

Run ML, LLM, data, and pipeline tests automatically.

04Monitoring

Drift, output behavior, performance, and anomalies in production.

05Reporting

Technical results, management summary, and audit evidence.

06Improvement

Feed findings back into data, prompts, guardrails, or architecture.

·QUALITY STANDARD

What good AI validation must deliver.

Five criteria for AI tests that hold up in practice.

01

Measurable

Model behavior assessed through defined metrics and test sets.

02

Reproducible

Data states, prompts, and model versions documented comparably.

03

Robust

Tested even under modified, unusual, or critical inputs.

04

Secure

LLM risks such as prompt injection and data exposure tested.

05

Provable

Connectable to governance, risk, and compliance processes.

Configure this solution →
USE CASES

Situations where we support you

AI Services
AI Services

Roll out internal AI assistants safely

Problem
Employees should work faster with internal knowledge without sensitive data flowing into external systems uncontrolled.
Approach
We design AI assistants with clear data spaces, roles, permissions, integration points, and governance.
Benefit
More productivity through AI, with better control over data, access, and results.
AI Services
AI Services
AI Services

Integrate LLMs and AI into existing IT processes

Problem
AI projects often stall as pilots because integration, data quality, process connection, and security requirements don’t align.
Approach
We support with LLM integration, secure data pipelines, process automation, and technical architecture.
Benefit
Usable AI solutions, not isolated experiments.
AI Services
AI Services
AI Services

Process documents and automate workflows

Problem
Contracts, reports, forms, and emails consume manual capacity, even though many steps are rule-based and traceable.
Approach
We deploy LLM and workflow components to classify documents, extract data, route tasks, and write results back into existing systems in a structured way.
Benefit
Tangible relief for recurring processes, with traceable handling of sensitive content.
AI Services
AI Services
AI Services

Connect ERP, CRM, and knowledge data with LLMs

Problem
Valuable knowledge is scattered across ERP, CRM, wikis, and file shares. General-purpose models can’t use this data without risk.
Approach
We build retrieval-augmented generation architectures with clear data spaces, permissions, source references, and audit logs. Sovereign-hosted on request.
Benefit
Answers based on your own data, with traceable provenance and controlled access.
AI Services
Configure this solution →
·CUSTOM MODELS

Custom Model Development.

Not every problem can be solved with generic models. For specific requirements we develop or train models for classification, forecasting, anomaly detection or decision support.

  • Anomaly detection
  • Classification
  • Forecasting
  • Decision Support
  • Security & risk analysis
  • Document classification
CLASS A CLASS B
·APPROACH

From idea to a production AI solution.

Five phases in which architecture, data, models, governance and operations are considered together.

01Analysis

Clarify use case, data situation, risks and target vision.

02Architecture

Plan system landscape, data pipelines, models and integrations.

03Development

Implement AI backends, assistants, automations or custom models.

04Governance

Integrate roles, permissions, logging, monitoring and documentation.

05Operations

Prepare deployment, maintenance, quality assurance and further development.

For validation and quality assurance, AI Test Automation can be connected as a complement.

View AI Test Automation →