ende

AI Integration: From Hype to Production

We integrate generative AI deep into your core systems — as a scalable, production-ready architectural component.

Let's talk

Integration makes all the difference

Most AI projects don't stall because of technology — they stall because of missing connections. LLMs without access to real-time enterprise data hallucinate. Agents without access to the systems they need can't act.

As experts in making complex architectures work together, we integrate AI systems securely into production environments. That's how we turn experiments and proofs of concept into real competitive advantages. Technology-agnostic and enterprise-ready.

What We Do

AI System Integration

AI becomes part of your core systems — embedded in business processes, connected to real-time data, delivering value from day one.

AI Process Automation

Recurring workflows run on autopilot. Cycle times go down, and so do error rates.

Agentic Workflows

AI agents that autonomously gather information, prepare decisions, and trigger actions — all within defined guardrails.

Knowledge Management / RAG Systems

Make internal knowledge accessible: RAG-based assistants connect securely to your data and surface it exactly where it's needed.

AI Search

Semantic search that understands what you mean — even with typos, synonyms, or mixed languages. More relevant results, less support effort

AI Use Case Evaluation

Which AI application has the highest impact? We evaluate use cases by business value, feasibility, and risk — and turn them into a prioritized roadmap.

AI-native software agency for complex architectures

As a software agency, we think AI-native. Rather than stopping at pilot projects, we integrate AI where real business value is created: in running commerce platforms, PIM systems, CMS, and middleware. Directly within complex architectures handling real traffic and real business constraints.

Our foundation: 25+ years of experience integrating enterprise systems. We know the architectures AI needs to work in — because we build them ourselves. That systems expertise makes all the difference.

For us, AI-native means:

  • AI is deeply embedded in architectures and solutions,

  • AI assists in how we work, and

  • AI amplifies outcomes for our clients.

AI Embedded

AI is a functional building block of every system we develop. Models run directly in commerce workflows, personalize content across CMS/PIM, or drive decisions in backend services. We architect systems so that AI components are scalable, secure, and maintainable by design.

AI Assisted

Our teams use AI to accelerate development cycles and elevate output quality. That includes automated code generation and review, data-driven decision support for technology and design choices, and intelligent testing and debugging pipelines.

AI Enhanced

The applications we build deliver tangible value through AI: processes that optimize themselves, assistive capabilities for operational teams, automated analytics and monitoring, and systems that make data-driven decisions. The goal is measurable impact — greater efficiency, lower costs, or a better user experience.

How we work

AI-powered development brings a new level of efficiency

Our teams work with AI coding agents that handle routine development tasks: setup, scaffolding, tests, code reviews, documentation. This lets our engineers focus on what matters — architecture, design decisions, and complex problem-solving. The result: shorter development cycles, higher code quality, and faster iterations from prototype to production.

Here's how we put AI to work in production:

Code Generation

AI assists with code creation, freeing developers to focus on architecture, design, and complex problem-solving.

Quality Assurance

AI automates testing and code analysis, surfaces vulnerabilities earlier, and improves overall software reliability.

Project Setup & Deployment

AI speeds up setup and CI/CD, catches pipeline issues early, and ensures stable deployments.

Rapid Prototyping

AI helps us prototype UI concepts and features faster — and iterate with real user feedback.

Documentation

AI generates and maintains technical documentation automatically — always up to date with the codebase.

Data Analysis & Visualization

AI processes project data and usage metrics, turning them into clear, actionable dashboards for smarter decisions.

AI Tech Stack

AI Use Cases

Customer Service

Customer Service

Incoming customer inquiries via email are automatically detected, categorized, and routed to the right process step. A classification model analyzes subject lines, body text, and attachments, extracts relevant entities (customer, deadline, references), and forwards the inquiry to the appropriate pipeline.

  • Less manual inbox screening effort

  • Faster response times on RFQs

  • A clean entry point into the digital quoting process

Data Integration

BOM Agent

Different bill-of-materials formats (BOMs) are converted into a unified exchange format. Models recognize table structures, column meanings, and units, and map customer descriptions to internal product structures.

  • Media breaks in the quoting process are eliminated

  • BOMs can be processed automatically across systems

Procurement

Parts Recommendation Agent

The system automatically suggests alternative parts for requested components. Recommendation logic draws on a central data platform with products, pricing, partners, and historical orders/quotes, prioritizing alternatives by price, availability, and margin.

  • Better quote quality

  • Higher margins

  • A more resilient supply chain without manual research effort

Commerce

Semantic Product Search

Semantic search that understands typos, synonyms, foreign languages, and varying phrasing. Transformer models convert products and search queries into vectors, queried via similarity search against a vector index. The model is fine-tuned with real search queries and domain data and integrated into a hybrid search setup where semantics improve recall while existing rules control ranking.

  • Better result quality despite typos and mixed languages

  • Higher conversion and satisfaction

  • Less support overhead

  • A foundation for conversational search or self-service

Process Automation

RFQ Generator

Free-text emails and attachments are transformed into structured requests for quotation (RFQs). AI-powered information extraction identifies parts, quantities, delivery terms, and other parameters and writes them into a standardized RFQ format.

  • Significantly reduced manual data entry

  • A consistent data foundation for further automation steps

Software Development

CRAG: Contextual RAG Assistant for Developer Teams

An internal RAG system that models project code and sessions as a knowledge graph, supporting developers with questions about code and project knowledge. The code structure is mapped in a graph database, queries run through local LLM backends with optional reranking and a web UI. Contradictions in the knowledge graph can be detected and resolved via CLI.

  • Less manual context searching in code

  • Faster answers to complex developer questions

  • More consistent project knowledge

IT Support

Support Assistant

An internal company app that serves as the entry point for IT/SysAdmin support, answering simple requests directly and only escalating complex cases as Jira tickets. Users send a message, the service first attempts an LLM-generated answer; if that's not sufficient, a ticket with full context is created automatically. Errors are logged in an internal log channel.

  • Less routine work for SysAdmin teams

  • Shorter response times on support requests

  • Transparent escalation for larger issues

Workshop

First things first: AI Readiness Workshop

AI projects don't fail because of ideas. They fail because of the technical foundation. Our workshop is the reality check: we assess your infrastructure, evaluate your AI readiness, and develop an actionable roadmap together.

Book your intro call

Let's get serious

Let's talk about your AI projects — whether it's an idea, an early pilot, or a running system.

Timothy Becker