AI & Data

AI that works in the real world. Not just in the demo.

We support companies from strategy to operations, with realistic expectations, solid evaluation, and a focus on measurable outcomes.

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  • Proof of value often in 2–6 weeks
  • GDPR-ready, on-prem if needed
  • Source citations, not hallucinations
Live demo · retrieval over this page
bm25 · k1=1.2 · b=0.75

→ index ready

Runs entirely in your browser: BM25 ranking over the content of this page, no LLM and no data leaves your device. In client projects we combine retrieval with embeddings, a vector database and an LLM.

Services

The complete AI stack from one team.

From consulting to engineering to infrastructure. What matters to us: clear goals, reliable data, and an operating model that still works after the pilot.

01 · Strategy What is actually worth it?

AI strategy & use-case discovery

From ideas to a prioritized backlog: target picture, business KPIs, feasibility, risks, build-vs-buy.

02 · Foundation

Data & infrastructure

  • Data foundation & data engineering

    Access, modeling, quality checks, pipelines and governance: the prerequisites for dependable AI.

    In practice: Data model, quality, ETL/ELT, feature pipelines, access models, PII handling, data catalog.

  • Vector databases & hosting

    Operating vector databases such as Milvus, a key building block for RAG and semantic search.

    In practice: Operations and scaling, index strategies, backups, upgrades, access control, performance tuning, on-prem or in the cloud.

03 · Application

Models & assistants

  • Machine learning & forecasting

    Predictions, anomaly detection and optimization, e.g. maintenance intervals, failure probabilities, demand forecasts.

    In practice: Define targets, build baselines, evaluate rigorously, and provide explainability and monitoring where it matters.

  • GenAI / LLM applications (RAG)

    Knowledge assistants, document automation, internal search with tests, guardrails and source citations.

    In practice: Chunking, embeddings, retrieval, source citations, policies, prompt versioning and evaluation (quality, safety, cost).

04 · Operations

In production & measurable

  • MLOps & operations

    Automate training and deployments, continuously measure quality, operate models safely and improve them.

    In practice: CI/CD for models, model registry, monitoring (quality, drift, latency, cost), rollbacks and retraining.

Cross-cutting · Across all layers

Responsible AI, privacy & compliance

GDPR-ready, traceable, auditable: data flows, permissions, evaluation and documentation.

Data lineage, permissions, audit trails, documentation and clear approval processes, designed-in from day one.

Cross-cutting · Across all layers

Enablement: workshops & coaching

Hands-on trainings: prompting, evaluation, data literacy, MLOps basics. Knowledge stays in your team.

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How we work

From prototype to production.

A clear process reduces risk and turns prototypes into production-ready products.

  1. 01

    Goals & success metrics

    What decision or process should improve? We define measurable KPIs (e.g. time saved, precision/recall, cost, deflection rate).

  2. 02

    Data & feasibility

    We assess data quality, data access, privacy, and technical constraints. If needed: instrumentation and data collection.

  3. 03

    MVP / proof of value

    Fast, testable results including proper evaluation (offline tests, human review, A/B comparisons).

  4. 04

    Production readiness

    Integration into your systems, security, observability, cost controls. For GenAI: guardrails, source citations, policies.

  5. 05

    Operations & improvement

    Monitoring, drift detection, retraining strategy, incident playbooks, so quality remains stable long-term.

Realistic expectations

AI can do a lot, but it’s not magic.

We help calibrate expectations and make risks visible early.

  • Hype “We just need the right model.”
    Reality

    Data beats models

    Without reliable data (definitions, quality, history) even great models will be noisy. The biggest leverage is often in the data foundation.

  • Hype “The chatbot will know.”
    Reality

    LLMs can hallucinate

    Generative AI may produce plausible but wrong statements. That’s why we use RAG, tests, guardrails, and human approvals where necessary.

  • Hype “The demo works, we’re done.”
    Reality

    Pilot ≠ production

    A demo chatbot is quick to build. Secure, cost-efficient and maintainable operations require MLOps, monitoring and clear ownership.

  • Hype “We need something with AI, too.”
    Reality

    Impact must be measurable

    We avoid AI for AI’s sake. What matters are business outcomes: fewer outages, faster processing, better forecasts, lower cost.

Team

The people behind the models.

Data science, data engineering and MLOps from one cooperative, complemented by cloud, security and software experts from our team when needed.

See the whole team

Tools we work with

  • Python
  • Pandas
  • Scikit-Learn
  • TensorFlow
  • PyTorch
  • ChatGPT
  • LLM
  • RAG
  • doc2vec
  • Apache Spark
  • Apache Kafka
  • Kafka Streams
  • AWS MSK
  • Databricks
  • Delta Lake
  • Apache Druid
  • BigQuery
  • Redshift
  • Imply Druid
  • Apache Doris
  • Snowflake
  • TigerGraph
  • Grafana
  • Kibana
  • Tableau
  • Power BI
  • D3.js
  • Apache Superset
  • Matplotlib
  • Seaborn
  • DBT
  • Apache Beam
  • AWS Step Functions
  • AWS Lambda
  • Azure Data Factory
  • Pentaho
  • Informatica
  • Natural Language Processing
  • Speech Recognition
  • Recommender Systems
  • Statistics
  • Neural Networks

FAQ

Common questions about AI projects

How fast can we see results?

Often within 2–6 weeks for a proof of value (depending on data access and scope). Production readiness typically takes longer due to security, monitoring and integration.

Do we need custom models or is an LLM enough?

Many use cases work well with LLM + RAG. For forecasting and optimization (e.g. maintenance intervals) classic ML is often a better fit. We choose based on goals/KPIs, data and cost.

How do you handle privacy?

We design data flows from day one: PII handling, access control, logging, retention/deletion concepts and, if required, on-prem or private cloud setups.

How do you ensure quality?

With baselines, test sets, clear metrics and reviews. For GenAI additionally: safety tests, prompt and retriever evaluation and source citations.

What about operating costs?

Costs depend on load, latency requirements and model choice. We add cost measurement early (tokens, compute, storage) and optimize deliberately.