AI & Data
AI that works in the real world.
We support companies from strategy to operations – with realistic expectations, solid evaluation, and a focus on measurable outcomes.
Typical AI services
We cover the full AI lifecycle – from consulting to engineering to infrastructure. What matters to us: clear goals, reliable data, and an operating model that still works after the pilot.
From ideas to a prioritized backlog: target picture, business KPIs, feasibility, risks, build‑vs‑buy.
Access, modeling, quality checks, pipelines and governance – the prerequisites for dependable AI.
Predictions, anomaly detection and optimization – e.g. maintenance intervals, failure probabilities, demand forecasts.
Knowledge assistants, document automation, internal search – with tests, guardrails and source citations.
Automate training/deployments, continuously measure quality, operate models safely and improve them.
Operating vector databases, indexes, backups and access control – a key building block for RAG/semantic search.
GDPR‑ready, traceable, auditable: data flows, permissions, evaluation and documentation.
Hands-on trainings: prompting, evaluation, data literacy, MLOps basics – knowledge stays in your team.
How we work
A clear process reduces risk and turns prototypes into production-ready products.
- 1
Goals & success metrics
What decision or process should improve? We define measurable KPIs (e.g. time saved, precision/recall, cost, deflection rate).
- 2
Data & feasibility
We assess data quality, data access, privacy, and technical constraints. If needed: instrumentation and data collection.
- 3
MVP / proof of value
Fast, testable results – including proper evaluation (offline tests, human review, A/B comparisons).
- 4
Production readiness
Integration into your systems, security, observability, cost controls. For GenAI: guardrails, source citations, policies.
- 5
Operations & continuous 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.
Without reliable data (definitions, quality, history) even great models will be noisy. The biggest leverage is often in the data foundation.
Generative AI may produce plausible but wrong statements. That’s why we use RAG, tests, guardrails, and human approvals where necessary.
A demo chatbot is quick to build. Secure, cost-efficient and maintainable operations require MLOps, monitoring and clear ownership.
We avoid “AI for AI’s sake”. What matters are business outcomes: fewer outages, faster processing, better forecasts, lower cost.
Details: what’s behind it?
A realistic look at typical deliverables – with examples. We combine consulting, engineering and operations so the solution actually works.
Data foundation & data engineering
Typical building blocks: data model, quality, ETL/ELT, feature pipelines, access models, PII handling, data catalog.
Machine learning (classic)
For forecasting/optimization (e.g. predictive maintenance) we define targets, build baselines, evaluate rigorously, and provide explainability/monitoring where it matters.
GenAI / LLM applications (RAG)
We build knowledge assistants on top of your documents: chunking, embeddings, retrieval, source citations, policies, prompt versioning and evaluation (quality, safety, cost).
MLOps & operations
CI/CD for models, model registry, monitoring (quality, drift, latency, cost), rollbacks and retraining – so teams don’t end up firefighting.
Vector DBs & hosting (Milvus)
Operating/scaling vector databases (e.g. Milvus), index strategies, backups, upgrades, access control and performance tuning – on‑prem or in the cloud.
Responsible AI & compliance
Privacy, security and traceability are designed-in: data lineage, permissions, audit trails, documentation and clear approval processes.
FAQs
Common questions about AI projects
Short and honest answers.
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/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/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.
Identify AI opportunities – without buzzword bingo
If you like, we’ll look at your goals, data situation and the fastest levers for real business impact in a short call.