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Services / 05 / Production foundations

Data Engineering, MLOps & Cloud

Build the governed data, deployment, evaluation, and observability layer that keeps AI systems reliable.

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Private AI compute and production infrastructure
Production foundations · Production engineering
Discipline05 / 06
DeliveryEvidence → Production
ArchitectureModel · Product · Operations
01 / Engagement outcome05 · Production foundations

A production foundation your team can operate, inspect, secure, and improve after launch.

EvidenceAcceptance criteria before scale
SystemModel, product, data, and infrastructure
OwnershipOperating controls and documentation
02 / Fit and scope

Start where the operational pressure is visible.

The first task is to separate the underlying system problem from the technology that may solve it. That keeps scope tied to evidence and operating value.

Operational signalsWhen this becomes relevant
  • 01

    Models depending on fragile notebooks and manual data movement

  • 02

    No visibility into model, prompt, data, or threshold changes

  • 03

    Inconsistent deployment across cloud, on-premise, and edge environments

  • 04

    Rising inference cost without workload-level measurement

Engineering ownershipWhat Aevum can own
  • 01

    Batch and streaming data pipelines

  • 02

    Model and prompt deployment

  • 03

    Evaluation infrastructure

  • 04

    Observability and drift monitoring

  • 05

    Container and Kubernetes platforms

  • 06

    Cloud, on-premise, and edge operations

03 / Delivery architecture

A controlled route to production.

Each stage should close a specific uncertainty and produce the evidence required for the next commitment.

01

Map

Trace source systems, transformations, ownership, quality, and service expectations.

02

Standardize

Create reproducible environments, artifacts, tests, lineage, and release controls.

03

Deploy

Select infrastructure around workload, residency, latency, resilience, and cost.

04

Operate

Monitor data, performance, reliability, security, and unit economics continuously.

Control pointNo stage advances on momentum alone—evidence, decision, and ownership stay explicit.
04 / Technology

Selected around the operating constraint.

Tools are chosen for capability, deployment environment, team ownership, security, latency, reliability, and total operating cost—not vendor novelty.

AWSAzureGCPDockerKubernetesFastAPIEvent streaming
05 / Related work

See the capability in context.

Delivered projects and production-minded blueprints showing how the engineering behaves inside a real domain.

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Energy infrastructure monitored through a modern intelligence systemDelivered projectPhoto · Frantisek Duris
Energy

Automated Energy Data Intelligence Dashboard

A source-aware data platform for consolidating production signals, notices, market context, and operational exceptions.

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Modern distribution center with loading docks and organized vehicle movementSolution blueprintPhoto · Bernd 📷 Dittrich
Logistics

Route, dock, and yard decision intelligence

A unified operational layer for predicting delays, allocating docks, sequencing work, and improving shipment visibility.

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06 / Connected disciplines

Most production systems cross more than one service boundary.

01AI Strategy & Consulting↗02AI Agents, LLM Applications & RAG↗03Computer Vision & Edge AI↗04Custom Machine Learning & Predictive Analytics↗06AI Product & Web Engineering↗
Make the first decision smaller

Define the evidence required to proceed.

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