AI That Actually Runs in Production

The engineering discipline that closes the gap between a model that works in a notebook and a system the business can depend on. From pipeline engineering to drift detection to cost control - built as a single system, not solved as separate problems.

A model that performs well in a notebook is not a production system. The gap between the two is where most AI initiatives stall.

Data pipelines that worked for a controlled dataset break under real traffic patterns. Inference latency that was acceptable in a demo becomes a user experience problem at scale. Model performance degrades quietly over weeks as input distributions shift. Nobody notices until the business outcome changes.

Most organisations treat pipeline engineering, model versioning, inference infrastructure, and monitoring as separate problems to solve later. Gradion treats them as a single engineering system to build from the start.

How We Engage

For smaller teams: MLOps-Lite applies the same principles at a lighter operational weight. Scoped in two weeks, core infrastructure delivered in eight. Experiment tracking, lightweight model registry, deployment automation, and monitoring sufficient for the model count - without the overhead of an enterprise ML platform.

We work with your existing ML platform or build one. Kubeflow, MLflow, SageMaker, Vertex AI - the discipline matters more than the tooling.

What We Build

Core: Pipeline & Model Lifecycle

Production ML Pipeline Engineering End-to-end ML pipelines that are repeatable, testable, and version-controlled. Feature engineering with lineage tracking, training runs reproducible from a commit hash, model registries with promotion gates, and deployment pipelines that treat a model artifact with the same discipline as application code. The output is a pipeline you can audit, not a notebook someone ran once.

Model Monitoring & Drift Detection Models decay. The question is whether you find out from a dashboard or from a customer complaint. We instrument production models with statistical monitoring that tracks input distribution shifts, output confidence degradation, and business metric divergence. Alerts fire before performance crosses a threshold that matters.

Retraining Pipelines Automated retraining closes the loop between monitoring and improvement. Pipelines trigger on drift signals or scheduled cadence, validate against held-out evaluation sets, and promote to production only when performance thresholds are met. For financial services and identity verification use cases, every retraining event produces an auditable record that satisfies regulatory requirements.

Infrastructure: Serving & Data

Inference Infrastructure Inference is where AI costs either get controlled or spiral. We design serving patterns matched to the load profile: batch, real-time, or async. Right-sized compute, caching where latency allows, benchmarked against cost and SLA targets before go-live. For GPU-dependent models: utilisation baselines, spot instance strategies, and model quantization where accuracy tolerances permit.

ML Data Pipeline Engineering The upstream dependency for every ML outcome. We build ML-specific data pipelines that handle ingestion, transformation, validation, and lineage tracking designed for reproducibility. GDPR compliance is engineered in at the pipeline level, not retrofitted after the fact. Where assessment reveals that the data layer is the bottleneck - not the model - we fix it first, drawing on Gradion's data engineering practice.

Optimization: Cost & Governance

Cost Observability for AI AI/ML resource utilization is often the largest and least understood line item in a technical infrastructure budget. We map spending to business value: which models cost what to serve, what the cost-per-inference is at current volume, and where architecture changes would reduce cost without degrading output quality.

Data Residency For ML Workloads

For regulated ML workloads - particularly in financial services, identity verification, and healthcare - where models are trained and where inference runs are compliance decisions, not just infrastructure choices.

We deploy training and serving infrastructure on EU sovereign cloud or fully on-premise where required. Open-weight models (Llama, Mistral, Phi) enable on-premise inference without external API dependencies. Data used for training, evaluation, and retraining remains within the residency boundary throughout the model lifecycle.

Proof In Production

IDNow - Real-time ML at Regulated Scale IDNow, one of Europe's leading AI-powered identity verification providers, required real-time ML in production at the latency and reliability constraints of regulated identity verification. Gradion has run the ML engineering track inside IDNow's organization for multiple years - model development for document parsing, facial matching, and fraud detection at enterprise scale, with compliance and auditability built into every deployment.

Shopware - Production AI Features at Ecosystem Scale Shopware ships AI-powered features - Flow Builder, AI-generated product descriptions, intelligent search - used daily by hundreds of thousands of merchants across Europe. Gradion's 21-engineer team built these capabilities as production features inside the platform, not prototypes. The collaboration reduced Shopware's development COGS by approximately 40%.

Procelo - Cost-viable AI Agent in 8 Weeks Procelo engaged Gradion to assess feasibility and engineer an AI agent for automated data analysis. Cost and latency analysis was a core deliverable - because a model that runs correctly but at the wrong cost profile is not a viable product. The agent reached 80%+ SQL query accuracy across complex ERP schemas within an eight-week engagement.

All figures are from live engagements. Additional references available under NDA.

Upstream Data Quality

MLOps assessment sometimes reveals that the bottleneck is not the model or the pipeline - it's the data feeding them. Inconsistent schemas, fragmented sources, and undocumented transformations undermine model performance regardless of how well the ML infrastructure is built.

Where data quality is the constraint, we engage Gradion's data engineering practice to fix the foundation before building on top of it.

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