MLOps, IoT & AI Solutions

AI governance consulting helps organizations establish frameworks for responsible AI use. This includes ethical guidelines, regulatory compliance (EU AI Act, GDPR), bias detection, risk management, and transparency. Opsio's AI governance consulting ensures your AI initiatives are safe, fair, and compliant while delivering business value.

MLOps (Machine Learning Operations) is the practice of deploying and maintaining machine learning models in production reliably and efficiently. It combines ML engineering, DevOps, and data engineering to automate the ML lifecycle — from data processing and model training to deployment, monitoring, and retraining.

Our AI visual inspection systems typically achieve 95-99% accuracy, depending on the defect type and image quality. We train custom models on your specific products and defect patterns, then continuously improve accuracy through active learning and retraining on production data.

We work with all major IoT platforms including AWS IoT Core, Azure IoT Hub, Google Cloud IoT, and custom solutions. We also integrate with edge computing platforms, industrial protocols (MQTT, OPC-UA), and sensor hardware from leading manufacturers.

Start with a hosted foundation-model API (Claude, GPT, or Gemini) plus retrieval-augmented generation against your own data. Fine-tuning is justified only when you have a repeatable, measurable failure mode that prompting and retrieval cannot fix — and when the projected query volume makes the inference and engineering cost of a fine-tuned model cheaper than continuing to call the hosted API. For structured prediction (fraud, churn, forecasting), classical ML on your own data still beats foundation models on cost, latency, and interpretability.

Every Opsio engagement ships with an evaluation harness — a frozen test set representative of production traffic, a defined metric set (precision, recall, latency, business KPI lift), and a baseline the model must beat to be considered ready. We run targeted slice tests for known failure modes (rare classes, edge inputs, demographic groups where bias risk is highest) and require explicit sign-off on regression thresholds before promotion. Production monitoring then re-runs key metrics weekly so degradation is detected before it shows up in business outcomes.

The EU AI Act, with phased obligations through 2026 and 2027, requires high-risk AI systems to maintain a risk management system, evidence data quality and governance, produce technical documentation, enable human oversight, and meet accuracy/robustness/cybersecurity standards. Each is auditable. Opsio's AI governance work translates these obligations into concrete controls: a model registry mapped to risk tier, lineage from raw data through deployment, bias and robustness reports per release, an incident response plan, and a documentation template that satisfies regulator and internal audit.

A scoped first use case — narrow problem, available data, clear success metric — typically lands in 8 to 14 weeks. The decisive factor is data readiness: if the relevant data already lives in a warehouse with a documented schema and clean history, training and deployment can move fast. If we are joining transactional systems, stitching CSV exports, or agreeing on the definition of the target variable, expect data engineering to consume 50–70% of the timeline. The second use case on the same data foundation usually costs roughly half the first.

No, but we do need a defined source of truth for the data the model will train on. For a focused first use case, that can be a single warehouse table, a managed feature view, or even a curated S3 prefix — a full data-lake build is not a prerequisite. We commonly stand up the lake later, once multiple use cases are live and the cost of duplicated extraction pipelines outweighs the cost of consolidation. Sequencing matters: pilot on the smallest data footprint that proves the use case, then invest in the lake to scale.

RAG combines a foundation model with a search index over your own documents — when a user asks a question, the system retrieves relevant passages and grounds the model's answer in them. It is the right pattern whenever the answer must reflect proprietary, private, or recent information that the foundation model has not been trained on (internal knowledge bases, product documentation, regulatory texts, customer histories). Done well, RAG dramatically reduces hallucination and lets you update the system's knowledge by changing documents instead of retraining the model.

DevOps automates the build, test, and deployment of code; MLOps adds the parts that are unique to machine learning: dataset versioning, training-pipeline reproducibility, model evaluation and validation gates, model registry and rollout, drift detection (input distribution, prediction distribution, downstream KPI lift), and continuous retraining. DevOps treats software as the only artefact; MLOps treats the dataset, the trained model binary, and the evaluation report as equally first-class artefacts that all need to be versioned, tested, and rolled back together. Most teams need both — MLOps sits on top of a working DevOps practice, not as a replacement.

A first MLOps-enabled use case — narrow scope, available data, clear success metric — typically lands at €60K-€180K of services over 8-14 weeks, plus €500-€8,000/month in production compute and licence costs at moderate volume. Foundation-model API costs add a usage-linked line item (typically €0.001-€0.05 per 1,000 tokens) that becomes dominant once a chatbot or summarisation feature crosses ~1M monthly requests. The second use case on the same platform usually costs 30-50% of the first because data engineering, observability, and CI/CD are reusable; that ratio is the strongest argument for treating MLOps as a platform investment rather than a per-project cost.