Most ML projects stall at proof of concept. Agmis delivers production-ready machine learning systems - built on real manufacturing, retail, and industrial data - through machine learning consulting that treats deployment as the goal, not the afterthought. Founded in 2007 with 18+ years of custom engineering, our results are measured in orders of magnitude.
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The consulting market is full of ML prototypes that look good in a sandbox and fail in production. Data that's messier than expected. Infrastructure that wasn't built for inference at scale. Models that drift after six months with no one watching them. Effective machine learning consulting addresses all of these - not just the model.
That's the gap Agmis fills. We handle the full lifecycle - from data architecture to model deployment to long-term monitoring - so you're not left holding a notebook file after the engagement ends.
Every model we build is designed for your infrastructure from day one. Deployment is not an afterthought.
ISO/IEC 27001 certified. EU-based. We understand the data governance requirements that US-first vendors routinely overlook.
Our automotive seat inspection system runs at 27x faster than manual - 2.2 seconds per unit, 99% accuracy across 40+ seat models. That's the standard we hold ourselves to.
Our machine learning consulting spans four core practice areas, each grounded in real industrial deployments with measurable results.
Turn historical operational data into forward-looking decisions. We build forecasting models for demand planning, production scheduling, yield optimization, and maintenance prediction. If you're running spreadsheet-based forecasting in 2026, there's a measurable cost sitting in that gap.
We design and deploy vision-based inspection, detection, and classification systems for manufacturing, logistics, agriculture, and safety compliance. Our food manufacturing deployment achieves 93% accuracy on PPE detection for hardhats and vests, 100% on face masks - running in real factory conditions, not a test environment.
Automate document extraction, classification, and processing workflows. We also advise on LLM integration for internal knowledge retrieval, contract review, and report generation — with a clear-eyed view of where language models are genuinely useful and where they add risk.
A model that degrades silently is worse than no model at all. We architect the monitoring, retraining, and versioning infrastructure that keeps your ML systems accurate over time. This is the piece most machine learning consulting engagements skip. We don't.
Four stages, each with a defined output - not open-ended sprints.
We assess your data infrastructure, existing tooling, and business objectives. If ML isn't the right solution for your problem, we'll tell you at this stage - before you've committed budget to development. That honesty is part of the engagement, not a disclaimer.
We build a working prototype on your actual data and validate it against your performance benchmarks before committing to full development.
Full model development with your engineering team integrated throughout. We design for your infrastructure - on-premise, cloud, or hybrid - and build deployment pipelines from the start.
We deploy, configure monitoring, set retraining triggers, and run a structured knowledge transfer with your team. You own the system after we leave.
Numbers from machine learning consulting projects that reached production - not controlled pilots.
Leading European Manufacturer
99% detection accuracy across 40+ seat variants. 2.2 seconds per unit. Deployed on a live production line.
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Ortho Baltic
Pre-surgical CT scan processing reduced from 3 hours to 60 seconds. Enables same-day surgical planning that was previously impossible.
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Pioneer In a Grid Intelligence
We helped a $6M-funded grid intelligence startup transition from Jupyter notebooks to scalable ML infrastructure using AWS SageMaker and Weights & Biases.
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Feetsee
Mobile platform for at-home diabetic foot monitoring. Thermal imaging detects early inflammation, alerting healthcare providers before complications develop.
Read the case studyWe don't consult across every vertical. Our machine learning consulting is concentrated where we've built genuine deployment depth.
Quality control, defect detection, predictive maintenance, production scheduling
Crop monitoring, yield forecasting, harvest quality classification
Queue management, shelf analytics, demand forecasting, inventory optimization
Medical imaging analysis, pre-surgical planning systems
Aerial inspection, power line monitoring, structural analysis
Perimeter monitoring, intrusion detection, anomaly detection, critical infrastructure protection
Off-the-shelf tools are trained on generic data. Your production process, your defect patterns, your customer behavior - these require models trained on your data, tuned to your context. Custom machine learning consulting delivers accuracy thresholds that justify operational change where generic tools fall short.
Technical audit: 2-3 weeks. Prototype and validation: 4-8 weeks. Production development: 8-20 weeks depending on scope. We define timelines in the audit phase before any development begins. If something would take longer than that range, we tell you then.
You own it. We build documentation, run a structured knowledge transfer with your team, and configure monitoring so you can operate and retrain the system independently. We offer ongoing MLOps retainer arrangements for organizations that prefer it, but it is not a requirement.
Agmis is ISO/IEC 27001 certified and operates under EU data protection frameworks. We can work on-premise with air-gapped environments where required, and we have established data handling protocols for regulated industries including healthcare and manufacturing.
Yes, but we are honest about what that means for scope. If you are starting from zero, the first phase is typically data infrastructure design and collection pipeline setup before any model development begins. We include this in the audit and scope it separately so you know exactly what you are committing to.
We do not publish fixed minimums, but machine learning consulting engagements below 30-50k EUR rarely make economic sense — the setup and audit work is similar regardless of scale. If your problem is smaller, we will tell you what makes more sense in the initial call.
