Most businesses don't have an AI problem. They have a process problem, a data problem, or a decision-making problem - and AI may or may not be the right solution. Agmis helps you figure out which it is, then builds and deploys the solution if it is - which is what sets our AI consulting apart from firms that sell technology first.
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The difference isn't about having AI. It's about having AI deployed against the right problem, with the right data, in the right environment — which is precisely where effective AI consulting makes the difference. Here's what that shift looks like in practice.
We're not a strategy consultancy that hands you a roadmap and walks away. We're an engineering team that stays with you from problem definition through to production deployment - and that's what makes the relationship different from other AI consulting engagements.
The audit phase exists precisely to prevent expensive mistakes. If your problem doesn't require AI, we'll tell you in the first two weeks - before you've invested in development.
Our AI consultants have engineering backgrounds. Every recommendation we make is grounded in what we've seen work - and fail - in real deployments across manufacturing, retail, healthcare, and agriculture.
ISO/IEC 27001 certified. EU-based. We understand the data governance, privacy, and compliance requirements that matter to enterprise clients in healthcare and manufacturing.
AI consulting with Agmis spans the full journey from strategy to deployment - not just the interesting technical parts.
Before any AI project begins, you need an honest picture of where you stand. We assess your data infrastructure, process maturity, and existing tooling to identify where AI can create real value and where the prerequisites aren't yet in place. Output: a clear readiness report with prioritized recommendations.
We work with your leadership team to define an AI strategy that fits your business model, budget, and risk appetite. This includes identifying the highest-value use cases, sequencing implementation, and setting realistic expectations for timelines and returns.
Once the right problem is identified, we design the solution architecture and help you evaluate whether to build custom or integrate an existing platform. We have no vendor affiliations - our recommendations are based solely on what fits your requirements.
We build and deploy custom AI solutions - computer vision systems, predictive models, NLP workflows, and process automation - within your existing infrastructure. On-premise, cloud, or hybrid. We don't outsource the technical work.
Deployment is not the end. We support your team through onboarding, run structured knowledge transfer, and monitor system performance post-launch. If a model starts to drift, we catch it before it affects operations.
Five stages, each ending with a concrete deliverable before the next begins.
We start by understanding your business, not by pitching solutions. Through structured discussions with your operational and technical teams, we map your processes, identify pain points, and assess where data exists and in what condition. This stage has no assumptions - we form a view from first principles.
We assess whether the identified opportunities are technically feasible and economically justified. This includes data quality assessment, infrastructure review, and a realistic projection of timelines, costs, and expected returns. We'll tell you if the numbers don't add up — a step many AI consulting engagements rush past.
For opportunities that pass feasibility, we build a working prototype using your actual data. We validate it against your performance targets before committing to full development. This stage exists to reduce risk, not to produce something presentable.
We build the production system and integrate it into your existing infrastructure. This includes testing, security review, and coordination with your IT and operations teams. We don't consider a project deployed until it's running stably in your environment, not ours.
We run structured knowledge transfer with your team, configure monitoring and alerting, and document everything needed to operate the system independently. For clients who want ongoing support, we offer MLOps retainer arrangements. For clients who don't, we make sure you don't need one.
AI consulting that ends in production. Numbers from live systems.
Leading European Manufacturer
99% detection accuracy across 40+ seat variants. 2.2 seconds per unit. Deployed on a live production line, replacing a manual process that had been in place for years.
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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 logistically impossible.
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AI Power Grid Inspection for ESO
Average precision across all power grid infrastructure elements - insulators, crossarms, pillars, and poles - analyzed from 200,000 inspection photos
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Major CEE Retailer
237 queue incidents prevented. 2.5 hours saved per store daily. Deployed across multiple retail locations.
Read the case studyWe don't claim to consult across every sector. Our AI work is concentrated where we've built real depth over multiple deployments.
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
AI consulting is the broader strategic engagement - identifying where AI applies, assessing feasibility, and defining what to build. ML consulting is a more focused technical service for organizations that have already identified a machine learning problem and need it designed, built, and deployed. Many clients start with AI consulting and move into ML consulting for specific projects.
We scope and price each engagement individually. The audit and feasibility stage is typically scoped as a fixed-price project. Development and deployment work is scoped after feasibility is complete, once we have a clear picture of what's involved. We don't sell standardized retainer blocks without assessing your data architecture first.
The initial discovery and business feasibility audit takes between 2 to 4 weeks. If the project progresses to the prototype and validation phase, it usually adds another 4 to 6 weeks. Full-scale production integration and enterprise deployment timelines depend heavily on system complexity but typically span 3 to 6 months.
No. We work directly within your architecture requirements. Whether your workflows run fully on-premises, on private clouds, or within custom hybrid enterprise configurations, we design and deploy local model integrations to keep data processing within your secure boundaries.
You do. All custom model architectures, data pipelines, training configurations, and integrations built specifically for your systems during the engagement belong entirely to your company upon project completion.
