Traditional IT consulting firms bill by the hour with junior analysts doing most work while senior partners appear for executive presentations. Large consultancies have methodology frameworks requiring months of organizational change before delivering value. Boutique firms often lack production experience, offering strategy without understanding implementation complexity.

DNA's IT consultancy works differently. Our consultants have built the systems we recommend. We've architected billing platforms processing hundreds of thousands of euros daily, deployed AI applications in compliance-sensitive environments, and migrated mission-critical infrastructure to cloud. This production background means recommendations account for operational reality, not theoretical best practices.

We stay small intentionally. You work directly with senior consultants who've solved similar problems, not teams of analysts learning on your budget. Engagements focus on actionable outcomes: detailed technical specifications, prioritized roadmaps with realistic timelines, cost estimates accounting for hidden complexity. No consultant jargon requiring translation. No frameworks needing extensive change management before showing results.

Our consulting includes implementation support because strategies fail when consultants hand off reports and disappear. We're available through execution, ensuring recommendations survive contact with operational constraints. This approach costs more per hour than junior analyst teams but delivers faster results with fewer expensive course corrections mid-project.

Most organizations experimenting with AI hit the same wall: impressive proof-of-concepts that never become operational systems. Pilots demonstrate AI capabilities but integrating with production infrastructure, handling edge cases, and maintaining accuracy over time prove more complex than anticipated. AI projects stall in pilot purgatory, burning budget without delivering business value.

Our AI consulting addresses the gap between demonstration and deployment. We assess whether your use case justifies AI investment or if simpler solutions deliver comparable results faster. This includes evaluating data quality, since machine learning models are only as good as training data. Many AI projects fail because organizations lack the clean, relevant data ML requires.

For viable use cases, we design implementation approaches accounting for integration complexity, performance requirements, and ongoing maintenance needs. This includes infrastructure for model training, deployment architecture handling production load, monitoring detecting accuracy degradation, and retraining pipelines keeping models current as data patterns evolve.

AI consulting draws from deploying document classification processing substantial volumes for international clients, building predictive systems, and integrating ML capabilities into applications where accuracy directly impacts operations. We know which technical decisions enable AI systems to scale and which create maintenance nightmares requiring constant developer attention.

The goal is AI applications delivering measurable operational improvements, not impressive demos executives show at conferences while business teams continue manual processes.

Cloud migration strategies fail when consultants treat them as pure technology projects ignoring organizational reality. Successful cloud modernization accounts for application dependencies, team capabilities, budget constraints, and operational risk tolerance. It's not about moving everything to cloud fast, it's about modernizing infrastructure delivering business value without gambling on your operations.

Effective cloud strategy starts with workload assessment. Which applications benefit from cloud scalability? Which have licensing or compliance constraints favoring on-premise deployment? Which are so intertwined with legacy systems that migration complexity outweighs cloud benefits? Honest assessment prevents the expensive mistake of migrating everything then discovering some workloads perform worse or cost more in cloud.

Platform selection matters more than vendor marketing suggests. AWS vs Azure decisions should consider your team's Microsoft ecosystem investment, compliance requirements, and specific workload characteristics. Hybrid approaches often make more sense than all-in cloud strategies consultants prefer selling.

Migration sequencing and cost management separate successful modernization from budget disasters. We design phased approaches validating each migration stage before expanding scope. This includes right-sizing infrastructure from day one, not overlicensing then optimizing later. Cloud bills spiral when migrations ignore architectural efficiency.

Our cloud consulting draws from migrating billing platforms to AWS, implementing Azure private cloud for compliance-sensitive workloads, and architecting infrastructure handling substantial transaction volumes. Experience with production systems where downtime creates immediate business impact shapes recommendations accounting for risk, not just technical possibility.

Successful cloud modernization delivers operational benefits justifying investment: improved scalability, reduced licensing costs, faster deployment cycles. Not cloud for cloud's sake because industry analysts say you should.

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