01
Infrastructure cost audit
Get a deep end‑to‑end X‑ray of your AI/ML and cloud spend to uncover waste, quantify savings, and build a concrete optimization plan.
Identify 25–40% potential annual savings across compute, storage, networking, and third‑party AI services.
Get clear, prioritized quick wins you can implement in 4–6 weeks, plus a 3–6 month optimization roadmap.
Align engineering and finance around a single, data‑driven view of AI/ML infrastructure costs and ROI.
Ideal for teams scaling AI or ML into production and seeing cloud costs grow faster than revenue.
02
ML pipeline optimization
Targeted tuning of your training, inference, and data pipelines to reduce compute costs, accelerate iteration, and ship models to production more reliably.
Cut end‑to‑end ML pipeline costs by up to 30–50% while maintaining or improving model quality.
Reduce training and experiment time so your team can iterate faster on better models.
Lower inference latency and cost per prediction through smarter batching, model optimization, and hardware choices.
Ideal for teams running several production or near‑production models where training and inference costs or latencies are becoming a bottleneck.
03
Strategic infrastructure design
A clear, opinionated blueprint for your AI and ML infrastructure that balances cost, performance, and governance for the next 2–3 years.
Replace ad‑hoc decisions with a cohesive cloud and platform strategy (single‑ or multi‑cloud).
Cut future re‑work and migration costs by getting the foundation right once.
Give engineering, finance, and security a shared view of how AI/ML workloads will run and scale.
Ideal for teams moving from successful pilots to business‑critical AI and ML in production.
04
FinOps setup
A structured FinOps program setup that turns random cloud bills into clear ownership, predictable spending, and lasting cost discipline.
Gain real‑time visibility into which teams, projects, and services drive cloud spend.
Cut 15–25% of annual cloud costs by surfacing waste and enforcing accountability.
Build a repeatable methodology for budgeting, reviews, and ongoing optimization.
Ideal for mid‑market and growing tech companies where cloud costs are rising faster than value, and no one owns them.
05
Edge devices pipeline optimization
Turn expensive, over‑provisioned edge infrastructure into a lean, high‑performance distributed system.
Cut the edge hardware TCO by 20–35% by enabling models to run on smaller, cheaper devices.
Reduce network bandwidth costs by up to 50% through smarter local processing and data prioritization.
Improve real‑time inference latency and lower power consumption across your fleet.
Ideal for organizations deploying computer vision, sensor fusion, or autonomous systems across 10+ edge devices where hardware costs or real‑time performance are becoming bottlenecks, limiting scale.
06
End-to-end model integration
Go from zero to production ML/AI, with data pipelines, training, evaluation, deployment, and product integration, built for real workloads.
Turn messy inputs into a reliable data and feature pipeline your model depends on, with clear ownership and repeatable runs.
Ship a production training and deployment flow, versioned datasets, models, and configs, so releases are reproducible and safe.
Deploy serving with a clean interface to your product, including monitoring and runbooks, giving your team full operational ownership.
Ideal for teams that have the use case but lack the end-to-end system from data ingestion to production serving.
07
Continuous optimization program
Turn one-time cost wins into sustained savings – so your team can focus on building while expert cost management runs in the background.
Sustain 10–25% cost optimization through monthly reviews, trend analysis, and proactive identification of new opportunities
Get fast response to cost spikes or architectural questions via dedicated email/Slack support (up to 10 hours/month)
Achieve 5–10× ROI annually: typical €3k–€7k/month investment generates €25k–€200k+ in sustained savings
Ideal for organizations spending €10k+/month on AI infrastructure who want ongoing expert cost management without the overhead of internal hiring.
08
Expert advisory and problem-solving
Flexible, on-demand access to deep ML infrastructure expertise—no project commitment required.
Get rapid technical guidance for platform decisions, cost spikes, or optimization questions.
Book expert reviews, feasibility assessments, or emergency troubleshooting as needed.
No long-term commitment required—book hours as needed for focused expert input.
Ideal when you need fast, expert input on a specific challenge without committing to a full project.
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