Applied AI, engineered like production infrastructure.
Veilia designs and ships cloud AI infrastructure, ML deployment, and intelligent automation for businesses in France and the EU — built by an engineer who has run this in production, not just prototyped it.
Five ways AI actually gets into production.
No fixed product, no single vertical. Veilia applies the same engineering discipline across the parts of a business that AI can genuinely improve — scoped to what each client actually needs.
Cloud AI infrastructure
EKS/AKS architecture, cost-optimized compute, and infrastructure sized for real model workloads instead of guesswork.
ML deployment & MLOps
Taking models from notebook to production — versioning, monitoring, and pipelines that keep working after launch day.
Intelligent automation
Replacing manual, repetitive processes with AI-driven workflows, wired into the tools your team already uses.
Data pipelines
Reliable ingestion, processing and storage that feeds AI systems clean data instead of firefighting bad inputs.
Applied AI solutions
Bespoke AI-led software development scoped to a specific business problem — not a demo, a shipped solution.
Advisory & compliance
Pragmatic guidance on GDPR and EU AI Act exposure for AI/data-processing work, handled case by case.
Services first. Products where the pattern earns it.
Veilia is built to de-risk AI adoption for clients, and de-risk the business itself — no bet on a single unproven product before it's paid for by real engagements.
Deliver, de-risked
Early revenue through AI-led software development and consulting for France and EU clients — grounded in a decade of production cloud and ML engineering, not a pitch deck.
Find the pattern
Recurring, high-value problems surfaced across client engagements get reinvested into one or more in-house AI tools, shaped by what clients actually asked for.
Scale what works
The strongest in-house product moves toward recurring, subscription-style revenue — growing the team as revenue, not ambition, supports it.
Engineering-first
Every engagement is led by someone who has run the infrastructure in production, not sold it.
Data-sovereign by default
France/EU-hosted, GDPR-aware delivery — not a dependency on US cloud AI providers by default.
Lean and accountable
No bloated overhead. Solo-founder discipline means every euro of client spend maps to real work.
Built by the engineer who runs it.
Veeraraghavan Sekar
FOUNDER & PRÉSIDENT, VEILIA SASSenior DevOps and R&D engineer with 10+ years spanning machine learning, cloud computing, and DevOps across French and international technology firms. Currently leads DevOps practice for a 12-engineer team at Ansys–Synopsys in Paris, managing 100+ services across AWS EKS and Azure AKS — work that cut cloud spend by 60% (~$500K) and reduced manual pipeline overhead by 90%.
Previously an R&D Engineer in Machine Learning & Cloud at Smile–Alter Way, building and deploying NLP, anonymization, and classification systems — including an open-source multilingual text-anonymization library with 5,000+ monthly PyPI downloads. Research experience includes Nokia Bell Labs (blockchain-based patch management) and TU Berlin (Digital Twin and cloud orchestration).
MSc in Cloud Computing from TU Berlin and the University of Rennes 1, with a minor in Innovation & Entrepreneurship from EIT Digital Academy. Veilia applies that production-grade engineering background directly to client work, rather than treating AI as a research exercise.
Built for teams that need AI, not an AI team.
Client profile
- Small and mid-sized businesses seeking AI/ML integration or cloud infrastructure modernization
- Corporate teams commissioning bespoke AI-led software development or automation projects
- Organizations that want to pilot AI adoption before committing to an in-house team
Where we work
- France, with a long-term footprint across the EU
- Delivery grounded in GDPR and EU AI Act awareness from day one
- Engagements structured as subscription support or project/day-rate work, depending on scope
Tell us what your team is trying to get AI to do.
A short conversation is usually enough to know whether it's a fit — no obligation, no sales deck.