Service · 15 to 30 days
MLOps deployment & industrialisation
Your POC works locally but production scares you? SeedVision builds the technical foundation that turns your AI prototype into an observable, compliant platform able to scale — CI/CD, LLM monitoring, guardrails, runbook.
→ Your teams run the AI platform without calling SeedVision every day.
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Who the MLOps industrialisation engagement is for
- You have a validated AI POC that needs to go to production
- You fear uncontrolled GPU and API costs, and customer-facing hallucinations
- You need to document AI Act compliance for an upcoming audit
When MLOps industrialisation is not the right engagement
- Your use case has not been proven on your data yet: run the POC first
- You are looking for a team to write the application itself: this offer builds the production foundation around the product, not the product
How the MLOps industrialisation engagement runs
- Week 1 — Architecture — Target architecture design: CI/CD, observability, guardrails, infrastructure (Kubernetes or serverless).
- Week 2 — Build — CI/CD pipeline implementation, observability foundation (Langfuse, Helicone, Prometheus), guardrails.
- Week 3 — Compliance and security — AI Act documentation, risk classification, audit logs, prompt-injection penetration tests.
- Week 4 — Run and handover — Progressive production rollout, ops runbook, team training, heightened monitoring after go-live.
What MLOps industrialisation delivers
- CI/CD pipeline for models and prompts (GitHub Actions or GitLab CI)
- LLM observability: cost, latency, hallucinations, drift, alerting
- Security guardrails: prompt injection, personal data, exfiltration
- AI Act compliance: risk classification, audit logs, documentation
- Operational runbook for your teams (incidents, updates, optimisation)
- One-day training session for your ops team
MLOps industrialisation: what the engagement expects from you
- An accessible code repository and a decided target environment: your cloud, your Kubernetes cluster, or infrastructure to be provisioned
- A named owner on the client side to receive the runbook — a platform delivered without an owner slides back into technical debt
- Compliance constraints known from the start: personal data, regulated sector, audit requirements
What you gain
- Costs under control — LLM observability shows every euro spent on APIs, per use case and per user. Optimisation becomes continuous.
- AI Act compliance ready — Documentation, classification, audit logs: you walk into the audit stress-free.
- Team autonomy — Runbook, training, handover support: your teams run the platform without SeedVision on-call.
Questions about MLOps industrialisation
What LLM observability stack do you recommend?
Self-hosted Langfuse for sovereignty, or Helicone for SaaS. For infrastructure metrics: Prometheus and Grafana. SeedVision picks according to your security constraints.
How does SeedVision reduce LLM API costs?
Three levers: smart routing to the cheapest model capable of the task, a semantic cache on repetitive queries, and prompt compression. Typical savings: 30 to 60% of the monthly bill.
What happens after go-live?
Either your team takes over with the delivered runbook, or SeedVision continues on a monthly retainer (Run & maintenance offer). The knowledge transfer is designed to make your team autonomous.
Do we have to rebuild everything to industrialise a POC?
No, if the POC was built with that in mind: the application code is taken as is, and the delivery pipeline, observability, guardrails and compliance documentation are added around it. A rewrite is proposed only when the prototype relies on shortcuts incompatible with production.
How does go-live day work?
Progressively: the new chain first serves limited traffic, with heightened monitoring and an already tested rollback path. Handover to your teams happens after that period, runbook in hand.
Case studies built on MLOps industrialisation
Related reading on the blog
- Sleeper Agents: A $1,000 Backdoor That Benchmarks Miss
- EU AI Act on August 2: The MLOps Stack for Production Compliance
- Designing Tools for AI Agents: Lessons from the hf CLI
- GitHub Copilot Goes Usage-Based: An MLOps Playbook for June 1
- Claude for Finance: Anthropic Ships 10 Ready-Made Agents
- A/B Testing Models in Production Beats Shadow Traffic Alone
- LiteLLM on Bedrock: The Gateway That Budgets Your AI Agents
Talk about your project
Considering the MLOps industrialisation engagement (15 to 30 days) — write to contact@seedvision.fr — reply within 24 hours.