Build Production-Ready AI Systems, Not Experiments
Logiciel provides senior AI software engineers who build and ship real-world AI systems from LLM applications and RAG pipelines to automation, ML workflows, and AI-powered SaaS features. Scale your AI roadmap with engineers who deliver measurable outcomes from day one.
AI engineering is not traditional software development. Companies fail when AI is built by general developers, leading to:
Unreliable AI outputs and hallucinations
Slow experimentation with no production readiness
Poor model performance and wrong architecture choices
Exploding GPU and cloud costs
Missing MLOps, monitoring, and security guardrails
AI features that don’t align with product value
AI systems require applied ML + software engineering + DevOps + data engineering. That’s exactly what Logiciel’s AI engineers bring.
Most AI engineers experiment. Ours ship production-grade systems. Our engineers specialize in:
LLMs (GPT, Claude, Llama, Mistral)
Embeddings, vector databases & RAG pipelines
Prompt engineering & agentic workflows
AI automation & SaaS integration
Real-time inference & GPU optimization
MLOps, monitoring & model safety
Cloud-native AI architectures
They focus on reliability, scalability, security, and cost efficiency not demos.
We help teams accelerate AI delivery across:
LLM Applications
Copilots, chatbots, search, summarization
RAG Systems
High-accuracy, low-hallucination AI pipelines
Agentic AI
Autonomous workflows & decision systems
AI for SaaS Products
Embedded AI features inside platforms
AI Automation
Ops, sales, support & workflow optimization
ML & Predictive Systems
Recommendations, forecasting, detection
MLOps & Deployment
CI/CD, monitoring, scaling & rollback
Data Engineering for AI
Pipelines, feature stores & ETL
Dedicated AI Engineers – full-time, embedded in your team
Project-Based Delivery – fixed-scope, outcome-driven
AI Engineering Pods – AI + ML + MLOps + backend
AI Consulting & Architecture – validate and design AI systems
We help teams: ship faster · avoid costly mistakes · reduce GPU waste · build future-ready AI
AI-first, production-focused engineering culture
Proven experience across SaaS, enterprise & automation platforms
Deep expertise in AI + Cloud + DevOps + Data
Rapid onboarding (1–2 weeks)
Strong focus on security, reliability & cost control
Engineers who understand product impact, not just models
Get matched with experienced AI engineers who can design, build, and scale production-grade AI systems.
