LLM Application Engineering
Secure LLM applications, copilots, knowledge assistants, document intelligence and workflow tools built for enterprise users.
Build AI systems that are ready for real users, real data and real business pressure.
Logiciel helps enterprises move AI from prototype to production with practical engineering discipline. From LLM applications and workflow automation to MLOps, data pipelines, cloud infrastructure, governance and managed operations, we build AI systems that perform reliably inside enterprise environments.
Most enterprises do not fail because an AI demo cannot work. They struggle because production AI needs reliability, security, scalability and operational ownership.
We build production AI systems that engineering, security and business teams can trust.
We cover the full production AI lifecycle. Models, data, infrastructure and operations need to work together.
Secure LLM applications, copilots, knowledge assistants, document intelligence and workflow tools built for enterprise users.
Automation of repetitive, manual and decision-heavy workflows across products, operations, support, finance and internal teams.
Repeatable training, validation, deployment, monitoring, versioning and rollback workflows for AI and machine learning systems.
Data pipelines, feature-ready datasets, retrieval architecture, vector databases and model-ready data foundations.
Scalable cloud infrastructure, containerized deployments, CI/CD automation, inference services and cost-controlled environments.
Monitoring for latency, cost, usage, errors, model behaviour, output quality, drift and production incidents.
Access controls, audit trails, human review workflows, risk classification, data protection and compliance-aligned AI operations.
A standing team of AI engineers, data engineers, cloud specialists, MLOps experts and product engineers embedded into your roadmap.
Senior AI architects and engineers who strengthen your internal product, platform, data or engineering teams.
Fixed-scope engagements with defined production outcomes, delivery milestones and success baselines agreed up front.
Detailed assessment of AI prototypes, workflows, data readiness, architecture gaps, reliability risks and production requirements.
Custom copilots, agents, RAG systems, intelligent workflows, prediction services and AI-first product features.
ETL, ELT, streaming pipelines, vector databases, embeddings, chunking, retrieval quality controls and model-ready datasets.
Model registries, CI/CD pipelines, validation gates, deployment automation, rollback workflows and environment promotion.
Dashboards, logs, traces, quality metrics, latency tracking, cost reporting, drift detection and alerting workflows.
Policies, access controls, audit trails, human approval checkpoints, monitoring, documentation and responsible AI practices.
Ongoing monitoring, incident response, performance tuning, cost review, model evaluation, reliability support and continuous improvement.
Patterns from our AI-first engineering teams that help enterprises move from AI pilots to dependable production systems.
Enterprise Production AI Operating Model
How we structure ownership, deployment controls, observability, governance, incident response and continuous improvement across AI systems.
Production AI Readiness Framework
A practical approach to ranking AI systems by business criticality, data readiness, reliability needs, governance exposure and scaling complexity.
1. Production AI Diagnostic and Baseline
We assess prototypes, workflows, data sources, deployment patterns, monitoring gaps, governance controls and business priorities.
2. Architecture and Readiness Mapping
We identify what must change across data, models, infrastructure, integrations, security and operations before production rollout.
3. Production Engineering and Integration
We build AI applications, data pipelines, deployment workflows, retrieval layers, integrations and secure cloud foundations.
4. Reliability, Governance and Observability
We harden AI systems with monitoring, drift detection, audit trails, access controls, runbooks, alerts and operational reporting.
5. Production AI Operating Model
We hand over a repeatable AI engineering practice, including ownership, KPIs, dashboards, release cadences, incident response and improvement workflows.
Ready to turn Production AI Engineering Services into a dependable foundation for enterprise AI adoption? Partner with Logiciel to design, build and operate AI systems that scale beyond prototypes and perform reliably in production.
Production AI Engineering Services include AI architecture, LLM development, data engineering, MLOps, deployment automation, observability, governance, security, reliability engineering and managed production operations.
An AI prototype proves that a use case can work. Production AI must also handle real users, changing data, security controls, monitoring, cost management, rollback workflows and ongoing operational support.
Most engagements produce a production readiness assessment and initial production roadmap within 2-4 weeks, while full production implementations usually run across phased delivery waves.
Yes. We can assess, refactor and productionize existing AI prototypes, LLM applications, RAG systems, ML models, automation workflows and AI-first product features.
Yes. We offer milestone-based pricing once scope, KPIs, production requirements, integration needs and delivery milestones are agreed.
You retain ownership of all AI applications, workflows, prompts, models, pipelines, infrastructure, dashboards, governance assets, runbooks and implementation materials.
We implement access controls, audit trails, human approval workflows, model monitoring, data protection, documentation and compliance-aligned deployment practices.
Yes. We run managed operations with SRE, observability, incident response, performance tuning, cost review, model evaluation, drift monitoring and continuous improvement.