AI agents and orchestration
Agents able to run multi-step workflows, make decisions, interact with the IS and delegate to other agents or tools.
LangGraph · CrewAI · n8n · MCP
Domain of expertise
Intelligent automation and end-to-end ML engineering
AI agents for orchestrating complex tasks, reproducible ML pipelines, cloud and edge deployment, MLOps to keep model quality over time. An AI system in production is not a model in a notebook: it is an infrastructure.
Capabilities
What we build
Reproducible ML pipelines
Versioning of data, code and models. Reproducible training, validation and deployment pipelines. Full traceability.
MLflow · DVC · Git · Airflow · Prefect
Model CI/CD
Automation of regression tests, performance evaluations and deployment. Automatic rollback on metric degradation.
GitHub Actions · Jenkins · Docker · Kubernetes
Model serving and API
Model deployment via REST or gRPC API. Autoscaling, load balancing, A/B testing of versions. Guaranteed latency and throughput.
Triton Inference Server · FastAPI · BentoML · Ray Serve
Production monitoring
Monitoring of input distributions, performance metrics, drifts (data drift, concept drift). Alerts and automatic retraining.
Evidently · Prometheus · Grafana · Datadog
Data engineering
Feature stores, real-time or batch ingestion pipelines, data quality, governance. Data in production, not in a notebook.
Feast · dbt · Spark · Kafka · Flink
Stack
Technologies
Open-source and cloud-agnostic stack. No vendor lock-in on MLOps tools: models and pipelines stay in your environment.
Orchestration
Pipelines & workflows
Airflow · Prefect · Dagster · n8n
Training
Training & experimentation
MLflow · DVC · W&B · Ray Train
Serving
Deployment & inference
Triton · FastAPI · BentoML · KServe
Monitoring
Production observability
Evidently · Prometheus · Grafana · OpenTelemetry
Infra
Infrastructure
Kubernetes · Docker · Terraform · AWS / Azure / GCP
Agents
Agents & LLMs
LangGraph · CrewAI · Claude
Deployment
Cloud, edge, hybrid
The deployment architecture is chosen based on latency, network connectivity and data sovereignty constraints: not on our technology preferences.
Industrial edge
NVIDIA Jetson Orin / AGX, Hailo-8, industrial IPC servers. For real-time latency constraints or limited network connectivity.
Managed cloud
AWS SageMaker, Azure ML, GCP Vertex AI. For variable loads, heavy batch processing or LLM models.
On-premise
GPU or CPU cluster on client infrastructure. For industrial or regulatory data sovereignty constraints.
Hybrid
Edge inference + cloud training. The most common case in industry: local decision, centralized retraining.
Results
What it changes in production
The difference between a model that works in dev and a system that runs in production without constant supervision.
< 1 week
to deploy a validated model with an established CI/CD pipeline
< 24 h
to detect a performance drift in production
99.5 %
target availability SLA on critical production systems
0 manual supervision
target on automated retraining pipelines
Approach
From data to production
01
System audit
Existing infrastructure, deployment constraints, required SLA, IS integrations. Identification of MLOps gaps. 1 week.
02
Architecture
Choice of the deployment stack, CI/CD pipeline design, monitoring strategy. Alignment with the IT / OT teams. 2 weeks.
03
Build
Model development or packaging of an existing model, performance tests, containerization. 3 to 6 weeks.
04
Deployment
Go-live, load tests, progressive rollout, SLA validation, team training. 2 to 4 weeks.
05
MLOps
Drift monitoring, alerts, automatic retraining, version management, performance dashboards.
Get started
An AI system to move into production?
POC validated but stuck before production, system with no monitoring, undetected performance drift: describe the situation and we assess what is needed.