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Axxonet
IT /AI & Machine Learning
APPLIED AI PRACTICE

AI & Machine
Learning

From data to decision in one loop. Language models, forecasting and computer vision, wired into the systems you already run.

LLMs · RAG · AGENTS · FINE-TUNING · INFERENCE · VECTOR SEARCH · MLOps · COMPUTER VISION · NLP · FORECASTING · ANOMALY DETECTION · EDGE AI · LLMs · RAG · AGENTS · FINE-TUNING · INFERENCE · VECTOR SEARCH · MLOps · COMPUTER VISION · NLP · FORECASTING · ANOMALY DETECTION · EDGE AI ·
01 · Discover
Use-case & data audit
Pick the decision worth improving and confirm the data can support it.
02 · Pilot
Proof of concept
A scoped model or agent, measured against an agreed baseline.
03 · Production
Integrate & harden
Wired into your systems with CI/CD, testing and rollback.
04 · Operate
Monitor & retrain
Drift detection and feedback loops keep accuracy from decaying.
Machine-learning analytics dashboard in production
Models in production: predictions and drift on one live dashboard.
[ 01 ]  Capabilities

Four engines, one intelligence layer.

LLM Automation & Agents

Retrieval-augmented assistants and tool-using agents that automate knowledge work and orchestrate real systems.

LLMs RAG Agents LangChain n8n

Predictive ML

Forecasting, churn, demand and anomaly detection. Models trained on your history and tested on periods they never saw.

Forecasting Anomaly Detection XGBoost Scikit-learn

Computer Vision

Detection, classification, OCR and quality inspection across image and video streams, at the edge or in cloud.

CNNs Detection OCR PyTorch Edge AI

MLOps & Serving

Reproducible pipelines, a model registry, and monitored low-latency serving. The rails that keep a model in production.

MLflow Docker Triton CI/CD
[ 02 ]  How it interacts

The MLOps data loop.

Each stage hands off to the next. Monitoring feeds drift and performance back into training, so the model keeps up with the data instead of ageing out.

01
Data Sources
Kafka · Debezium
02
Pipelines
Apache Hop · n8n
03
Feature Store
Versioned features
04
Training
TensorFlow · PyTorch
05
Model Registry
MLflow
06
Serving
Docker · Triton
07
Apps & Agents
APIs · LangChain
Monitoring and drift detection feed back into training, so the loop keeps learning.
[ 03 ]  Stack

The toolchain we build on.

TensorFlow PyTorch Scikit-learn Hugging Face LangChain RAG Vector DB MLflow Docker Kubernetes Triton ONNX Ray n8n Apache Hop Kafka
MLOps operations and monitoring console
MLOps monitoring: pipelines, model health and inference metrics in one console.

Put AI to work on your metrics.

From a first proof of concept to production MLOps. Tell us the decision you want to improve and we will say whether a model is the answer.