Vision AI & Agentic RAG

Machines that
see, reason
and act.

Perceptual intelligence architectures for enterprises that need more than automation: they need real understanding of the visual and semantic world.

Computer Vision AI
+120 Models Evaluated
98.3% Average CV Accuracy
40ms Average Inference Latency
Computer Vision Agentic RAG Multimodal Retrieval Object Detection Semantic Segmentation Vector Databases LLM Orchestration Visual Embeddings Foundation Models Real-time Inference Computer Vision Agentic RAG Multimodal Retrieval Object Detection Industrial SCAI OPC UA · MQTT Semantic Segmentation Vector Databases LLM Orchestration Visual Embeddings Foundation Models Real-time Inference
AI Laboratory
Barcelona, ES
Est. 2019

Advanced
Perception Engineering.

LAND NETWORKING S.L. is an engineering laboratory specialized in computer vision and agentic RAG systems. Our team combines applied research with production implementation, developing architectures that transform visual and semantic data into actionable intelligence.

We are not a generic AI consultancy. We are domain specialists who understand every layer of the stack: from sensor optics to agent decision-making.

  • Founding team with doctoral theses in CV and NLP
  • Production implementations in 12 countries
  • Collaborators in H2020 and Horizon Europe projects
  • Active contributors to PyTorch, Hugging Face, and LangChain
  • Certified partners: NVIDIA, Google Cloud, AWS

Our
Services

From feasibility audit to production deployment. We build systems that work in the real world, not just in research notebooks.

01
Computer Vision Systems

Design and implementation of vision pipelines: detection, segmentation, classification, pose estimation, and 3D reconstruction. From camera to API.

YOLO SAM2 ViT DINO
02
Agentic RAG Architecture

Next-generation RAG systems with orchestrated agents: multi-step planning, episodic memory, adaptive retrieval, and long-context reasoning.

LangGraph LlamaIndex ColPali
03
Multimodal Retrieval

Search systems that understand text, images, video, and audio in a unified way. Multimodal embeddings, cross-modal re-ranking, and late fusion pipelines.

CLIP ImageBind Weaviate
04
MLOps & Production

Infrastructure for production model deployment: inference optimization, quantization, distributed serving, drift monitoring, and CI/CD pipelines for ML.

TensorRT Triton MLflow
05
Vector Infrastructure

Design and optimization of vector databases at scale: ANN indexing, metadata filtering, sharding, replication, and incremental update strategies.

Qdrant Pinecone pgvector
06
Training & Fine-tuning

Supervised and self-supervised training, efficient fine-tuning with LoRA/QLoRA, data synthesis for CV, domain-specific data augmentation, and adversarial evaluation.

LoRA PEFT DPO
07
New Service
SCAI — SCADA + Industrial Artificial Intelligence

We transform industrial infrastructures with accumulating data into systems that observe, understand, predict, and act. It's not about "adding AI to everything": it's about building the right four layers where AI makes real industrial sense.

The starting point is not a model. It's the operational model: defining what to optimize, with what KPIs, at what frequency, and at what error cost. Without that, you end up with dashboards with pretty curves — half the industry's favorite sport.

Reference Pipeline
Sensors/PLC/Edge Broker/Normalization Historian + Context Rules + ML/AI Alarms + Action
OPC UA MQTT InfluxDB Telegraf Node-RED PostgreSQL Grafana Mosquitto EMQX Modbus

SCAI: from accumulated
data to systems
that act.

If you have sensors writing to InfluxDB, you already have half a SCADA set up. What's missing is structuring that into four clean layers where AI makes real industrial sense: reliable capture → operational context → analytics and rules → AI for decision and optimization. And on top, an HMI/SCADA that isn't a badly tuned Grafana iframe.

First Correct Deliverable
It's not an AI model. It's an objectives and variables table:
ObjectiveReduce kWh/unit produced KPIkWh/batch · MTBF · peak demand InputsPower, temp., shift, recipe, weather Frequency1 min / 5 min ActionSetpoint change · load stop Error CostMedium / High
Reference Architecture · 4 Layers
Layer A
Industrial
Acquisition

Signal standardization at the source. OPC UA for maximum machine-enterprise semantic interoperability. MQTT for lightweight edge telemetry. Gateways to translate Modbus, BACnet, CAN, PROFINET.

OPC UA MQTT 5.0 Modbus BACnet Edge Gateways
Layer B
Ingestion and
Normalization

MQTT broker (Mosquitto/EMQX), collectors (Telegraf, Node-RED), and a normalizer that enforces canonical names, units, signal quality, consistent timestamps, and correct context tags. Poorly designed tags in InfluxDB are perpetual premium suffering.

Mosquitto EMQX Telegraf Node-RED
Layer C
Historian and
Operational Context

InfluxDB 3 as time series historian. PostgreSQL for assets, work orders, shifts, recipes, alarms, energy cost, and plant taxonomy. Optionally Neo4j for asset graph and causality analysis between events.

InfluxDB 3 PostgreSQL Neo4j
Layer D · Output
Observability
and HMI/SCADA

Grafana for dashboards, multi-source alerting, and ad-hoc analysis. Dedicated SCADA HMI for operational synoptics of plant, line, and machine. Panels by energy, quality, maintenance, and alarms. This is where AI becomes visible: operator recommendations, early alerts, supervised actions.

Grafana HMI SCADA Alerting
What AI optimizes on top of that stack
Total energy consumption and kWh per line, shift, batch, and machine
Prediction and flattening of demand peaks
MTBF / MTTR: preventive and predictive maintenance
Early detection of anomalies and equipment degradation
Quality vs energy consumption optimization per recipe
Real-time operational recommendations to operators
Time shifting of non-critical loads to optimize tariffs
Our Approach · What Sets Us Apart

We don't start with the AI model. We start with the operational model. Before training anything, we audit what exactly you want to optimize, with what variables and with what consequences of error.

If acquisition is poorly labeled, if timestamps are inconsistent, if there's no operational context (shift, recipe, machine state), the best AI model will produce garbage. AI is the last layer, not the first.

The final result isn't a notebook in production: it's an industrial operating system with actionable alerts, operator recommendations, and dashboards that a plant manager can use without ML training.

4
architecture layers
0
models without defined objective
100%
knowledge transfer to your team
Discuss Your SCADA →

Stack Map

The ecosystem we master. We select tools for maturity, performance, and maintainability, not hype.

Detection
YOLOv11
Detection
RT-DETR
Segmentation
SAM 2
Segmentation
Mask2Former
Foundation
DINOv2
Foundation
Grounding DINO
Multimodal
CLIP
Multimodal
ImageBind
Multimodal
ColPali
LLM
Llama 3.x
LLM
Mistral
LLM
Qwen-VL
Orchestration
LangGraph
Orchestration
LlamaIndex
Orchestration
CrewAI
Vector DB
Qdrant
Vector DB
Weaviate
Vector DB
pgvector
Inference
TensorRT
Inference
Triton
Inference
ONNX Runtime
Training
PyTorch 2.x
Training
Hugging Face
Training
Weights & Biases
MLOps
MLflow
MLOps
DVC
Infra
Kubernetes
Infra
Ray

Architectures,
Models and Benchmarks.

Dense technical documentation. No level-0 tutorials. Straight to what matters: architectures, trade-offs, real results.

Agentic RAG
Agentic RAG · Technical Guide
Building an Agentic RAG System with LangGraph: Memory, Planning, and Adaptive Retrieval

A deep analysis of how to design agents that iterate over their own context retrieval, evaluate relevance, and autonomously reformulate queries for more precise answers.

Read Article →
YOLOv11
Computer Vision
YOLOv11 vs RT-DETR: Real Benchmark on Edge Devices

Exhaustive comparison of latency, mAP, and energy consumption on Jetson Orin, Raspberry Pi 5, and cloud hardware.

SAM2
Segmentation
SAM 2 in Production: Edge Cases, Optimization, and Deployment

How to integrate Segment Anything Model 2 into industrial pipelines, manage complex prompts, and optimize for real-time video.

ColPali
Multimodal RAG
ColPali: Visual Document Retrieval Without OCR

Analysis of ColPali's architecture, its full-page embeddings, and how it outperforms classic pipelines on documents with tables and charts.

Vector DB
Infrastructure
Qdrant vs Weaviate vs pgvector at Scale: What to Choose and When

Vector DB selection guide with real benchmarks at 100M vectors: throughput, consistency, filtering, and operational cost.

Fine-tuning
Fine-tuning
LoRA for Vision Models: When It Works and When It Doesn't

Practical guide to efficient fine-tuning with LoRA on ViT and LLaVA. Analysis of rank, alpha, and layer selection strategies for CV.

Inference
MLOps
TensorRT 10: Multimodal Model Optimization for Production

Complete workflow for export, INT8 quantization, and deployment with Triton Inference Server for combined vision and text models.

Research
Digest

Relevant papers on CV, multimodal RAG, and agentic systems. Selected and commented by our team. No automation.

Mar
2026
VideoRAG: Retrieval-Augmented Generation over Video Corpora with Temporal Awareness
Chen et al. · University of Hong Kong · arXiv:2603.04821
RAG + CV
Feb
2026
VisionAgent: Hierarchical Planning and Tool Use for Complex Visual Question Answering
Patel, Kumar et al. · CMU · arXiv:2602.11034
Agents
Feb
2026
Grounded SAM 3: Universal Open-Vocabulary Segmentation with Spatial Reasoning
Meta AI Research · arXiv:2602.08841
Segmentation
Jan
2026
HybridRAG: Combining Sparse and Dense Retrieval with Cross-Modal Re-ranking at Scale
Zhang, Li et al. · Tsinghua · arXiv:2601.09234
Retrieval
Jan
2026
EfficientDINO: Distilling Vision Foundation Models for Edge Deployment
Martínez et al. · ETH Zurich · arXiv:2601.03718
Edge CV
Dec
2025
ColPali-v2: Efficient Document Retrieval through Late Interaction over Visual Tokens
Faysse et al. · CentraleSupélec · arXiv:2512.14312
Multimodal
Nov
2025
MRAG: Memory-Augmented Retrieval with Episodic and Semantic Stores for Long-Horizon Tasks
Wang, Zhao et al. · Stanford · arXiv:2511.07291
Memory

Use Cases.

Systems deployed in production environments. Real architectures, not lab demos.

Healthcare
Healthcare · Diagnostics
Anomaly Detection in Radiology Images
CV + Multimodal RAG · 94.2% sensitivity
Industry
Manufacturing · Quality
Visual Defect Inspection on Production Line
Semantic segmentation · 40ms latency
Retail
Retail · Intelligence
Product Recognition and Planogram Analysis
Object detection + RAG · national scale
Mobility
Mobility · ADAS
Multimodal Perception for Last-Mile Autonomous Vehicles
Camera-LiDAR fusion · real-time

How We Work.

01
Discovery & Feasibility Audit

We analyze the business problem, available data, and technical constraints. We produce a feasibility report with candidate architectures, computational cost estimation, and agreed success metrics. No blind commitments.

02
Data & Baseline

Audit of existing data, labeling strategy design, baseline benchmarking with foundation models (zero-shot / few-shot) to establish the floor. If the baseline solves the problem, we say so. We don't inflate projects.

03
Architecture & Experimentation

Final architecture design, systematic experimentation with full tracking (W&B), ablation studies, and domain-specific cross-validation. We deliver reproducible notebooks.

04
Production Deployment

Inference optimization (TensorRT, ONNX, quantization), containerization, cloud or edge deployment, drift monitoring, and alerts. CI/CD for models included.

05
Knowledge Transfer

Comprehensive technical documentation, training sessions for the client's team, maintenance playbooks, and continued access to our Knowledge Base. The goal is client independence.

People Who
Build.

CEO
Carlos Carné
CEO & Computer Vision Lead

Telecommunications Engineer. 10 years in industrial perception systems.

CTO
Nestor Casado
CTO & RAG Systems Architect

LangChain and LlamaIndex contributor. Specialist in large-scale retrieval systems.

MLOps
Miquel Ferrer
Head of MLOps

ML infrastructure engineer at Amazon Science for 5 years. Expert in distributed serving and inference optimization.

Research
Maria Flores
Research Lead

Postdoc at ETH Zurich in multimodal learning. Co-author of 14 papers in CVPR, ICCV and NeurIPS. Directs the Research Digest.

Do you have a vision
or retrieval problem?
Let's talk.

First free 45-minute technical session. No sales decks. We analyze your problem and tell you if we have something that adds real value.

Request Technical Session →

No commitment · 24h response

Contact.

Office
C/ Aragón, 186
28011 Barcelona, Spain
Phone
+34 902 40 40 17
Hours
Mon–Fri · 9:00 – 18:30 CET
Legal Details
LAND NETWORKING S.L.
VAT: B-63106900
Barcelona Registry · Vol.2 · Page.23235 · Entry.43