05 — Division

AI Consulting

Your AI pilot has been in 'proof of concept' for two years. Nobody can explain the ROI.

Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers • Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers • Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers • Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers • Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers • Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers • Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers • Computer Vision — NLP — Predictive Models — Deep Learning — MLOps — Edge AI — LLMs — Transformers •

The Problem

Every AI consultant shows you a demo that works on clean data.

Your data is messy, fragmented, and lives in 6 different systems — a CRM that's been running since 2011, a data warehouse nobody fully migrated, and a decade of analyst notes in an Excel file someone's been maintaining on their personal laptop.

That demo will never survive your production environment. And two years from now, your AI pilot will still be a PowerPoint deck with a Gantt chart that says "Phase 2: TBD."

85%
of AI projects never reach production
2 years
average time industrial AI stays in 'pilot' phase
$1.8M
average cost of a failed enterprise AI project

Why Only Droz

We don't build demos that die in committee. Every model we deploy runs in production, monitored 24/7 by our team. Proof of concept to production in 90 days — with the metrics to prove it.

Other AI consultants hand you a model and a slide deck. We own the outcome. If it isn't running in production and hitting your accuracy targets, we aren't done.

Part of Something Bigger

AI needs context. We have 20 years of it.

Most AI consultancies build models on clean data and hope for the best. We have divisions that generate the industrial data, build the software platforms, instrument the buildings, and maintain the equipment. Your AI model gets context nobody else can provide.

Inference Architecture

Models That Decide in Under 12 Milliseconds

In your environment, a model that takes 2 seconds to respond is a model nobody uses. We optimize for latency from day one — not as an afterthought.

Multi-Layer Perceptron — Inference Mode

InputHidden 1Hidden 2Hidden 3Output
Architecture
MLP / 5L
Activation
ReLU
Optimizer
AdamW
Inference
< 12ms

Intelligence at Work

Models built for production from day one.

Not trained on clean benchmarks. Built on your actual data — messy, fragmented, and real — then deployed into production with monitoring that catches drift before your stakeholders notice it.

What We Deploy

AI That Survives Your Environment

Computer Vision at Production Scale

Production

Object detection, image segmentation, and visual inspection systems trained on your actual data — not ImageNet. Deployed in manufacturing, construction, logistics, and security environments.

NLP for Enterprise Knowledge Systems

Production

Document processing, semantic search, and classification across contracts, reports, manuals, and regulatory filings. Because your critical knowledge is buried in PDFs nobody can search.

Predictive Models Trained on Messy Reality

Production

Time-series forecasting and anomaly detection integrated with your existing data pipelines — ERP, CRM, IoT, and legacy systems. We've normalized data from platforms that haven't been updated since 2009.

Deep Learning at Scale

R&D

Custom neural architecture design, training infrastructure, and optimization for your specific domain. Not fine-tuned on someone else's problem. Built from the ground up for yours.

MLOps That Survives Production

Infrastructure

Model versioning, CI/CD for ML, drift monitoring, automated retraining, and A/B testing. Because a model that worked in Q1 quietly fails by Q3 when your data distribution shifts.

Edge AI for Constrained Environments

Embedded

Model compression and deployment to edge devices, embedded systems, and air-gapped infrastructure. Because not every environment has a cloud connection, and not every decision can wait for one.

Results — Not Pilots

0+
Models in Production
0.5%
Avg Accuracy
0TB+
Data Processed
0 days
Proof to Production

How We Get There

From Messy Data to Production in 90 Days

PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes • PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes • PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes • PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes • PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes • PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes • PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes • PyTorch — TensorFlow — OpenAI — Anthropic — LangChain — HuggingFace — MLflow — Ray — Kubernetes •

You don't have a data problem. You have a context problem. We have 20 years of it.

We'll audit your data, identify your highest-ROI AI opportunity, and give you a clear path from where you are to 200+ models in production — in 90 days or less.