import torch import torch.nn as nn from transformers import AutoModel class CustomMLModel(nn.Module): def __init__(self, config): super().__init__() self.backbone = AutoModel.from_pretrained(config.model_name) self.classifier = nn.Sequential( nn.Linear(config.hidden_size, 256), nn.ReLU(), nn.Dropout(0.1), nn.Linear(256, config.num_labels) ) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) pooled = outputs.last_hidden_state[:, 0] return self.classifier(pooled) # Training pipeline trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics )import torch import torch.nn as nn from transformers import AutoModel class CustomMLModel(nn.Module): def __init__(self, config): super().__init__() self.backbone = AutoModel.from_pretrained(config.model_name) self.classifier = nn.Sequential( nn.Linear(config.hidden_size, 256), nn.ReLU(), nn.Dropout(0.1), nn.Linear(256, config.num_labels) ) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) pooled = outputs.last_hidden_state[:, 0] return self.classifier(pooled) # Training pipeline trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics )import torch import torch.nn as nn from transformers import AutoModel class CustomMLModel(nn.Module): def __init__(self, config): super().__init__() self.backbone = AutoModel.from_pretrained(config.model_name) self.classifier = nn.Sequential( nn.Linear(config.hidden_size, 256), nn.ReLU(), nn.Dropout(0.1), nn.Linear(256, config.num_labels) ) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) pooled = outputs.last_hidden_state[:, 0] return self.classifier(pooled) # Training pipeline trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics )import torch import torch.nn as nn from transformers import AutoModel class CustomMLModel(nn.Module): def __init__(self, config): super().__init__() self.backbone = AutoModel.from_pretrained(config.model_name) self.classifier = nn.Sequential( nn.Linear(config.hidden_size, 256), nn.ReLU(), nn.Dropout(0.1), nn.Linear(256, config.num_labels) ) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) pooled = outputs.last_hidden_state[:, 0] return self.classifier(pooled) # Training pipeline trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics )import torch import torch.nn as nn from transformers import AutoModel class CustomMLModel(nn.Module): def __init__(self, config): super().__init__() self.backbone = AutoModel.from_pretrained(config.model_name) self.classifier = nn.Sequential( nn.Linear(config.hidden_size, 256), nn.ReLU(), nn.Dropout(0.1), nn.Linear(256, config.num_labels) ) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) pooled = outputs.last_hidden_state[:, 0] return self.classifier(pooled) # Training pipeline trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics )import torch import torch.nn as nn from transformers import AutoModel class CustomMLModel(nn.Module): def __init__(self, config): super().__init__() self.backbone = AutoModel.from_pretrained(config.model_name) self.classifier = nn.Sequential( nn.Linear(config.hidden_size, 256), nn.ReLU(), nn.Dropout(0.1), nn.Linear(256, config.num_labels) ) def forward(self, input_ids, attention_mask): outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) pooled = outputs.last_hidden_state[:, 0] return self.classifier(pooled) # Training pipeline trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics )
ai-ml-development.tsx
$dgcrux build --service=ai-ml▌

AI & ML that ships to production

We don't just build models — we deploy them. Every project ends with a production-ready, monitored, and continuously improving ML system integrated into your product.

> start_project()View Results
400+ models in production
12ms avg. inference
60+ ML engineers
PyTorch · TensorFlow · JAX
PyTorch// Frameworks
TensorFlow// Frameworks
scikit-learn// ML
Hugging Face// NLP
LangChain// GenAI
OpenAI API// GenAI
MLflow// MLOps
Kubeflow// MLOps
Apache Spark// Data
Kafka// Streaming
Airflow// Orchestration
Ray// Distributed
ONNX// Deployment
Triton// Serving
dbt// Data
Weights & Biases// Experiment
PyTorch// Frameworks
TensorFlow// Frameworks
scikit-learn// ML
Hugging Face// NLP
LangChain// GenAI
OpenAI API// GenAI
MLflow// MLOps
Kubeflow// MLOps
Apache Spark// Data
Kafka// Streaming
Airflow// Orchestration
Ray// Distributed
ONNX// Deployment
Triton// Serving
dbt// Data
Weights & Biases// Experiment

// capabilities

Every flavour of AI/ML, production-proven

Core ML

Custom Machine Learning Models

We architect, train, and deploy ML models tuned precisely to your data and business problem — not off-the-shelf solutions repurposed for your domain.

+34%
Model accuracy improvement
5×
Training time reduction
NLP

Natural Language Processing

Text classification, sentiment analysis, entity extraction, and custom LLM fine-tuning for your private data.

Computer Vision

Vision & Image Intelligence

Object detection, image segmentation, video analytics, and defect inspection systems for industrial and consumer applications.

Data Engineering

AI-Ready Data Pipelines

End-to-end data ingestion, cleaning, feature engineering, and real-time streaming pipelines built for ML workloads.

MLOps

Model Deployment & MLOps

Production-grade ML infrastructure with continuous training, A/B testing, drift monitoring, and automated retraining pipelines. Your models stay accurate long after launch.

Hours not weeks
Deployment time
99.9%
Model uptime SLA
Generative AI

GenAI Application Development

RAG systems, LLM agents, copilots, and multi-modal apps using OpenAI, Anthropic, Mistral, and open-source models.

400+
ML Models in Production
98%
Model Accuracy Target
12ms
Avg. Inference Latency
60+
AI/ML Engineers
3×
Faster Time-to-Model
100%
Model Explainability

// process

From raw data to live model in 6 stages

Every ML project follows a rigorous, reproducible methodology — not ad-hoc notebooks. You get full visibility at every stage.

Discuss Your Project
01

Problem Framing

We translate your business challenge into a precise ML problem statement — choosing the right task type, success metrics, and data requirements before touching code.

02

Data Assessment

Audit your existing data for quality, volume, and relevance. We design data collection strategies and feature engineering pipelines that give models the best possible inputs.

03

Experimentation

Rapid prototyping with tracked experiments in MLflow or W&B. Multiple model architectures evaluated against your success metrics with full reproducibility.

04

Training & Optimisation

Distributed training on GPU clusters, hyperparameter tuning, and model compression for efficient inference. We optimise for both accuracy and serving cost.

05

Evaluation & Validation

Rigorous hold-out testing, bias audits, and explainability reports. We don't deploy what we can't explain and validate.

06

Production Deployment

Containerised model serving with auto-scaling, A/B testing infrastructure, real-time monitoring, and automated retraining triggers.

// results

Real models. Real outcomes.

A sample of what we've shipped across industries.

IndustryUse CaseOutcome
Fintech
Fraud detection model43% fewer false positives
Healthcare
Medical image diagnosis92% diagnostic accuracy
Retail
Demand forecasting28% inventory reduction
Manufacturing
Predictive maintenance67% downtime reduction
SaaS
Churn prediction3.1× retention improvement
Logistics
Route optimisation22% fuel cost reduction
> model.status = "ready_for_production"

Your ML model is
one conversation away

Tell us your problem. We'll tell you which ML approach fits, estimate the data requirements, and give you a realistic timeline — free, with no strings.

Build My ML System See More Results