Our Services

Three pillars of AI excellence designed to transform your business

Model Training & ML Pipelines

From raw data to production models, we handle the entire machine learning lifecycle. Whether you need convolutional neural networks for computer vision, transformer models for language tasks, or classical ML for structured data, we design, build, and deploy models that deliver business value.

What We Build

  • Deep Learning: CNNs, RNNs, Transformers, Vision models
  • LLMs: Fine-tuning, prompt engineering, quantization
  • Classical ML: Classification, regression, clustering, time series
  • Custom Models: Domain-specific architectures

The Full Pipeline

  • Data Engineering: Preparation, cleaning, feature engineering
  • Development: Architecture design, training, tuning
  • Testing: Unit tests, integration, adversarial robustness
  • Deployment: MLOps, monitoring, retraining

Technology Stack

TensorFlow PyTorch JAX Scikit-learn HuggingFace OpenAI Anthropic Docker Kubernetes

AI Agent Development

Intelligent agents go beyond static models. They reason about problems, plan multi-step solutions, take autonomous actions, and adapt to new situations. We build agents that automate complex workflows, interact with your systems, and deliver measurable business outcomes.

Agent Capabilities

  • Task Automation: Multi-step workflows with reasoning
  • Tool Integration: APIs, databases, systems
  • Decision Making: Autonomous with oversight
  • Learning: Agents that improve from feedback

Frameworks We Use

  • Google ADK: Production-grade agent framework
  • LangGraph: State machine-based orchestration
  • OpenClaw: Multi-agent coordination
  • Custom: Tailored frameworks

LLM Application Development

Large language models are transformative but require careful application architecture. We build robust LLM systems that are accurate, safe, and aligned with your business needs. From prompt engineering to multi-model orchestration, we handle the full stack.

What We Build

  • RAG Systems: Graph-based retrieval
  • Multi-LLM: Best model orchestration
  • Guardrails: Safety and compliance
  • Chat Interfaces: Conversational AI

Quality Assurance

  • Real-time Harnesses: Continuous evaluation
  • Prompt Testing: Systematic evaluation
  • Output Validation: Safety guardrails
  • Monitoring: Production tracking

Frequently Asked Questions

Model training teaches ML algorithms to recognize patterns in your data and make predictions. You need it when you have complex patterns that traditional rule-based systems can't capture. NuWorks handles the complete pipeline from data preparation through deployment.

AI agents can reason about complex problems, make decisions with incomplete information, and adapt to new situations. Traditional software follows fixed rules. Agents learn from interactions and handle novel scenarios.

RAG connects LLMs to your company's actual data so they provide accurate, current answers grounded in your information. Without RAG, LLMs can hallucinate. Graph-based RAG ensures retrieval of the most relevant information.

We deliver a working proof-of-concept in 2 weeks. Moving to full production typically requires 2-4 additional weeks depending on infrastructure, compliance, and scale requirements.

Yes. We manage the complete ML pipeline including data engineering, infrastructure setup, container orchestration, model versioning, and automated retraining. Proper MLOps is essential for production performance.

We use industry-standard tools including TensorFlow, PyTorch, LangGraph, OpenAI, Anthropic, and Google ADK. We're framework-agnostic and choose what's best for your specific problem.

Ready to Transform Your Business?

Let's discuss your AI challenges and how we can help you build production systems that deliver results.

Schedule a Consultation