What is the difference between fine-tuning and prompt engineering?

Prompt engineering guides pre-trained models with context, while fine-tuning alters model weights to adopt specific domain knowledge and tone.

How do you prevent AI model hallucinations?

We combine Retrieval-Augmented Generation (RAG), strict prompt system rules, guardrails, and context grounding to keep output reliable.

What cloud or hardware infrastructure is required to run custom LLMs?

We optimize models to run on cost-effective cloud GPU clusters (AWS, GCP, Azure) or private self-hosted local GPU hardware.

How do you evaluate model performance during custom training?

We perform automated evaluation benchmarks, human-in-the-loop validation, and latency-accuracy trade-off testing.

How do you maintain data privacy during model fine-tuning?

All fine-tuning occurs in isolated, encrypted cloud instances or local infrastructure where training data is never sent to public APIs.

LLM Development & Training

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LLM development services delivering tailored AI solutions and training to enhance automation, efficiency, and AI-driven insights.

13BPARAMS

8KCONTEXT

99%ACCURACY

Fine-tuningRAGEmbeddingsMulti-modal

LLM Development & Training

Empower Your Business with Intelligent AI Solutions

At Aptagon Technologies, we build and fine-tune Large Language Models (LLMs) tailored to your business goals. Our AI experts leverage machine learning, Natural Language Processing (NLP), and deep learning to build intelligent systems that understand and respond to human language effectively. From custom chatbots to AI automation, content generation, and data analysis, we design scalable solutions that improve efficiency and enable innovation. With responsible AI practices and domain-specific training, we partner with you to bring generative AI into your workflow and lead the next wave of intelligent transformation.

Our Tech Ecosystem

Our Technology Stack

We leverage modern frameworks and robust languages to build future-proof software.

Django

Kafka

YOLO

Gensim

XGBoost

OpenCV

NLTK

TensorFlow

PyTorch

Fast.ai

KubeFlow

Hugging Face Transformers

Flask

Next.js

Stanford NLP

Scikit-learn

spaCy

MLflow

React.js

Keras

LightGBM

Apache Spark

FastAPI

Hadoop

Our AI Development Process

We follow a strategic, data-driven approach to design, fine-tune, and deploy AI models that deliver real business value.

Step 1

Custom Model Development

Building custom LLMs tailored to your business data to deliver accurate, and relevant AI solutions.

Step 2

Model Fine-Tuning

Optimizing existing models like GPT, LLaMA, and Falcon for industry-specific applications and use cases.

Step 3

Prompt Engineering

Designing effective prompt structures for accurate, contextual, and consistent AI responses.

Step 4

Data Preparation & Training

Curating and preprocessing high-quality datasets for efficient and accurate model training.

Frequently Asked Questions

Yes, we fine-tune open-source models like Llama, Mistral, and DeepSeek on your business data for full control and local data security.

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Empower Your Business with Intelligent AI Solutions

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Custom Model · v2.4

Empower Your Business with Intelligent AI Solutions

Our Technology Stack