Custom LLM Development
& Private AI Deployment
Your data never touches a public model. We train and fine-tune large language models entirely within your own infrastructure, including private LLM deployment , RAG architecture, and domain-specific fine-tuning, so you get GPT-level capability without the exposure risk.
*No pressure. No obligation. Just honest product insights from our experts.
End-to-End Custom LLM Engineering Services
Foundation Model Selection & Strategy
Not every problem requires a massive, expensive model. Our AI architects analyze your specific use case as part of a disciplined ai model development process to recommend the perfect foundation — whether that is a heavy-duty model like Meta's Llama 3, or highly efficient Small Language Models (SLMs) like Mistral or Google Gemma, ensuring maximum performance at the lowest inference cost.
LLM Fine-Tuning & Domain Adaptation
We turn generic models into industry experts through hands-on finetuning llm work. Using advanced techniques like Parameter-Efficient Fine-Tuning (PEFT) and LoRA, we train models on your historical data, legal contracts, or medical records — a common starting point for healthcare teams. This creates a highly specialized AI that understands your unique operational logic with pinpoint accuracy.
Secure Private LLM Deployment
Keep your data sovereign. We architect and deploy your custom LLMs entirely within your private cloud (AWS VPC, Azure) or on-premise servers as a true private llm deployment — the same discipline behind every engagement in our wider Generative AI & LLM practice, not a one-off feature. This air-gapped approach ensures your sensitive data never leaves your firewall and complies strictly with GDPR, HIPAA, and SOC2 regulations.
Small Language Model (SLM) Optimization
Bigger isn't always better. We specialize in quantizing and optimizing smaller models (7B to 13B parameters) to run on incredibly cost-effective hardware, backed by rigorous ai model training and evaluation before anything reaches production. You get enterprise-grade intelligence without the crippling cloud computing bills.
LLM Guardrails & Security Engineering
Trust but verify. As part of every enterprise ai development engagement, we engineer strict cognitive guardrails into your models to prevent hallucinations, block toxic outputs, and ensure the AI never reveals sensitive internal data to unauthorized users. Your custom model remains safe, predictable, and brand-aligned.
Enterprise System Integration
An LLM is useless if it sits in a silo. Our llm integration services connect your new custom LLM into your existing SaaS platforms, internal ERPs, or customer-facing mobile applications via robust, custom-built APIs — and if your roadmap eventually calls for the model to take action rather than just answer questions, this is where it connects into our Agentic AI Solutions work.
Our LLM Engineering Tech Stack
Foundation Models
Meta Llama 3
Mistral 8x7B
Google Gemma
Falcon
Claude 3 (API)
Fine-Tuning Frameworks
Hugging Face Transformers
PyTorch
Axolotl
DeepSpeed
Deployment & Inference
vLLM
TGI (Text Generation Inference)
Ollama
NVIDIA Triton
Evaluation & Ops
MLflow
LangSmith
RAGAS
Weights & Biases
The VGD Advantage: Your IP, Your Intelligence
100% IP Ownership
When you rely on SaaS AI tools, you are just renting intelligence — stop paying, and the model is gone. When VGD Technologies fine-tunes a model for you, you own the weights, the training pipeline, and the final intellectual property forever, free to redeploy or retrain it however you choose — the core promise behind every private llm engagement we deliver.
Engineering over "Prompting"
Many agencies claim to do AI, but they just write API wrappers. We are hardcore software engineers, not prompt writers — the difference between a real llm developer team and an agency that just wraps someone else's API. We understand vector math, GPU optimization, and complex data pipelines, allowing us to build production-grade AI systems that actually scale.
Cost-Efficient Inference Architectures
We build for your budget, not just for the demo. By utilizing model quantization and strategic cloud architecture, we dramatically reduce the ongoing hardware costs required to run your AI, ensuring a rapid return on investment — the same standard behind every ai model development services engagement we run.
Frequently Asked Questions About Custom LLMs
Custom LLM development is the process of training, fine-tuning, and deploying a large language model on a company's own proprietary data — rather than using a generic public model — so it understands your specific terminology, workflows, and compliance needs. It typically combines domain-specific fine-tuning with RAG architecture and private deployment.
A custom LLM is trained or fine-tuned on your business data and deployed within your own infrastructure, so it understands your domain and keeps your data private, while a public API like ChatGPT is general-purpose, shared infrastructure, and not customized to your workflows. The trade-off is cost and setup time versus depth of accuracy and data control.
Cost depends heavily on scope: fine-tuning an existing open-source model on your data typically starts in the low tens of thousands, while a fully custom-trained model with private infrastructure and enterprise integration can run into six figures. The most accurate number comes from a scoping session, since data volume and compliance requirements move the price more than the model itself.
A proof of concept typically takes 4–6 weeks, while a full production deployment with enterprise integration usually ranges from 12–20 weeks. Fine-tuning an existing model moves faster than training a model from scratch, which needs significantly more data preparation time.
RAG connects a language model to your company's live data — documents, databases, and knowledge bases — so it retrieves accurate, current information instead of relying only on what it was trained on. This is what allows a custom LLM to answer questions about your actual business rather than general knowledge, without needing to retrain the model every time your data changes.
Fine-tuning takes an existing open-source model and trains it further on your specific data, which is faster and far less expensive. Training from scratch builds a model's architecture and knowledge entirely from the ground up, giving full control but requiring substantially more data, compute, and budget — most business use cases are solved well by fine-tuning.
Yes — custom LLMs can run entirely within your own Virtual Private Cloud (AWS/Azure) or on-premise servers, so no data is sent to a public model provider. This is the standard approach we use for private, enterprise-grade AI deployments that need to meet SOC2 or HIPAA compliance requirements.
Yes — through RAG architecture, a custom LLM can connect to your CRM, ERP, SQL databases, and document repositories to pull real-time information into its responses, without needing the model itself to be retrained. This means it plugs into your existing business processes rather than operating as a standalone chatbot.
Industries with sensitive data or specialized terminology benefit most — particularly healthcare, fintech and banking, legal, and enterprise operations — since a custom LLM can handle compliance-sensitive document review, internal knowledge search, and customer support without exposing proprietary data to public models. The strongest use cases are ones with a measurable outcome, like reduced manual review time.
A custom LLM answers questions and generates content based on your data, while an AI agent goes a step further — it can take action, using tools and APIs to complete multi-step tasks autonomously, like researching, drafting, and triggering a workflow without a human directing each step. Many enterprise deployments start with a custom LLM and later extend it into agentic AI capabilities once the underlying model is in place.