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AI Model Customization & Fine-Tuning background

Custom LLM Fine-Tuning &
Domain Model
Adaptation

Train and adapt open-source and proprietary foundation models on your private domain data to achieve high task accuracy, custom formatting, and low inference latency.

✓

Domain-Specific Precision: Teaches models industry-specific terminology, specialized logic, and custom output structures.

✓

Lower Inference Costs: Replace expensive commercial API calls with lean, highly optimized task-specific Small Language Models (SLMs).

✓

100% IP & Data Ownership: Your model weights, fine-tuning datasets, and checkpoints remain your exclusive property.

Problem & Solution

General Foundation Models Fail on Niche Tasks

Out-of-the-box LLMs are trained on general internet data. They often fail when tasked with strict formatting requirements, specialized medical/legal reasoning, or niche industry terminology.

The Problem

Relying entirely on complex system prompts (prompt engineering) consumes high token volumes, slows down latency, and still results in inconsistent formatting.

The Problem illustration
The Solution

We apply Parameter-Efficient Fine-Tuning (PEFT/LoRA) and Supervised Fine-Tuning (SFT) to embed your domain knowledge and output formats directly into the model weights.

The Solution illustration

Core Capabilities

Dataset Preparation & Synthetic Expansion

Curate, clean, de-duplicate, and format your unstructured business data into high-quality instruction-tuning pairs.

Parameter-Efficient Fine-Tuning (PEFT & LoRA)

Fine-tune multi-billion parameter models (Llama 3, Mistral, Qwen) efficiently without catastrophic forgetting of general reasoning capabilities.

Task-Specific SLM Distillation

Distill the capabilities of massive commercial models into compact 7B/8B parameter models that run affordably on dedicated hardware.

Direct Preference Optimization (DPO)

Align model behavior and tone with your enterprise policies using human feedback and preference ranking.

150%

Reduction in Manual Workload

60%

Faster Decision-Making

3x

Improved Workflow Efficiency

40%

Task Automation Rate

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Technical Specifications

No Component Technical Framework
01 Target Architectures Llama 3, Mistral, Mixtral, Qwen, DeepSeek, OpenAI Fine-Tuning
02 Training Tooling PyTorch, Hugging Face Transformers, Axolotl, Unsloth, DeepSpeed
03 Quantization & Serving vLLM, TensorRT-LLM, GGUF, Ollama, TGI
04 Deployment Hardware NVIDIA A100 / H100 GPU clusters, AWS Bedrock / RunPod endpoints

Implementation Process

A 6-week path from data strategy to production deployment.

WEEKS 1–2 01

Data Strategy

Identify target tasks, extract historical records, and construct structured evaluation datasets.

WEEKS 3–4 02

Training Runs

Run LoRA/SFT training runs with hyperparameter optimization.

WEEK 5 03

Evaluation & Alignment

Benchmark task accuracy against commercial baselines using automated evaluation harnesses (e.g., Ragas, DeepEval).

WEEK 6 04

Optimization & Deployment

Quantize weights for low-latency inference and deploy onto your private GPU infrastructure.

01 WEEKS 1–2

Data Strategy

Identify target tasks, extract historical records, and construct structured evaluation datasets.

02 WEEKS 3–4

Training Runs

Run LoRA/SFT training runs with hyperparameter optimization.

03 WEEK 5

Evaluation & Alignment

Benchmark task accuracy against commercial baselines using automated evaluation harnesses (e.g., Ragas, DeepEval).

04 WEEK 6

Optimization & Deployment

Quantize weights for low-latency inference and deploy onto your private GPU infrastructure.

Your Success Story Starts Here
Let’s Begin!

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Frequently Asked Questions

Got questions? We've answered the most common ones about working with RixDigi — from services to timelines to support.

FAQ illustration

Use RAG to give an AI access to dynamic, changing knowledge (documents, policies). Use Fine-Tuning when you need the model to learn a specific tone, adhere strictly to complex output formats, or specialize in niche technical reasoning.

Build Your Enterprise’s Proprietary AI Model

Schedule a discovery call with our machine learning engineers to assess your dataset and fine-tuning feasibility.

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Ready to Bring Enterprise-Grade AI into Your Operations?

Book a 30-minute discovery session with our engineering team to evaluate your workflows and identify your highest-impact AI opportunities.

Rixdigi Locations:

United Arab Emirates (Global Operations Hub)

Office 408, 4th Floor, Al-Wasal Building, Dubai.

+971 (050) 3495669

Pakistan (Regional Office)

Office 202, 2nd FLoor, Building #85, Shaheed-e-Millat Road, Karachi

+92 (030) 05002659

United States (Regional Office)

923 Elm St, Unit #9, Manchester, NH 03101

+1 (603) 6145703