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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.
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.
Relying entirely on complex system prompts (prompt engineering) consumes high token volumes, slows down latency, and still results in inconsistent formatting.
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.
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
A 6-week path from data strategy to production deployment.
Identify target tasks, extract historical records, and construct structured evaluation datasets.
Run LoRA/SFT training runs with hyperparameter optimization.
Benchmark task accuracy against commercial baselines using automated evaluation harnesses (e.g., Ragas, DeepEval).
Quantize weights for low-latency inference and deploy onto your private GPU infrastructure.
Identify target tasks, extract historical records, and construct structured evaluation datasets.
Run LoRA/SFT training runs with hyperparameter optimization.
Benchmark task accuracy against commercial baselines using automated evaluation harnesses (e.g., Ragas, DeepEval).
Quantize weights for low-latency inference and deploy onto your private GPU infrastructure.
Frequently Asked Questions
When should we use Fine-Tuning instead of RAG?
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.
Are our training datasets protected?
Yes. Fine-tuning is executed entirely in isolated, non-shared cloud environments. Your dataset is never exposed to public repositories.
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 is an AI and workflow automation engineering agency building private, production-ready AI solutions for modern enterprises.
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Important Links
Rixdigi Locations:
United Arab Emirates
Office 408, 4th Floor, Al-Wasal Building, Dubai.
+971 (050) 3495669
Pakistan
Office 202, 2nd FLoor, Building #85, Shaheed-e-Millat Road, Karachi
+92 (030) 05002659
United States
923 Elm St, Unit #9, Manchester, NH 03101
+1 (603) 6145703