Overview
Large Language Models have fundamentally shifted how AI systems interact with text, knowledge, and reasoning. At AppSofa Lab, our LLM research focuses on the practical gap between frontier model capabilities and reliable enterprise deployment — particularly in regulated domains where accuracy, traceability, and security are non-negotiable.
We research the full stack from fine-tuning and alignment through retrieval-augmented generation (RAG) to quantization and on-premise inference, with a focus on federal and commercial clients who cannot rely on public API infrastructure.
Fine-Tuning and Alignment
Pre-trained models encode general world knowledge but require targeted adaptation to excel in specific domains. Our research explores parameter-efficient fine-tuning methods that adapt large models without full retraining:
LoRA and QLoRA
Low-rank adaptation inserts small trainable matrices into attention layers, reducing trainable parameters by over 99% while preserving most of the model's capabilities. QLoRA further enables fine-tuning on consumer hardware through quantization.
Instruction Tuning
Supervised fine-tuning on curated instruction-response pairs shapes model behavior for task-specific formats — critical for enterprise workflows with structured inputs and outputs.
RLHF and DPO
Reinforcement Learning from Human Feedback and Direct Preference Optimization align model outputs with human preferences, reducing harmful or off-target generations in production deployments.
Retrieval-Augmented Generation
RAG decouples model knowledge from model parameters — the LLM handles reasoning and generation while a retrieval system provides up-to-date, verifiable facts. This is essential for enterprise applications where the knowledge base evolves and hallucinations carry real costs.
Our RAG research focuses on three problem areas:
- Chunking and indexing strategy — Optimal document segmentation and embedding schemes for high-precision retrieval across long-form technical documents.
- Hybrid retrieval — Combining dense vector search with sparse BM25 retrieval to capture both semantic similarity and exact keyword matches.
- Graph-enhanced RAG — Augmenting vector retrieval with knowledge graph traversal to surface multi-hop relational context that flat embeddings miss.
Quantization and Efficiency
Our NVIDIA GPU infrastructure with 96 GB per unit enables training and inference at scales beyond typical research labs. We also research model compression to support deployment in constrained environments — edge devices, air-gapped federal systems, and real-time inference at scale.
Enterprise Applications
Our LLM research directly informs AppSofa's enterprise AI services, with active work in:
Document Intelligence
Extraction, summarization, and question answering over large unstructured document repositories.
Knowledge Graph + LLM
Combining structured ontologies with generative models for verifiable, traceable AI responses.
Code Generation
Domain-adapted code assistants for internal tooling, data pipelines, and API integration.
Compliance Automation
Policy-aware LLMs for regulated industries — healthcare, defense, and financial services.
Collaborate
Interested in LLM research or deployment?
We work with federal and commercial clients on custom LLM solutions — from fine-tuning to on-premise deployment.
Get in Touch