AUSTIN, TX • ENTERPRISE AI ENGINEERING
Custom LLM Development &Fine-Tuning in Austin, TX
Fine-tune, adapt, and deploy proprietary open-weight LLMs trained on your enterprise data—hosted entirely in your private cloud with zero data leakage and 60% lower token compute costs.
- LOCATION
- Austin, TX (HQ)
- DELIVERY
- Production AI
- COMPLIANCE
- SOC2 / HIPAA / TX Privacy
- IP OWNERSHIP
- 100% Client
MARKET DYNAMICS
Why Standard Frontier Models Fall Short for Specialized Enterprises
While generic models like ChatGPT and Claude are impressive generalists, enterprise use cases demand deep mastery of proprietary taxonomies, custom coding standards, specialized regulatory requirements, and deterministic JSON schemas. Relying solely on massive system prompts consumes excessive context windows, drives up token costs exponentially, and introduces latency. Custom fine-tuning permanently adapts model weights to your domain, achieving superior accuracy on specialized tasks using smaller, faster, and dramatically cheaper models.
CORE CAPABILITIES
Engineered for production complexity.
Four architectural pillars designed to transition AI systems from fragile demo wrappers to resilient, production-grade enterprise platforms.
01
Domain-Specific Parameter-Efficient Fine-Tuning (PEFT / LoRA)
We employ cutting-edge Low-Rank Adaptation (LoRA) and QLoRA techniques to train open-weight architectures (Llama 3.1, Mistral, Qwen 2.5) on curated internal datasets. This achieves precision domain performance while requiring a fraction of traditional full-parameter compute.
02
High-Quality Synthetic Data Generation & Curation
Model quality is dictated by data quality. We build automated data cleaning, de-duplication, instruction-tuning curation, and synthetic data generation pipelines using advanced filtering (Evol-Instruct, UltraFeedback) to ensure pristine training sets.
03
Private VPC & On-Premises Model Serving (vLLM & TensorRT)
We deploy optimized model inference engines inside your private AWS/GCP/Azure tenant or on-premise GPU infrastructure. Using vLLM, continuous batching, and TensorRT-LLM, we deliver sub-30ms token generation times with zero data leaving your network.
04
Direct Preference Optimization (DPO) & RLHF Alignment
We align model behavior with your specific business ethics, safety guidelines, and executive tone using Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF).
TECH STACK MATRIX
Enterprise production stack.
Field-tested models, orchestrators, vector stores, and deployment infrastructure with zero vendor lock-in.
Foundation Architectures
- Meta Llama 3.1 (8B/70B)
- Mistral / Mixtral
- DeepSeek-V3
- Qwen 2.5
- Phi-3
Fine-Tuning Frameworks
- Unsloth
- Axolotl
- Hugging Face TRL
- PyTorch
- DeepSpeed
- FSDP
Inference Acceleration
- vLLM
- NVIDIA TensorRT-LLM
- AWQ / GPTQ Quantization
- GGUF
- Triton
Data Preparation & Curation
- DataTrove
- Cleanlab
- Argilla
- Label Studio
- Spark
Compute Platforms
- AWS SageMaker / EC2 (H100/A100)
- GCP Cloud TPU/GPU
- RunPod
- Lambda Labs
DECISION FRAMEWORK
Cost & Latency Comparison: Proprietary API vs. Self-Hosted Custom LLM
Evaluating financial and operational metrics at enterprise scale (50M tokens/month):
Option 01
Commercial API (GPT-4o / Claude 3.5)
~$250–$750/month in variable API fees with shared multi-tenant latency and vendor data governance policies.
Option 02
Custom Fine-Tuned Llama 3.1 8B on vLLM
~$80–$150/month on a single A10G GPU instance, sub-20ms time-to-first-token (TTFT), and 100% private data isolation.
Option 03
Custom Fine-Tuned Llama 3.1 70B Quantized
~$400/month for state-of-the-art reasoning at parity with frontier models, zero API rate limits, and full IP ownership.
AUSTIN CASE STUDY
Verified Silicon Hills delivery.
CLIENT: Austin HealthTech Firm (Dell Medical Technology Corridor)
THE CHALLENGE
Needed to parse and summarize complex clinical records with 99%+ medical coding accuracy while maintaining strict HIPAA compliance and sub-second latency.
ENGINEERED SOLUTION
AllZone fine-tuned a custom Llama 3.1 8B model using QLoRA on 45,000 anonymized clinical encounters and deployed it on a private HIPAA-compliant AWS SageMaker cluster with vLLM.
99.6% clinical extraction accuracy (outperforming base GPT-4o), 68% lower monthly compute costs, and full regulatory compliance with zero third-party data transmission.
DELIVERY LIFECYCLE
Structured engineering roadmap.
From initial feasibility audits and rapid PoC benchmarking to production VPC hardening and continuous SLA retraining.
- PHASE 1
Dataset Audit, Tokenization & Curation
Extracting, deduplicating, and formatting proprietary enterprise documents into high-quality instruction-response pairs.
- PHASE 2
Baseline Benchmarking & Evaluation Framework
Establishing quantitative evaluation benchmarks (BLEU, ROUGE, LLM-as-a-Judge) on your core business tasks.
- PHASE 3
Hyperparameter Optimization & LoRA Training
Executing fine-tuning runs with DeepSpeed/FSDP, optimizing learning rates, rank dimensions, and context lengths.
- PHASE 4
Alignment & DPO Safety Tuning
Applying Direct Preference Optimization to align model tone, formatting adherence, and safety boundaries.
- PHASE 5
Quantization & High-Throughput Serving Setup
Quantizing weights (AWQ 4-bit / FP8) and configuring vLLM with PagedAttention and continuous batching.
- PHASE 6
Production Telemetry & Automated Retraining Loops
Setting up logging for prompt drift, latency metrics, and automated synthetic data pipelines for periodic retraining.
CENTRAL TEXAS ECOSYSTEM
Austin presence & accountability.
We partner with Austin’s thriving hardware, software, and semiconductor engineering community to deploy optimized, private artificial intelligence that gives Texas enterprises a proprietary technological advantage.
FAQ
Common questions.
With modern LoRA and instruction-tuning techniques, high-impact domain adaptation can be achieved with as few as 1,000 to 5,000 meticulously curated, high-quality examples.
An 8B model runs efficiently on a single NVIDIA A10G or L4 GPU (24GB VRAM). A 70B model quantized with AWQ or FP8 runs smoothly on 2x A100 (80GB) or 4x L40S GPUs.
Yes. We train custom models with constrained decoding frameworks (Outlines, Instructor) ensuring 100% syntactically valid JSON output on every request.
RAG is superior for dynamic information retrieval (finding facts in documents), whereas fine-tuning is superior for learning style, structure, tone, domain vocabulary, and reasoning patterns. The highest performing systems combine both.
You do. 100% of the training datasets, LoRA adapter weights, merged checkpoints, and deployment scripts are your exclusive intellectual property.
A typical enterprise fine-tuning project takes 4 to 8 weeks from data preparation to production VPC deployment.
AUSTIN AI ENGINEERING
Ready to engineer production AI in Austin?
Discuss your requirements directly with our senior AI systems architects. We evaluate feasibility, infrastructure, and ROI within 5 business days.