Custom LLM Fine-Tuning & Private RAG Systems Deployed in Your VPC
Generalist commercial AI models lack deep domain knowledge and risk leaking proprietary enterprise data. We fine-tune custom open-source models and architect private RAG pipelines deployed 100% inside your isolated cloud perimeter.
Traditional Flaws vs. The UraanTech Fix
Why traditional agency engagements stall, leak capital, and deliver brittle solutions.
Poor Accuracy on Domain Jargon & Proprietary Formats
Generalist models fail when parsing specialized medical nomenclature, legal contract structures, or proprietary manufacturing codes.
We fine-tune open-source models (Llama 3, Mistral) on your curated domain datasets, training the model to master your internal vocabulary.
Unacceptable Data Privacy & Cloud Compliance Risks
Regulated industries (FinTech, Healthcare, Defense) are legally barred from sending confidential records to public third-party APIs.
We deploy self-hosted models inside your private AWS/Azure VPC with vLLM, ensuring zero customer records ever leave your encrypted boundary.
Skyrocketing Commercial API Token Invoices
High-volume batch processing on commercial foundation APIs (GPT-4) generates tens of thousands in unpredictable monthly billing.
Fine-tuned 8B or 70B open-source models running on dedicated GPU instances reduce ongoing inference costs by up to 75% at scale.
What You Receive
Every deliverable is production-grade, documented, and transferred with 100% intellectual property ownership.
Curated Domain Training Dataset Pipeline
Data extraction, de-duplication, synthetic generation, and formatting scripts preparing high-quality instruction datasets.
Parameter-Efficient Fine-Tuning (LoRA / QLoRA)
Domain model fine-tuning on dedicated GPU clusters optimizing reasoning performance and output format adherence.
Enterprise Hybrid RAG Vector Pipeline
Multi-stage retrieval combining BM25 keyword search, dense embeddings, and cross-encoder re-ranking.
High-Throughput vLLM Private Inference Server
Optimized model serving engine with PagedAttention delivering 5x inference throughput on private GPUs.
Automated Evaluation & Benchmark Suite
Quantitative evaluation pipelines measuring hallucination rates, semantic accuracy, and token latency.
100% Model Weight & Code Ownership
Complete transfer of all model checkpoint weights, training code, and infrastructure deployment manifests.
Real-World Architectural Deployments
Practical use-case breakdowns demonstrating how we engineer solutions around operational bottlenecks.
Private 70B Legal Model Fine-Tuned on 80,000 Corporate Contracts
A legal intelligence firm needed to analyze confidential M&A agreements without violating client non-disclosure agreements with public AI models.
- Curated and sanitized an instruction dataset of 80,000 corporate contracts and legal precedent filings.
- Fine-tuned a Llama 3 70B model using QLoRA on a cluster of 8x NVIDIA A100 GPUs.
- Deployed private inference endpoints inside an isolated AWS GovCloud VPC with strict IAM locks.
- Integrated hybrid RAG allowing associates to query 15 years of internal case precedents in seconds.
On-Premise Clinical EHR Summarization & ICD-10 Coding LLM
A hospital network wanted to automate doctor clinical note summarization, but HIPAA regulations strictly barred cloud API transmission.
- Fine-tuned an open-source 8B medical model for structured clinical SOAP note generation and ICD-10 coding.
- Packaged the model into optimized TensorRT-LLM runtimes executing on on-premise hospital GPU servers.
- Engineered automated medical abbreviation expansion and contradiction detection guardrails.
- Integrated directly into legacy Epic EHR systems via HL7 and FHIR API protocols.
Architected for International Scale & Regional Compliance
Engineered to comply with local financial mandates, data jurisdictions, and cross-border latency targets.
Universal International Standard
Cloud-agnostic model serving deployable to AWS, Google Cloud, Azure, or private colocation data centers.
International Discovery →Gulf Enterprise Ready
Specialized Arabic foundation models (Jais, ALLaM) fine-tuned for regional Gulf enterprise documents and local laws.
WhatsApp Engineering Desk →High Velocity & Compliance
Strict compliance with US HIPAA, SOC2 Type II, and European GDPR data residency mandates with private VPC isolation.
Schedule ROI Audit Call →Our 4-Step Execution Process
A disciplined milestone framework designed to eliminate guesswork, budget drift, and release delays.
Architecture & Specs
We audit existing systems, map database schemas and user flows, and deliver a fixed-scope milestone proposal within 48 hours.
Interactive Design
We craft clickable interactive prototypes and complete design tokens in Figma for rapid stakeholder validation before writing code.
Production Sprints
We develop in tight 2-week milestones with automated CI/CD deployments and live staging previews with regression testing.
Deployment & Warranty
Zero vendor lock-in. Full Git repository transfer, cloud manifest handoff, and 30 days of complimentary SLA post-launch warranty.
Production Tech Stack & Security Standards
Tools, Frameworks & Protocols Used for This Service
Every deployment follows OWASP Top 10 hardening, automated container vulnerability scans, strict environment variable isolation, and automated database backups.
Frequently Asked Questions
RAG is best for injecting factual knowledge (policies, product catalogs, internal documents). Fine-tuning is best for teaching a model a specialized tone, complex reasoning structure, domain jargon, or strict output format. Often, combining both delivers the ultimate enterprise result.
Model weights are stored and served exclusively inside your own private cloud infrastructure or on-premise servers. You own the weights unconditionally with zero external dependencies.
Using modern quantization techniques (AWQ, GPTQ), a highly capable 8B model runs efficiently on a single consumer-grade GPU (like an NVIDIA RTX 4090 or A10G), while larger 70B models run on an 8x A100 cluster.
We run automated evaluation pipelines (using RAGAS and custom benchmark test suites) comparing model outputs against gold-standard human expert answers across accuracy, hallucination rate, and format precision.
A standard engagement (data preparation, training runs, evaluation, and production private deployment) is typically completed within 6 to 8 weeks.
Ready to Solve Your Custom LLM Fine-Tuning & Private RAG Systems Bottleneck?
Book a 30-minute discovery call directly with a Senior Systems Architect. We evaluate your current technical setup and provide an actionable roadmap within 24 hours.