Custom fine-tuned LLMs deployed entirely within your infrastructure. Complete data sovereignty. Measurable performance. Compliance-ready from day one.
We don't just fine-tune models on code. Our training pipelines work across fundamentally different domains — from enterprise software engineering to physical infrastructure design to regulatory knowledge bases.
A large enterprise Java codebase spanning WildFly, Spring Boot, Kafka, and Elasticsearch needed an AI assistant that understood internal patterns — without sending proprietary code to third-party APIs.
Fine-tuned DeepSeek Coder 6.7B using LoRA on curated Java source files from production repositories. 49-hour training cycle on RTX 5090 GPU with CUDA 13.1 Blackwell architecture.
DeepSeek Coder 6.7B · LoRA/QLoRA · CUDA 13.1 (sm_120) · GGUF quantization (Q4_K_M, Q5_K_M, Q8, FP16) · vLLM serving
Achieved 0.4092 eval loss. Deep understanding of WildFly subsystem config, Spring Boot patterns, Kafka implementations, and Elasticsearch queries — knowledge entirely absent from the base model.
Airport authorities and engineering firms needed rapid access to expert knowledge on parking structure design — spanning FAA Advisory Circulars, ACRP research, IBC/ACI codes, traffic flow, and ADA compliance.
Automated FAA/ACRP document collection, PDF extraction, and domain-specific training pair generation. Hybrid RAG+fine-tuning architecture with Qdrant vector store for grounded, citable answers.
Qwen 2.5 7B Instruct · LoRA · Automated PDF pipeline · Qdrant vector DB · Hybrid dense+BM25 retrieval · nomic-embed-text
Purpose-built AI that reasons about ramp grades, turning radii, load calculations, fire codes, and revenue control — grounded in authoritative sources. Proves our ability to train on non-code domain knowledge.
Regulated enterprises need AI that deeply understands their domain AND can reference specific docs, configs, and runbooks — all without data leaving their infrastructure.
Production-grade RAG layered on domain-fine-tuned models. Ingests four document types with domain-aware chunking, locally embedded, with traceable source citations.
vLLM · Qdrant · Domain-aware chunking (code/prose/config/PDF) · Local embeddings · FastAPI with RBAC, rate limiting, audit logging
Turnkey on-premises platform combining deep domain understanding with factual grounding. Regulated customers get traceable AI answers with source citations — critical for compliance teams.
Our fine-tuning infrastructure is language-agnostic and framework-flexible. Below is a representative snapshot — if your stack isn't listed, we can train on it.
On-premises AI doesn't just simplify compliance — it eliminates entire categories of regulatory risk by design. Here's how our delivery model maps to the frameworks your compliance teams care about.
AI systems processing PHI must comply with HIPAA's Privacy Rule, Security Rule, and Breach Notification Rule. The 2025 HHS proposed updates make all safeguards mandatory — including for AI systems.
Zero data exfiltration — PHI never leaves your security perimeter. No BAA with an external AI vendor required. Eliminates cloud API data transit risks entirely.
Full audit trail — Complete training provenance: data sources, parameters, evaluation metrics, version history. Satisfies HIPAA's requirement to include AI tools in risk analysis.
Security controls — RBAC, MFA, TLS 1.3 encryption in transit, AES-256 at rest, comprehensive audit logging. All standard HIPAA Security Rule requirements.
De-identification — Automated pipelines following HHS Safe Harbor or Expert Determination methods. No PHI leaks into training logs or debugging outputs.
HITECH alignment — Breach notification procedures, minimum necessary access, and documentation supporting enhanced enforcement provisions.
GDPR governs processing of EU residents' personal data regardless of where the organization is headquartered. The EU AI Act adds AI-specific transparency and risk management obligations.
Data sovereignty by design — Data never crosses jurisdictional boundaries. No Schrems II transfer issues, no Standard Contractual Clauses needed for the AI processing itself.
Data minimization — Training pipelines process only what's necessary. Supports right to erasure (Article 17) through model retraining without deleted data.
Transparency — Model Evaluation Reports satisfy Articles 13–14 transparency obligations and Article 22 automated decision-making requirements.
DPIA support — Technical documentation, data flow diagrams, and risk assessments for your Data Protection Officer.
EU AI Act readiness — Risk assessments, activity logs, human oversight provisions, and training data governance aligned with high-risk AI system requirements.
Federal agencies require FISMA compliance, NIST SP 800-53 controls, and FedRAMP authorization. The NIST AI RMF adds AI-specific governance requirements.
NIST SP 800-53 Rev 5 — Supports the full control catalog: access control (AC), audit (AU), configuration management (CM), identification (IA), system protection (SC), integrity (SI).
NIST AI RMF — Maps to all four core functions: Govern, Map, Measure, and Manage across the complete AI lifecycle.
FISMA-ready — On-premises deployment inherits your agency's existing ATO boundary. No separate FedRAMP authorization required for the model.
IL4/IL5 support — All processing remains within the accreditation boundary for CUI and National Security System environments.
Continuous monitoring — Integrates with SIEM platforms (Splunk, ELK/OpenSearch) for FISMA and OMB monitoring requirements.
Auditable AI — Complete training provenance, version control, and evaluation reports create the audit trail required for SOX Section 404 compliance.
Internal control integration — On-premises deployment integrates into existing change management, access control, and segregation of duties frameworks.
Want the full compliance details, case studies, and capabilities in a shareable document?
Download Full Capabilities PDFSee compliance in action — not just on paper
A 20-minute live proof session generates a dated evidence report your compliance officer can hand directly to an auditor.
On-premises fine-tuned models don't just match cloud APIs — they eliminate entire categories of risk that cloud-based solutions can't address.
Every engagement follows a structured, repeatable process designed to deliver production-ready AI with full compliance documentation.
We work with your engineering and compliance teams to identify high-value training data: source code, documentation, wikis, configs, PDFs, and regulatory standards. All data stays within your perimeter.
Using state-of-the-art base models (DeepSeek, Qwen, Code Llama, StarCoder, Mistral), we fine-tune with LoRA/QLoRA on your curated datasets. Models from 1B to 34B+ parameters.
Every model ships with a comprehensive Model Evaluation Report — objective metrics benchmarked against base models and commercial APIs using your actual use cases.
Production deployment via vLLM, TGI, Ollama, or Triton. Integration guides for IDE plugins, CI/CD pipelines, chat interfaces, and custom endpoints. RAG stack included where applicable.
Every engagement begins with a no-obligation technical assessment.
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