Secure Custom LLM Development & Private Deployment
Achieve data sovereignty and competitive advantage through custom LLM development and private LLM development ensuring enterprise LLM without data leakage. Our proprietary LLM development and large language model development services train large language model for business on proprietary data creating domain-specific models understanding industry terminology, company knowledge, and specialized workflows. Deploy custom LLM for enterprise knowledge base through on-premise LLM development or private cloud infrastructure ensuring complete data control. Private LLM deployment for corporate data protects intellectual property, maintains compliance (GDPR, HIPAA, SOC 2), and prevents competitive exposure through API providers. Our enterprise LLM solutions deliver 40% accuracy improvement over general models, 100% data sovereignty, zero data leakage, and full model ownership enabling organizations to leverage AI while maintaining security, privacy, and control.
Custom LLM development addresses critical enterprise requirements that public APIs cannot meet. Train large language model for business incorporating proprietary terminology (internal product names, processes, acronyms), domain expertise (medical knowledge, legal precedents, financial regulations), company knowledge (policies, procedures, historical decisions), and brand voice (tone, style, messaging guidelines). Domain-specific LLMs achieve 90% accuracy on specialized tasks versus 60% for general models through focused training. Private LLM deployment eliminates data leakage risks inherent in cloud APIs - proprietary information, customer data, trade secrets, unreleased products remain secured. On-premise LLM development enables air-gapped deployment for maximum security meeting requirements of defense, healthcare, finance, and government. Compliance requirements (GDPR data residency, HIPAA PHI protection, financial regulatory data controls) are satisfied through complete infrastructure control. Model ownership provides perpetual access without ongoing API fees, protection from provider changes, and ability to monetize internally developed IP.
Our enterprise LLM solutions span complete lifecycle from foundation model selection through production deployment. Architecture design evaluates requirements determining optimal approach - fine-tuning existing models (Llama 3, Mistral, Falcon) versus training from scratch, model size balancing capability and compute (7B to 70B+ parameters), context window requirements (8K to 200K tokens), multimodal needs (text-only versus vision/audio), and deployment constraints (cloud, on-premise, edge). Data preparation curates training corpora from enterprise sources (documents, wikis, code repositories, customer interactions) ensuring quality, relevance, and compliance. LLM training infrastructure provisions compute (hundreds to thousands of GPUs), implements distributed training (data parallelism, model parallelism, pipeline parallelism), monitors training metrics (loss curves, validation perplexity), and optimizes hyperparameters (learning rate, batch size, sequence length). Post-training alignment includes instruction tuning teaching models to follow commands reliably, RLHF (Reinforcement Learning from Human Feedback) aligning outputs with preferences, safety training preventing harmful outputs, and evaluation across benchmarks measuring reasoning, knowledge, and task performance.
Deployment and operationalization deliver production-ready private LLM systems. Inference optimization implements quantization (reducing precision from FP16 to INT8/INT4 cutting costs 50-75%), flash attention accelerating generation 3-5x, KV caching reducing latency for multi-turn conversations, and continuous batching maximizing GPU utilization. On-premise infrastructure deploys models on enterprise hardware (NVIDIA DGX, Lambda Labs, custom clusters) or private cloud (AWS VPC, Azure Private Cloud, GCP VPC) ensuring data never leaves organizational control. Air-gapped systems for maximum security operate completely disconnected from internet. Model serving provides APIs for applications, SDKs for developers, and integration with enterprise systems. Monitoring tracks performance metrics (latency, throughput, accuracy), detects drift (model degradation over time), and captures feedback improving models. Security implements authentication, authorization, encryption, and audit logging. Our custom LLM development transforms generative AI from external service dependency into owned strategic asset delivering competitive advantage through proprietary intelligence, complete data control, and sustained innovation.
Comprehensive Custom LLM Development
Our enterprise LLM solutions cover domain-specific training, private deployment, on-premise infrastructure, data sovereignty, and complete lifecycle management.
Domain-Specific LLM Training & Development
Train large language model for business incorporating proprietary knowledge, industry terminology, and specialized workflows through custom LLM development achieving 90% accuracy on domain tasks versus 60% for general models. Our domain-specific LLM training fine-tunes foundation models (Llama 3, Mistral, Falcon) on curated enterprise data or trains models from scratch for maximum customization. Process includes data curation from company sources (documents, wikis, code, interactions), dataset preparation (cleaning, formatting, quality filtering), tokenizer customization for domain vocabulary, model architecture selection (7B to 70B+ parameters), distributed training infrastructure (data/model/pipeline parallelism), hyperparameter optimization, instruction tuning teaching task performance, RLHF alignment with preferences, and comprehensive evaluation across benchmarks. Medical LLMs understand clinical terminology achieving 95% accuracy on medical QA. Legal LLMs interpret contracts and regulations. Financial LLMs analyze reports and perform specialized reasoning. Technical LLMs generate code and documentation. Our large language model development delivers competitive advantage through proprietary AI understanding your business better than any general model.
- Foundation model fine-tuning (Llama, Mistral)
- Training from scratch
- Custom tokenizer development
- Domain data curation
- Distributed training infrastructure
- Instruction tuning & RLHF
- Industry-specific models (medical, legal, finance)
- Performance optimization
Private LLM Deployment & Data Sovereignty
Ensure complete data control through private LLM deployment for corporate data eliminating exposure to public API providers. On-premise LLM development deploys models on enterprise infrastructure (private data centers, private cloud VPCs) or air-gapped systems for maximum security ensuring enterprise LLM without data leakage. Benefits include data sovereignty (data never leaves organization), zero exposure risk (no training on customer queries by API providers), compliance satisfaction (GDPR data residency, HIPAA PHI protection, financial regulations), intellectual property protection (proprietary information, trade secrets, unreleased products remain secured), and independence from provider changes (model availability, pricing, terms of service). Deployment options include on-premise hardware (NVIDIA DGX, Lambda Labs clusters, custom GPU servers), private cloud (AWS VPC, Azure Private Cloud, GCP Private Network), hybrid (sensitive on-premise, general cloud), and air-gapped (completely disconnected for defense/government). Infrastructure includes model serving, API gateways, load balancing, monitoring, security controls, and disaster recovery. Our private LLM deployment delivers security and control required for sensitive enterprise applications.
- On-premise hardware deployment
- Private cloud infrastructure
- Air-gapped systems
- Zero data leakage
- GDPR/HIPAA compliance
- IP protection
- Complete infrastructure control
- Provider independence
Enterprise Knowledge Base LLM Integration
Build custom LLM for enterprise knowledge base combining proprietary LLM development with RAG (Retrieval Augmented Generation) enabling AI to access organizational knowledge securely. Architecture integrates private LLM with enterprise content (SharePoint, Confluence, databases, file systems) through vector databases deployed on-premise ensuring documents never leave organizational control. System chunks documents, generates embeddings, performs semantic search, retrieves relevant context, and provides to custom LLM grounding responses in corporate knowledge. Applications include employee assistance (answering HR, IT, policy questions), customer support (product documentation, troubleshooting), research (synthesizing internal reports, analyses), compliance (interpreting regulations, procedures), and decision support (providing context from past decisions, best practices). Benefits include accuracy through domain-specific LLM understanding terminology, grounding preventing hallucinations, currency through updated knowledge base, security through private deployment, and scalability serving entire organization. Our enterprise knowledge base solutions democratize institutional knowledge through AI-powered access delivering 10x productivity improvement while maintaining complete data sovereignty.
- Private RAG architecture
- On-premise vector databases
- Enterprise content integration
- Semantic search
- Document processing
- Citation tracking
- Access controls
- Real-time knowledge updates
LLM Fine-Tuning & Customization Services
Customize foundation models through LLM fine-tuning adapting pretrained models (Llama 3 70B, Mistral Large, Falcon 180B) to enterprise requirements achieving 95% accuracy on domain tasks with 90% less compute than training from scratch. Fine-tuning approaches include full fine-tuning updating all parameters for maximum customization, LoRA/QLoRA (parameter-efficient methods updating 0.1% of weights reducing compute 90%), adapter-based tuning adding small learned modules, prompt tuning optimizing soft prompts, and instruction tuning teaching specific task patterns. Applications include brand voice adaptation matching tone and style, terminology learning incorporating company vocabulary, task specialization optimizing for summarization/extraction/classification, format compliance ensuring output structure, safety alignment preventing harmful outputs, and bias mitigation improving fairness. Process includes dataset creation (1K-100K examples depending on task), data quality assurance, training execution monitoring metrics, validation preventing overfitting, evaluation across test cases, and deployment optimization. Benefits include faster results (2-4 weeks versus 3-6 months training from scratch), lower costs ($10K-50K versus $500K+), maintained general capabilities while adding specialization, and flexibility updating models as requirements evolve.
- Full fine-tuning
- LoRA/QLoRA (90% cost reduction)
- Instruction tuning
- Brand voice adaptation
- Task specialization
- Safety alignment
- Dataset creation
- Performance evaluation
LLM Training Infrastructure & MLOps
Build and operate large language model development infrastructure supporting custom LLM training at scale. Training infrastructure includes GPU clusters (hundreds to thousands of NVIDIA A100/H100 GPUs), distributed training frameworks (DeepSpeed, Megatron-LM, PyTorch FSDP) implementing data parallelism (distributing batches), model parallelism (sharding model across GPUs), pipeline parallelism (processing different stages concurrently), high-performance networking (InfiniBand, RoCE), distributed storage (parallel file systems, object storage), and orchestration (Kubernetes, Slurm). MLOps capabilities include experiment tracking (MLflow, Weights & Biases), hyperparameter tuning (Ray Tune, Optuna), training monitoring (TensorBoard, custom dashboards), checkpoint management, model versioning, automated evaluation, CI/CD for model updates, and deployment pipelines. Cloud options include AWS (SageMaker, EC2 P4/P5), Azure (ML, NC-series VMs), GCP (Vertex AI, A2/A3 VMs), or specialized providers (Lambda Labs, CoreWeave, Nebius). On-premise infrastructure leverages enterprise hardware maximizing existing investments. Our infrastructure management delivers reliable, efficient, cost-effective large language model development enabling enterprise AI innovation.
- GPU cluster management
- Distributed training (DeepSpeed, Megatron)
- Data/model/pipeline parallelism
- High-performance networking
- Experiment tracking
- Hyperparameter tuning
- Cloud & on-premise infrastructure
- MLOps automation
LLM Inference Optimization & Deployment
Deploy custom LLMs efficiently through inference optimization reducing latency 60% and costs 70% while maintaining quality. Optimization techniques include quantization (converting FP16 to INT8/INT4 reducing memory 50-75%), KV caching (storing attention keys/values reducing repeated computation), flash attention (optimized attention mechanism 3-5x faster), continuous batching (combining requests maximizing throughput), speculative decoding (using small model to speed large model generation), and model pruning (removing less important weights). Deployment infrastructure includes model serving frameworks (vLLM, TensorRT-LLM, Text Generation Inference), API gateways (authentication, rate limiting), load balancers (distributing traffic), auto-scaling (adjusting capacity dynamically), monitoring (latency, throughput, errors), caching (storing frequent responses), and multi-model routing (selecting optimal model per request). Hardware acceleration leverages NVIDIA GPUs (A100, H100, L40S), custom inference chips (Groq, Cerebras), or CPU inference for smaller models. Result: production-grade custom LLM deployment achieving sub-second latency, millions of daily requests, and cost-effective operation supporting enterprise-scale applications.
- Quantization (INT8, INT4)
- Flash attention optimization
- KV caching
- Continuous batching
- Model serving (vLLM, TensorRT-LLM)
- Load balancing & auto-scaling
- Sub-second latency
- Cost optimization (70% reduction)
Secure LLM Development & Compliance
Ensure security and compliance through secure LLM development implementing comprehensive controls protecting data and meeting regulations. Security measures include data encryption (transit and rest), access controls (authentication, authorization, RBAC), network isolation (VPCs, firewalls, private endpoints), secrets management (credential vaults), audit logging (all operations tracked), vulnerability scanning, penetration testing, and incident response procedures. Compliance frameworks include GDPR (data residency, right to deletion, data processing agreements), HIPAA (PHI protection, business associate agreements, audit controls), SOC 2 (security, availability, confidentiality), ISO 27001 (information security management), financial regulations (data retention, access controls), and government requirements (FedRAMP, IL4/IL5 for defense). Data governance establishes policies for data collection, storage, usage, and retention. Model governance implements review, approval, and monitoring processes. Privacy-preserving techniques include federated learning (training without centralizing data), differential privacy (adding noise protecting individuals), and synthetic data (training on artificial data). Our secure LLM development enables compliant AI deployment in regulated industries (healthcare, finance, government, legal) protecting organizations and customers.
- Data encryption & access controls
- Network isolation
- Comprehensive audit logging
- GDPR/HIPAA/SOC 2 compliance
- Penetration testing
- Data governance
- Privacy-preserving techniques
- Incident response
LLM Evaluation & Benchmarking
Measure custom LLM performance through comprehensive evaluation and benchmarking ensuring models meet quality, accuracy, and safety requirements. Evaluation approaches include automated benchmarks (MMLU, HellaSwag, TruthfulQA, HumanEval measuring general capabilities), domain-specific tests (custom test sets for industry tasks), human evaluation (expert review of outputs), A/B testing (comparing model variants), regression testing (ensuring updates don't degrade performance), and adversarial testing (identifying failure modes). Metrics include accuracy (correct responses), relevance (appropriate answers), coherence (logical flow), factuality (truthfulness), safety (harmful content absence), bias (fairness across groups), latency (response time), and cost (inference expense). Benchmarking compares custom LLM versus general models (GPT-4, Claude), competitor models, and baseline systems quantifying improvements. Continuous evaluation monitors production performance detecting drift (degradation over time) and identifying improvement opportunities. Reports document capabilities, limitations, and appropriate use cases. Our LLM evaluation ensures reliable, safe, effective custom models meeting enterprise quality standards enabling confident deployment supporting business-critical applications.
- Automated benchmarks (MMLU, HellaSwag)
- Domain-specific testing
- Human evaluation
- A/B testing
- Safety & bias assessment
- Performance comparison
- Continuous monitoring
- Capability documentation
Continuous LLM Improvement & Maintenance
Maintain and improve custom LLMs through continuous learning, monitoring, and optimization ensuring sustained value. Monitoring tracks performance metrics (accuracy, latency, throughput, errors), quality indicators (user satisfaction, task completion), drift detection (model degradation identifying when retraining needed), and cost metrics (compute expenses, ROI). Feedback loops capture user corrections, ratings, and behaviors training improved models. Continuous learning implements online learning (incremental updates from new data), active learning (strategically requesting labels for informative examples), and periodic retraining (full model updates incorporating accumulated feedback). Update cadence balances freshness (keeping current) with stability (avoiding disruption). Model versioning tracks changes enabling rollback if issues arise. A/B testing validates improvements before full deployment. Documentation updates reflect capability changes. Infrastructure maintenance includes security patching, dependency updates, performance tuning, and capacity planning. Our continuous improvement ensures custom LLMs evolve with business needs, maintain performance, incorporate new knowledge, and deliver increasing value adapting to changing requirements and opportunities.
- Performance monitoring
- Drift detection
- Feedback loops
- Continuous learning
- Periodic retraining
- A/B testing
- Model versioning
- Infrastructure maintenance
Custom LLM Strategy & Consulting
Guide enterprise LLM adoption through strategic consulting ensuring optimal approach, architecture, and implementation. Assessment evaluates use cases identifying high-impact opportunities, analyzes data availability and quality determining training feasibility, reviews compliance requirements (GDPR, HIPAA, industry regulations), examines infrastructure constraints (compute, network, storage), and assesses organizational readiness (skills, processes, culture). Strategy development recommends build-versus-buy decisions (custom training versus fine-tuning versus API), architectural approach (model size, deployment location, integration pattern), roadmap prioritizing initiatives (quick wins, strategic investments), and budget with timeline estimates. Risk analysis identifies technical challenges (data scarcity, evaluation difficulty), organizational obstacles (skills gaps, adoption barriers), and mitigation strategies. Vendor selection evaluates foundation models (Llama, Mistral, Falcon), training platforms (cloud providers, specialized vendors), tools (frameworks, MLOps platforms), guiding optimal choices. Our consulting expertise helps organizations navigate custom LLM complexity making informed decisions avoiding costly mistakes enabling successful implementation delivering business value through strategic AI investments.
- Use case assessment
- Data readiness evaluation
- Compliance requirements analysis
- Build vs buy recommendations
- Architecture design
- Roadmap development
- Risk analysis
- Vendor selection guidance
Build Proprietary AI with Custom LLM Development
Domain-Specific Training • Private Deployment • Data Sovereignty • Zero Leakage
Partner with enterprise LLM experts delivering custom LLM development and private LLM deployment achieving 40% accuracy improvement through domain-specific training while ensuring 100% data sovereignty. Whether training large language model for business, deploying custom LLM for enterprise knowledge base, implementing on-premise LLM development, or ensuring enterprise LLM without data leakage, we combine deep AI expertise with security focus delivering proprietary LLM development and large language model development protecting intellectual property while leveraging AI's transformative power.
Why Choose Our Custom LLM Development
We deliver production-grade enterprise LLM solutions combining deep AI expertise with security focus ensuring data sovereignty, competitive advantage, and measurable results.
Years AI Expertise
Over 15 years delivering AI solutions including 5+ years training and deploying custom LLMs. Our teams include AI researchers, ML engineers, and infrastructure specialists with deep experience in large language model development, distributed training, and production deployment.
Accuracy Improvement
Our domain-specific LLMs achieve 40% accuracy improvement over general models through focused training on enterprise data. Medical LLMs reach 95% accuracy on clinical tasks, legal LLMs interpret contracts with 90% precision, financial LLMs analyze reports reliably.
Data Sovereignty
Our private LLM deployment for corporate data ensures complete control - data never leaves your infrastructure, zero exposure to API providers, full compliance with GDPR/HIPAA/SOC 2, intellectual property protection, and independence from external dependencies.
End-to-End Capabilities
We deliver complete custom LLM development lifecycle - strategy and assessment, data preparation, model training (fine-tuning or from scratch), evaluation and testing, deployment infrastructure, monitoring and optimization, security and compliance, ongoing maintenance and improvement.
Proven at Scale
Deployed 50+ custom LLMs across industries (healthcare, finance, legal, manufacturing, technology) processing millions of requests monthly. Production experience demonstrates reliability, performance, security, and business value at enterprise scale supporting mission-critical applications.
Infrastructure Expertise
Deep expertise in training infrastructure (GPU clusters, distributed training, cloud/on-premise deployment) and inference optimization (quantization, caching, acceleration) delivering cost-effective large language model development and operation reducing costs 70% through optimization.
Security & Compliance
Comprehensive security controls (encryption, access controls, audit logging, penetration testing) and compliance frameworks (GDPR, HIPAA, SOC 2, ISO 27001) ensure secure LLM development meeting regulatory requirements protecting organizations in highly regulated industries.
Model Ownership
Proprietary LLM development delivers full model ownership - perpetual access without ongoing API fees, protection from provider changes, ability to monetize IP, competitive advantage through unique AI capabilities that competitors cannot replicate or access.
Measurable Business Impact
Our enterprise LLM solutions deliver quantifiable ROI: 40% accuracy improvement, 100% data sovereignty, 70% cost reduction versus APIs at scale, 10x productivity gains, compliance satisfaction, competitive advantage. Every deployment demonstrates clear business value.
Our Custom LLM Development Methodology
We follow systematic approach ensuring successful custom LLM development from strategy through production deployment delivering reliable, secure, high-performance models.
Strategy & Assessment
Custom LLM development begins with comprehensive assessment and strategy. Use case discovery identifies high-value opportunities where custom models deliver advantage. Data assessment evaluates availability, quality, and volume determining training feasibility. Compliance review examines requirements (GDPR data residency, HIPAA PHI protection, financial regulations) informing architecture. Infrastructure analysis reviews compute resources, network capabilities, and deployment constraints. Build-versus-buy analysis compares custom training versus fine-tuning versus APIs determining optimal approach. Architecture design selects foundation model (Llama 3, Mistral, Falcon), model size (7B-70B parameters), training approach (fine-tuning vs from-scratch), deployment location (on-premise, private cloud, hybrid), and integration strategy. This phase produces custom LLM strategy, detailed roadmap, architectural specifications, budget estimates, timeline, and risk mitigation plan ensuring focused execution.
Data Preparation & Curation
Quality training data is foundation for effective custom LLMs. Data collection gathers enterprise sources - documents, wikis, code repositories, customer interactions, historical records, and proprietary databases. Data curation implements cleaning (removing noise, errors, duplicates), filtering (quality thresholds, relevance criteria), formatting (standardizing structure), deduplication (eliminating redundancy), and compliance checking (PII removal, sensitive data handling). Domain-specific corpus development includes terminology extraction, concept identification, relationship mapping, and knowledge structuring. For fine-tuning, dataset creation formats prompt-completion pairs or instruction-input-output examples. Data augmentation expands training sets through paraphrasing, translation, and synthetic generation. Tokenizer customization incorporates domain vocabulary ensuring proper handling of specialized terms. Privacy protection implements anonymization, access controls, and encryption. Result: curated, high-quality dataset enabling effective model training while maintaining security and compliance.
Model Training & Development
Train large language model for business through optimized training process. Infrastructure provisioning secures GPU clusters (A100/H100), implements distributed training frameworks (DeepSpeed, Megatron-LM), and configures high-performance networking. Training execution implements data parallelism (distributing batches across GPUs), model parallelism (sharding model layers), pipeline parallelism (processing different stages), monitoring loss curves and validation metrics, checkpointing regularly, and optimizing hyperparameters (learning rate, batch size, sequence length). Approaches include fine-tuning existing models (faster, cheaper, maintains general capabilities), LoRA/QLoRA for parameter-efficient tuning (90% cost reduction), or training from scratch (maximum customization, proprietary architecture). Post-training optimization includes instruction tuning teaching task performance, RLHF aligning with preferences, safety training preventing harmful outputs, and compression reducing model size. Result: custom LLM achieving target performance on domain tasks.
Evaluation & Validation
Comprehensive evaluation ensures custom LLM meets quality, accuracy, and safety standards. Automated benchmarking measures general capabilities (MMLU, HellaSwag, TruthfulQA) establishing baseline performance. Domain-specific testing evaluates performance on enterprise tasks using curated test sets with ground truth labels. Human evaluation involves domain experts reviewing outputs assessing accuracy, relevance, coherence, and usefulness. Safety testing identifies harmful outputs, bias across demographics, and failure modes. A/B testing compares custom LLM versus general models (GPT-4, Claude) and baselines quantifying improvements. Performance testing measures latency, throughput, and resource utilization. Regression testing ensures updates maintain quality. Results documentation captures capabilities, limitations, appropriate use cases, and performance metrics providing transparency for stakeholders. Evaluation demonstrates custom LLM superiority justifying investment and enabling confident deployment.
Deployment & Integration
Deploy custom LLM through optimized infrastructure and integration. On-premise LLM development provisions hardware (GPU servers, storage, networking) or configures private cloud (AWS VPC, Azure Private, GCP Private). Inference optimization implements quantization (INT8/INT4), flash attention (3-5x speedup), KV caching, and continuous batching maximizing performance. Model serving deploys inference engines (vLLM, TensorRT-LLM, TGI), implements API gateways (authentication, rate limiting), configures load balancers, enables auto-scaling, and establishes monitoring. Integration connects custom LLM to enterprise applications via REST APIs, SDKs, or direct embedding. Security implementation includes encryption, access controls, network isolation, secrets management, and audit logging. Deployment validation confirms performance targets (latency, throughput), security controls, compliance requirements, and integration functionality. Result: production-ready private LLM deployment delivering enterprise-grade reliability, security, and performance.
Monitoring & Continuous Improvement
Post-deployment monitoring and optimization ensure sustained value. Performance monitoring tracks latency, throughput, error rates, and availability through dashboards. Quality monitoring measures accuracy, relevance, and user satisfaction. Drift detection identifies performance degradation triggering retraining. Cost monitoring tracks compute expenses and ROI. Feedback loops capture user corrections, ratings, and behaviors. Continuous learning implements incremental updates, periodic retraining, and A/B testing of improvements. Security monitoring detects anomalies and potential threats. Capacity planning forecasts growth ensuring adequate resources. Infrastructure maintenance includes updates, patches, and optimization. Regular business reviews assess performance, strategic alignment, and evolution needs. Our commitment to continuous improvement ensures custom LLMs deliver increasing value adapting to changing requirements and opportunities maintaining competitive advantage through sustained operational excellence and innovation.
Custom LLM Development Technology Stack
We leverage cutting-edge foundation models, training frameworks, deployment platforms, and infrastructure delivering production-grade enterprise LLM solutions.
Llama 3
Mistral
Falcon
DeepSpeed
Megatron-LM
PyTorch FSDP
Hugging Face
vLLM
TensorRT-LLM
Text Generation Inference
LoRA/QLoRA
PEFT
Ray
MLflow
Weights & Biases
TensorBoard
Infrastructure & Deployment
NVIDIA A100/H100
AWS
Azure
Google Cloud
Kubernetes
Docker
Prometheus
Grafana
Custom LLM Development Pricing
Flexible engagement models fitting your requirements and maturity. All packages include strategy, development, evaluation, deployment, security, and knowledge transfer.
LLM Fine-Tuning
Customize existing models
- Strategy & assessment
- Dataset creation
- LoRA/QLoRA fine-tuning
- Evaluation & testing
- Deployment infrastructure
- 3-4 months timeline
- Training from scratch
- Custom architecture
Custom LLM Development
Domain-specific models
- Comprehensive assessment
- Data curation & preparation
- Model training (fine-tune or scratch)
- Infrastructure setup
- Private deployment
- Security & compliance
- 6-9 months timeline
- 12 months support
Enterprise LLM Program
Strategic AI capability
- Multiple custom models
- Training from scratch
- Custom architecture
- On-premise infrastructure
- Air-gapped deployment
- MLOps platform
- Dedicated AI team
- Long-term partnership
Need Custom Proposal?
Every organization has unique custom LLM requirements. Contact us for tailored proposal including feasibility assessment, architectural design, implementation plan, and transparent pricing for your proprietary LLM development needs.
Request Custom QuoteProven Custom LLM Results
Our enterprise LLM solutions deliver measurable business impact validated through production deployments.
Frequently Asked Questions
Get answers about custom LLM development, private deployment, training requirements, and enterprise implementation.
Ready to Build Your Proprietary AI with Custom LLM Development?
Join enterprises leveraging our custom LLM development expertise achieving 40% accuracy improvement and 100% data sovereignty through domain-specific training and private deployment. Whether fine-tuning foundation models, training large language model for business, building custom LLM for enterprise knowledge base, or implementing on-premise LLM development ensuring enterprise LLM without data leakage, schedule your free strategy session today and discover how proprietary LLM development delivers competitive advantage through owned AI capabilities protecting intellectual property while transforming operations.
✓ 40% accuracy improvement • ✓ 100% data sovereignty • ✓ Zero data leakage • ✓ Full model ownership
Trusted Custom LLM Partner for Enterprise Organizations
Leading enterprises across healthcare, finance, legal, manufacturing, and technology trust ARTEZIO to deliver production-grade custom LLMs. Our expertise in domain-specific LLM training, private deployment, on-premise infrastructure, data sovereignty, security, compliance, and enterprise integration has transformed operations achieving competitive advantage through proprietary AI capabilities protecting intellectual property enabling innovation while maintaining complete data control.
