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Open-Source LLM Development Services
Plavno helps businesses build custom AI products and automation systems using open-source large language models. We design, customize, fine-tune, integrate, and deploy open-source LLM solutions for companies that need more control over AI infrastructure, data privacy, cost, performance, and model customization.
Custom Open-Source LLM Solutions
Build custom AI solutions using open-source LLMs tailored to your product, workflows, data sources, and business goals.
Open-source LLM app development
Custom AI assistant development
LLM-powered SaaS features
Enterprise AI automation systems
Choose the right model strategy, infrastructure approach, and development roadmap for your business use case.
Model selection
AI architecture planning
Build vs. buy evaluation
MVP and PoC roadmap
Deploy open-source LLMs in private cloud, hybrid cloud, or on-premise environments for stronger data control.
Private model hosting
On-premise LLM deployment
Secure inference pipelines
Controlled AI infrastructure
Connect open-source LLMs to company documents, databases, and knowledge bases using retrieval-augmented generation.
Enterprise knowledge search
Document Q&A systems
Source-grounded AI responses
Internal knowledge assistants
Adapt open-source LLMs to your domain, terminology, output style, and business workflows.
Domain adaptation
Instruction tuning
Dataset preparation
Model evaluation
Build AI assistants powered by open-source models for internal teams, customers, and product users.
Customer support assistants
Employee helpdesk bots
Product AI copilots
Research and analytics assistants
Design AI infrastructure that balances quality, latency, hosting cost, scalability, and security.
GPU infrastructure planning
Model serving optimization
Inference cost control
Latency optimization
Deploy open-source LLM systems for enterprise environments with governance, monitoring, access control, and compliance-aware architecture.
Enterprise AI assistants
Private RAG platforms
Secure model deployment
AI monitoring and audit logs
Eugene Katovich
Sales Manager
Whether you need a private open-source LLM, a custom AI assistant, a RAG system, a fine-tuned model, or enterprise AI automation, Plavno helps you move from idea to secure production-ready implementation. Our team combines AI engineering, product development, cloud infrastructure, and business process automation to build open-source LLM systems that work in real business environments.
Case Study
Plavno developed an enterprise AI knowledge assistant that helps employees quickly find internal information, policies, and documentation across company systems. The solution centralizes enterprise knowledge access, reduces repetitive support requests, and improves employee productivity through AI-powered search and contextual retrieval.
faster internal information retrieval
Plavno builds intelligent ecommerce chatbot systems that automate customer support, product assistance, order tracking, and sales workflows across retail platforms.
Customer Requests Resolved Automatically
Whom We Serve
Benefits
More Control Over AI Infrastructure:Open-source LLM development gives businesses more control over model hosting, customization, deployment, and data handling.
Better Data Privacy Options:Open-source models can be deployed in private or controlled environments for sensitive workflows and regulated business processes.
Lower Vendor Dependency:Open-source LLM solutions help reduce reliance on a single commercial AI provider and create more flexible AI architecture.
Custom Model Behavior:Fine-tuning and optimization allow open-source LLMs to better match industry terminology, business rules, and workflow-specific output formats.
Cost-Optimized AI at Scale:With the right infrastructure, open-source LLMs can help businesses manage AI costs for high-volume or recurring workflows.
Security Measures
We deploy open-source LLMs in controlled environments to support sensitive data requirements and enterprise security policies.
We configure permissions so users only access the documents, AI features, and model-generated outputs that match their role and authorization level.
We add monitoring, logs, review workflows, and quality checks to track how open-source LLM systems behave in production.
We test open-source LLM systems for prompt injection, hallucinations, unsafe responses, data leakage, and business-rule violations before production launch.
Advanced Tech
Open-source LLMs provide flexible AI capabilities for text generation, summarization, classification, reasoning, and workflow automation.
RAG connects open-source LLMs to verified business documents, data sources, and knowledge bases for more accurate and context-aware outputs.
Vector databases enable semantic search, document retrieval, knowledge indexing, and source-grounded AI responses.
Fine-tuning and optimization help adapt open-source LLMs to domain-specific tasks, reduce latency, and improve output quality.
LLMOps supports model deployment, versioning, evaluation, monitoring, cost control, and continuous improvement.
Frequently Asked Questions
Find answers to your common concerns
Open-source LLM development services include the design, customization, integration, fine-tuning, deployment, and monitoring of AI solutions powered by open-source large language models.
Open-source LLMs can provide more control over deployment, data handling, customization, cost optimization, and vendor independence compared to fully managed commercial AI APIs.
Yes. Plavno can deploy open-source LLMs in private cloud, hybrid cloud, or on-premise environments depending on your security, compliance, and infrastructure requirements.
Yes. Plavno can connect open-source LLMs to internal documents, databases, knowledge bases, cloud storage, CRMs, ERPs, and enterprise tools using secure RAG architecture and API integrations.
Not always. Some use cases can be solved with RAG, prompt engineering, and model orchestration. Fine-tuning is useful when you need stronger domain adaptation, specific output formats, or repeated task performance.
Yes, if they are selected, deployed, secured, monitored, and evaluated properly. Enterprise open-source LLM systems should include access control, audit logs, monitoring, secure infrastructure, and AI safety testing.
The cost depends on model selection, infrastructure, data complexity, fine-tuning needs, integrations, security requirements, and whether you need an MVP, product feature, or full enterprise AI platform.
Yes. Plavno can build multi-model AI systems that combine open-source LLMs with GPT, Claude, Gemini, embedding models, vision models, and other AI technologies depending on your business case.
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