LLM Development Services

Costs depend heavily on the approach. Fine-tuning an existing model for your domain typically runs $15,000-$35,000. Building a RAG system to connect an LLM to your knowledge base ranges from $25,000-$50,000. Full custom LLM applications with end-to-end development cost $50,000-$100,000+. The main cost drivers are data preparation requirements, integration complexity, and accuracy requirements. We provide detailed estimates after understanding your specific use case—no ballpark figures that turn into surprises later.

AI agent development services help businesses build autonomous systems that can reason, plan, and execute multi-step tasks. Unlike simple chatbots, AI agents use LLMs to understand goals, break them into subtasks, use external tools (APIs, databases, browsers), and make decisions based on context. Examples include customer service agents that resolve issues end-to-end, research assistants that synthesize information across sources, and workflow automation agents that handle complex business processes. The development involves architecture design, tool integration, guardrail implementation, and extensive testing for reliability.

Fine-tuning teaches a model new patterns by training it on your data—it changes the model's weights. The knowledge becomes part of the model. RAG (Retrieval-Augmented Generation) keeps the base model unchanged but connects it to an external knowledge base at query time. Fine-tuning works best for learning styles, formats, or domain-specific language. RAG works best when you need current information, citations, or your knowledge base changes frequently. Many production systems use both.

Timeline varies by project type. Fine-tuning projects typically take 4-8 weeks from data preparation through deployment. RAG implementations run 6-12 weeks including knowledge base setup, retrieval optimization, and integration. Complex custom LLM applications can take 3-6 months for full development. The discovery phase takes 1-2 weeks regardless—that's when we determine the right approach and provide accurate timeline estimates for your specific situation.

Yes. When data sovereignty or regulatory requirements prohibit sending data to external APIs, we deploy models on your infrastructure. This works with open-source models like LLaMA or Mistral that don't require external API calls. On-premise deployment requires more compute resources but gives you complete control over your data. We also implement hybrid approaches where sensitive operations happen on-premise while less critical queries use cloud APIs.

Hallucination mitigation is built into our development process. For RAG systems, we implement source verification and confidence scoring—the model only answers from retrieved documents. For fine-tuned models, we use training data quality controls and output validation. All production systems include monitoring for hallucination patterns, human-in-the-loop workflows for high-stakes outputs, and graceful fallbacks when the model isn't confident. Zero hallucination is impossible, but acceptable error rates are achievable.

We work with both proprietary and open-source models depending on your requirements. Proprietary options include GPT-4, Claude, and Gemini—these offer strong performance but require API calls. Open-source options include LLaMA, Mistral, and Falcon—these can be deployed on your infrastructure. Model selection depends on your accuracy requirements, latency constraints, cost targets, and data sensitivity. We often recommend starting with a proprietary model for faster iteration, then moving to open-source if economics or privacy require it.

Yes. LLMs in production need ongoing attention. Models drift, edge cases emerge, and costs need optimization. Our maintenance includes monitoring for quality degradation, prompt optimization based on real usage, model updates as better options become available, and cost management as query patterns evolve. Most clients continue working with us after launch because they need the system to improve over time, not just maintain the status quo.

Security is built into our development process, not bolted on later. This includes input validation to prevent prompt injection, output filtering to prevent data leakage, API authentication and rate limiting, audit logging of all queries and responses, and encryption at rest and in transit. For sensitive deployments, we implement additional measures: private cloud or on-premise deployment, data anonymization pipelines, and compliance-specific controls (HIPAA, SOC 2, GDPR). Security requirements are defined in the discovery phase and inform architecture decisions.

ROI depends entirely on the use case. Customer service automation typically shows 40-70% reduction in ticket volume—calculate your cost per ticket to find the savings. Content generation at scale might enable 10x output without additional headcount. Document processing automation can cut review time by 80%. We help quantify expected ROI during the discovery phase by understanding your current costs and realistic performance expectations. Not every use case has positive ROI—we'll tell you if yours doesn't.

LLMOps (Large Language Model Operations) is the practice of managing LLMs in production—monitoring performance, optimizing costs, updating prompts, and maintaining quality over time. It matters because LLMs don't stay static: model drift occurs, edge cases emerge, API costs can spiral, and user needs evolve. Without proper LLMOps, your AI solution degrades. Our LLM development services include setting up monitoring dashboards, cost alerts, A/B testing for prompts, and automated quality checks that catch issues before users do.

Domain-specific LLM development starts with understanding your industry's unique vocabulary, compliance requirements, and error tolerance. For healthcare, that means HIPAA compliance and medical terminology. For legal, it means citation accuracy and jurisdiction awareness. For finance, it means audit trails and regulatory language. We either fine-tune existing models on your domain data or implement RAG systems that connect to your specialized knowledge bases. The goal is an LLM that speaks your industry's language and understands its constraints—not a generic model wearing a costume.

A good LLM development company has three things: production experience (not just demos), honest communication about what LLMs can and can't do, and full-stack capability to handle everything from model selection to deployment. Watch out for firms that oversell AI capabilities, can't show real production deployments, or treat every problem like it needs a custom model. The best LLM development companies will sometimes tell you that you don't need an LLM at all—that a simpler solution would work better. We've turned down projects where simpler approaches made more sense.