LLM Development Services
LLM development services that go beyond ChatGPT wrappers. We build custom large language model integrations, fine-tuned models, and RAG systems that solve real business problems. Our team combines LLM engineering expertise with honest guidance on what these models can and cannot do in production.
Beyond ChatGPT Wrappers
Most LLM applications are thin wrappers that could be replaced by a well-crafted prompt. We build systems with genuine value: custom knowledge retrieval, domain-specific fine-tuning, production-grade reliability, and architectures that handle what raw APIs cannot.
Common LLM Pitfalls
- ChatGPT wrappers that add little value over the raw API
- LLM apps that hallucinate critical business information
- Generic prompts that produce inconsistent outputs
- No strategy for handling sensitive or proprietary data
Our Approach
- Custom architectures that solve specific business problems
- RAG systems grounded in your actual data sources
- Engineered prompts tested across thousands of edge cases
- On-premise and hybrid deployment options for data control
LLM Development Capabilities
From integration to deployment, we handle the full spectrum of large language model development.
LLM Integration Services
Connect GPT-4, Claude, Llama, Mistral, or other models to your applications. We handle API orchestration, fallback logic, cost optimization, and response caching for production workloads.
LLM Fine-Tuning Services
Custom model training on your domain data. Fine-tuning improves accuracy, reduces costs, and creates models that understand your terminology and context. We handle data preparation, training, and evaluation.
RAG Implementation
Retrieval-augmented generation that grounds LLM responses in your documents. Vector databases, chunking strategies, and semantic search that make models accurate for your specific use case.
Prompt Engineering
Systematic prompt development for consistent, reliable outputs. We test prompts against edge cases, optimize for cost and latency, and create evaluation frameworks for ongoing improvement.
Model Selection Consulting
Guidance on choosing between OpenAI, Anthropic, open-source models, or custom solutions. We evaluate tradeoffs between capability, cost, latency, and data privacy for your requirements.
On-Premise LLM Deployment
Run open-source LLMs within your infrastructure. Full data control, no external API calls, compliance-friendly architecture. We handle model optimization for your hardware.
Model Selection Guidance
Different tasks need different models. We help you choose based on capability, cost, and your specific requirements.
When to Use GPT-4 / Claude
Complex reasoning, nuanced tasks, high-stakes outputs. Higher cost but stronger capability. Good for customer-facing applications where quality matters most.
When to Use Smaller Models
High-volume, well-defined tasks. Classification, extraction, summarization with clear patterns. Lower cost, faster latency, often fine-tunable for your domain.
When to Fine-Tune
Consistent formatting needs, domain-specific terminology, cost optimization at scale. Fine-tuning trades upfront investment for better per-request economics.
When to Build RAG
Answers need to reference your proprietary data. Knowledge bases, documentation, internal wikis. RAG keeps models current and grounded in facts you control.
Security & Privacy
LLM deployments need careful attention to data handling, cost controls, and production reliability.
Data Privacy Options
Choose between cloud APIs with enterprise agreements, private endpoints, or fully on-premise deployment. We architect solutions that match your data classification requirements.
Input/Output Filtering
Validation layers that prevent prompt injection, detect sensitive data leakage, and ensure outputs meet your compliance standards. Guardrails built into the architecture.
Cost Controls
Token budgets, caching layers, and model routing that prevent runaway API costs. Monitoring and alerting so you never get surprised by a bill.
Latency Optimization
Streaming responses, response caching, model selection based on task complexity. User experiences that feel responsive even for complex LLM operations.
Our Development Process
LLM projects fail when teams skip validation. We prototype fast and test assumptions before scaling.
Requirements Analysis
Understand your use case, data sources, accuracy requirements, and constraints. We determine whether LLMs are the right solution and which approach fits.
Architecture Design
Design the system architecture: model selection, RAG vs fine-tuning, deployment model, integration points. Technical decisions based on your specific requirements.
Prototype & Validate
Build a working prototype fast. Test with real data, measure accuracy, validate assumptions. Iterate based on actual performance, not theoretical capabilities.
Production Deployment
Harden for production: error handling, monitoring, cost controls, scaling. Deploy with confidence knowing the system handles edge cases gracefully.
Why Hexmount for LLM Development
LLM engineering requires more than API knowledge. It needs production experience and honest assessment of capabilities.
Beyond Wrappers
We don't build thin UI layers over ChatGPT. We engineer LLM systems that solve problems the raw API cannot: custom knowledge, consistent outputs, domain expertise.
Honest About Limitations
LLMs hallucinate. We tell you when accuracy requirements exceed what current models can deliver. RAG, fine-tuning, or hybrid approaches to mitigate real risks.
Production Engineering
Demo code is easy. Production LLM systems need error handling, cost controls, latency optimization, and graceful degradation. We build systems that run reliably at scale.
Tiger Team Velocity
Working prototypes in weeks, not quarters. Our distributed team delivers LLM solutions while others are still writing requirements documents.
Related Services
Explore our other AI and development services.
AI Development
Custom AI applications, computer vision, NLP, and intelligent automation beyond LLMs.
Learn moreAI Agent Development
Autonomous agents that use LLMs to execute multi-step tasks and complex workflows.
Learn moreEnterprise AI Development
LLM solutions for enterprise requirements: security compliance, on-premise deployment, system integration.
Learn moreReady to Build Your LLM Solution?
Tell us about your LLM project. Custom integration, fine-tuning, RAG implementation, or model selection guidance. We build large language model solutions that deliver real business value.
We choose projects where LLMs are the right solution. Let us assess your use case together.