LLM Integration Services
Integrate Claude, GPT-4o, or Azure OpenAI into your enterprise applications with production-grade reliability, cost controls, and safety guardrails — not demo-quality implementations.
The Challenge
LLM Demos Work. Production LLM Integrations Are Different.
Getting a language model to produce an impressive output in a demo takes an afternoon. Building an LLM integration that works reliably in production — handles edge cases gracefully, fails safely when the model returns unexpected output, stays within cost budgets, and maintains output quality over time — is a fundamentally different engineering challenge. Most LLM integration projects fail in production for predictable reasons: no structured output schema so the model returns unparseable text, no retry and fallback logic so a single API timeout breaks the user flow, no output validation so hallucinated data reaches end users, and no cost monitoring so bills grow uncontrollably. Healthcare LLM integrations add an additional layer: PHI must never leave your BAA-eligible infrastructure, and clinical outputs must be validated before they influence care decisions.
Deliverables
LLM Integration Capabilities
- Claude API integration — Anthropic Claude 3 Opus, Sonnet, and Haiku with prompt engineering optimised for each model tier and use case
- OpenAI GPT-4o and GPT-4 Turbo integration — function calling, structured JSON output, vision (multimodal), and Assistants API with file retrieval
- Azure OpenAI Service deployment — enterprise-grade LLM access with data residency, private endpoints, HIPAA BAA eligibility, and zero data logging
- Structured output engineering — Pydantic/Zod schemas, function/tool calling, JSON mode, and output validation pipelines
- Streaming response implementation — server-sent events, real-time token streaming to frontend, and partial-output error handling
- Prompt engineering and management — system prompt design, few-shot examples, chain-of-thought elicitation, and prompt versioning
- Cost optimisation — model routing (expensive model only when needed), prompt caching, output caching, token budget management
- LLM observability — LangSmith or custom tracing, input/output logging, cost-per-request monitoring, and quality scoring
- Safety and guardrails — input sanitisation, output filtering, PII detection, clinical claim validation, and human escalation triggers
- Multi-modal LLM — document OCR and understanding, medical image analysis, and audio transcription with Whisper
Stack
LLM Technology Stack
Process
LLM Integration Delivery
A clear, predictable engagement model with no surprises.
Use Case & Model Selection
Define the specific task the LLM will perform, the quality bar required, latency constraints, and volume. Select the right model tier — many production use cases work well with Claude Haiku or GPT-4o mini at 90% lower cost than the flagship models.
Prompt Engineering & Evaluation
Build a representative test dataset. Engineer the prompt. Run evaluation against test dataset. Measure quality. Iterate until quality target is met. Define the evaluation framework before building the integration.
Integration Architecture
Design the integration architecture — API client, retry logic, fallback behaviour, output parsing, validation, caching, and observability. For healthcare, design PHI handling and BAA-compliant infrastructure from this stage.
Build, Test & Deploy
Implement the integration. Integration test with representative production data. Load test under expected volume. Deploy with full observability from day one — cost, latency, error rate, and output quality.
FAQ
Frequently Asked Questions
Ready to Integrate LLMs into Your Application?
Book a free 30-minute call. We will scope your LLM integration and define a path to production.
Response within 24 hours · No commitment required