Your LLM prototype works in a notebook but breaks in production?
Model responses are inconsistent -- the same question gives different answers?
LLM Integration Services
Language models are powerful general tools. Making them powerful for your specific business requires integration work that most dev teams underestimate.
We build LLM integration layers that connect language models to your data, your APIs, and your user workflows -- with the prompt engineering, context management, and output handling that makes the difference between a demo and a production system.
OpenAI, Anthropic, Gemini, Llama, and Mistral integrations
Production-grade: rate limiting, fallbacks, token management, and monitoring
RAG pipelines, function calling, and structured output built to your spec
20+ LLM-powered products shipped to production
RaftLabs builds production-grade LLM integrations — RAG pipelines, function calling architectures, structured output extraction, and multi-step AI agents — for businesses that need language model capabilities running reliably in production, not just in a demo. We integrate with OpenAI, Anthropic (Claude), Gemini, Llama, and Mistral, and have shipped 20+ LLM-powered products to production. Every integration includes prompt engineering, failure handling, output validation, and monitoring.
The gap between LLM prototype and production system
Every LLM integration looks easy at first. You call the API, you get a response, the demo works. Then you try to run it at scale: responses are inconsistent, the model ignores instructions, token costs add up, the API times out under load, and someone asks the model something it shouldn't answer and it does anyway.
Production LLM integration is an engineering problem, not just an API call. The systems that run reliably in production are the ones where someone thought carefully about prompt design, failure handling, output validation, and monitoring before writing the first line of application code.
What we build
RAG pipelines
End-to-end retrieval-augmented generation: document ingestion, embedding generation, vector store indexing, semantic search, context assembly, and response generation. Built with your data sources -- PDFs, databases, APIs, websites. Production-grade, not a notebook proof-of-concept.
Function calling and tool use
Language models that call your APIs and use your tools to complete tasks. An AI assistant that can look up a customer record, create a support ticket, send a message, or run a database query -- all from a natural language instruction. We design the function signatures, implement the tool handlers, and build the orchestration layer.
Structured output extraction
LLMs that reliably produce structured JSON output that feeds into your application data model. Contract data extraction, form auto-fill, entity recognition, classification -- output that's parsed, validated, and ready to store without manual intervention. We use JSON mode, tool calling, and schema validation to make this reliable.
Multi-step AI agents
AI agents that reason through multi-step tasks: research a topic, summarise findings, draft a document, and send it for review -- all from a single instruction. We build the planning loop, the tool integrations, the state management, and the human-in-the-loop checkpoints.
Prompt engineering and optimisation
System prompts engineered for consistency, accuracy, and cost efficiency. We write, test, and evaluate prompts against your specific use cases -- measuring output quality with defined metrics, not just "looks good to me." We version-control your prompts like code and track changes against production metrics.
LLM evaluation and monitoring
Frameworks for measuring whether your LLM integration is working correctly in production -- and detecting when it degrades. We build evaluation datasets, automated quality checks, and production monitoring that catches problems before your users do.
Have an LLM integration that's not working the way it should?
Tell us what you're trying to do and where it's breaking down. We'll diagnose the problem and fix it.
Frequently asked questions
LLM (Large Language Model) integration is the process of connecting a language model API to your application, data, and workflows in a production-ready way. This includes designing prompts that produce consistent output, building retrieval systems so the model can use your data, handling rate limits and failures gracefully, parsing and validating model output, and monitoring the system in production. It's the engineering work between "the API works" and "this is running reliably in production."
We've built production integrations with GPT-4o and GPT-4 Turbo (OpenAI), Claude 3.5 Sonnet and Claude 3 Haiku (Anthropic), Gemini 1.5 Pro and Flash (Google), Llama 3.1 8B, 70B, and 405B (Meta/Groq), Mistral Large and Mixtral (Mistral AI), and Cohere Command R+. Model selection depends on the use case -- we recommend based on context window, cost, latency, and reasoning requirements.
RAG (retrieval-augmented generation) is a pattern where the model retrieves relevant information from your data before generating a response. Instead of relying on what the model learned during training, it looks up the relevant documents, database records, or knowledge base articles for the specific query, then uses that retrieved context to generate an accurate, source-backed response. You need RAG when your application requires accurate information about your specific business, products, or data that the model wouldn't otherwise know.
Inconsistency is the primary production challenge with LLMs. We address it through: structured output modes (JSON schema enforced by the model or validated by a parsing layer), few-shot examples in the system prompt that show the model exactly what format you want, output validation that retries the call with corrected instructions when the format is wrong, and temperature and sampling settings tuned for your task (lower temperature for factual extraction, higher for creative tasks).
LLM latency is real -- a GPT-4 call can take 10--30 seconds for long outputs. We design around it: streaming responses that show output as it's generated (so users see something immediately), caching for deterministic queries that always return the same answer, smaller/faster models (Claude Haiku, GPT-4o Mini, Gemini Flash) for latency-sensitive tasks, and async processing for tasks where real-time response isn't required. We profile latency during build and design the UX around it.
We instrument LLM integrations with request and response logging (with PII scrubbing where required), latency and error rate tracking, token usage monitoring (for cost management), model version tracking, and output quality sampling. We use LangSmith, Langfuse, or custom logging depending on the scale and complexity of the integration. You can see what the model is doing, what it costs, and where it's failing.