We connect large language models to your products and workflows with reliable, cost-efficient, production-grade integrations that scale.
Large language models have moved from research curiosities to essential infrastructure for modern applications. From customer-facing chatbots to internal knowledge tools, LLMs unlock capabilities that were impossible just two years ago. But integrating these models into production systems requires more than calling an API endpoint. It demands careful prompt engineering, robust error handling, latency management, and cost controls that keep your application performant and your budget predictable.
Arthiq has integrated LLMs into dozens of production applications, including our own products like InvoiceRunner and Social Whisper. We work with the full spectrum of model providers, selecting the right model for each use case based on quality, speed, and cost tradeoffs. Our experience spans OpenAI GPT-4 and GPT-4o, Anthropic Claude Opus and Sonnet, Google Gemini, and open-source models like Llama and Mistral that can be self-hosted for data-sensitive applications.
Our integration work goes beyond simple API calls. We build the surrounding infrastructure that makes LLM-powered features reliable, including prompt management systems, response caching layers, fallback chains that route to alternative models during outages, and structured output parsing that converts free-form LLM responses into typed data your application can consume safely.
Not every task requires the most powerful model available. A key part of our integration work is model selection and benchmarking specific to your domain. We run your actual data through candidate models, evaluate output quality against your criteria, and measure latency and cost at projected production volumes. This empirical approach prevents the common mistake of defaulting to the most expensive model when a smaller, faster model would perform equally well for your specific task.
For applications that require processing sensitive data, we evaluate self-hosted open-source alternatives like Llama 3 and Mistral that keep your data within your infrastructure. We handle the GPU provisioning, model serving with vLLM or TGI, and performance optimization needed to make self-hosted models viable for production traffic. Many clients end up with a hybrid approach where sensitive operations use self-hosted models while general tasks use hosted API models.
We also consider the long-term implications of model choice. Vendor lock-in to a single provider creates risk, so we design integrations with abstraction layers that allow model swapping without rewriting application code. When a new model launches that offers better price-performance for your use case, switching should take hours, not weeks.
A production LLM integration needs more than working API calls. It needs infrastructure that handles the realities of operating at scale: rate limits, transient failures, variable latency, and evolving model versions. Arthiq builds resilient LLM infrastructure with request queuing, automatic retries with exponential backoff, circuit breakers that prevent cascade failures, and response streaming that provides low-latency user experiences.
Cost management is a critical concern for LLM-powered applications. We implement token budgeting systems that track and limit usage per user, per request, or per time period. Intelligent caching using semantic similarity means identical or near-identical queries hit a cache instead of the model, often reducing API costs by 30 to 50 percent without affecting user experience. We build dashboards that give you real-time visibility into model usage and spending.
For applications with strict latency requirements, we implement speculative execution patterns, response streaming, and pre-computation strategies that minimize perceived wait times. Our integrations consistently deliver end-to-end response times under two seconds for typical queries, even when using the most capable models.
LLMs produce text, but your application needs structured data. A critical part of every integration we build is reliable structured output extraction. We use function calling, JSON mode, and custom parsing with validation layers to convert LLM responses into typed objects that your application can process without errors. When the model produces malformed output, our retry logic with adjusted prompts handles it gracefully.
We integrate LLM capabilities directly into your existing application architecture. For web applications, we build server-side endpoints that manage the LLM interaction and return processed results. For data pipelines, we create batch processing modules that handle large volumes efficiently. For real-time applications, we implement WebSocket-based streaming that delivers tokens as they are generated.
Every integration includes comprehensive testing infrastructure. We build evaluation suites that test LLM outputs against expected results across a diverse set of inputs. These tests run in CI/CD pipelines and alert your team when model updates or prompt changes cause regression in output quality. This systematic approach to quality assurance is what separates production integrations from prototypes.
Our team brings deep experience across the entire LLM ecosystem. We have worked with every major model provider and have the benchmarking data to make informed recommendations for your specific requirements. We do not promote a single vendor; we recommend the approach that delivers the best results for your use case and budget.
As a Singapore-based AI engineering studio, we combine world-class technical skills with a pragmatic delivery approach. We scope projects carefully, deliver in focused sprints, and maintain transparent communication throughout. Our Product Owner mindset means we take full responsibility for the outcome, not just the code we write.
Whether you are adding AI features to an existing product or building an AI-native application from scratch, Arthiq has the expertise to get you to production quickly and reliably. Contact us at founders@arthiq.co to discuss your LLM integration needs.
Our engineers will connect the right language models to your products with production-grade reliability, cost controls, and performance optimization.