ORVINUSLLM Integration Services
Foundation models are the easy part. We build the engineering around them — routing, evaluation, cost control, and APIs that don't fall over.
LLM integration is the engineering discipline of putting large language models — OpenAI's GPT family, Anthropic's Claude, Google's Gemini — inside a real product: selecting the right model per task, designing the prompt and context architecture, and wrapping it all in APIs with the reliability of any other production dependency.
The difference between a demo and a product is everything around the model call: structured outputs that downstream code can trust, evaluation suites that catch regressions before users do, fallback chains when a provider degrades, token budgeting that keeps unit economics sane, and observability that tells you what the model actually did. That surrounding system is what ORVINUS builds.
We are deliberately model-agnostic. Every integration starts with a benchmark on your actual task — capability, latency, and cost per call — so the model choice is a measured decision you can revisit, not a default you inherited.
LLM integration services connect large language models such as OpenAI GPT, Anthropic Claude, and Google Gemini into production software. ORVINUS provides LLM integration including model selection benchmarking, provider-agnostic architecture, structured outputs, streaming APIs, cost control, and custom AI backend development.
Multi-Provider Model Integration
We integrate all three frontier providers — OpenAI, Anthropic, and Gemini — behind a single abstraction in your codebase, so switching or mixing models is a configuration change, not a rewrite. Task-level routing sends each job to the model that wins on your benchmark: long-context document analysis to one provider, fast classification to another, cost-sensitive bulk work to a smaller tier.
Reliability is engineered in from the first commit: retries with exponential backoff, provider fallback chains for outages, response validation against typed schemas, and rate-limit management that queues instead of failing. Streaming responses, tool calling, and structured JSON outputs are wired to your product's exact needs.
Custom AI APIs & Backend Architecture
Model calls need a home: a backend that authenticates users, enforces quotas, streams responses, logs everything, and scales when a launch goes well. We build that layer — typically FastAPI or Node.js services in front of the model providers — as versioned, documented APIs your frontend, mobile app, or partners consume like any other service.
The architecture handles what naive integrations miss: request queuing under burst load, caching of repeated inference, background processing for long jobs with webhook callbacks, and per-tenant isolation so one customer's usage never degrades another's. Everything ships containerized with monitoring wired in.
Built With
The technologies we reach for on this work — and why we use each one.
See what we use every tool for in the full technology stack.
What You Get
Model selection benchmark
Your task run against GPT, Claude, and Gemini with measured accuracy, latency, and cost — the choice justified in numbers.
Production integration layer
Provider-agnostic abstraction with retries, fallbacks, structured outputs, and streaming — inside your codebase, documented.
AI API backend
Authenticated, metered, versioned endpoints your product and partners consume — containerized and deployed.
Evaluation & cost harness
Prompt regression tests and per-feature cost dashboards so quality and unit economics stay visible after launch.
Other AI MVP Development Specializations
RAG Systems & AI Chatbot Development
Chatbots that answer from your data instead of guessing — retrieval-augmented generation with document intelligence built in.
Explore RAG & ChatbotsAI Copilots & Workflow Automation
Assistants embedded in your product and automations that run your workflows — with analytics dashboards that show the impact.
Explore Copilots & AutomationFull-Stack SaaS Development
The complete product around the AI: frontend, backend, payments, admin, access control, and cloud deployment.
Explore Full-Stack SaaSCommon Questions
Which LLM is best — GPT, Claude, or Gemini?
It depends on the task, which is why we benchmark all three on your actual workload before choosing. Long-context document work, fast classification, and cost-sensitive bulk processing each favor different models — and the right answer changes as providers ship new versions, so we build the integration to be swappable.
How do you keep LLM API costs under control?
Token budgeting is designed in: right-sizing models per task, caching repeated inference, trimming context aggressively, and per-feature cost tracking so you see exactly where spend goes. Most cost blowouts come from using a frontier model for work a smaller tier handles — routing fixes that.
Can you integrate LLMs into our existing backend?
Yes. The integration layer is designed to slot into your existing services — we add the model abstraction, evaluation, and observability around your current architecture rather than forcing a rebuild.
What happens when a provider has an outage?
Fallback chains route requests to an alternate provider or a degraded-but-functional mode automatically. Combined with retries, queuing, and rate-limit handling, provider incidents become latency blips instead of product outages.
Ready for LLM Integration?
Free discovery call — we scope the work, name the trade-offs, and respond within 24 hours.