LLM Integration Services
OpenAI, Claude, Gemini & Open-Source
Embed GPT-4, Claude 3.5, Gemini, Mistral, or Llama 3 into your product — with production-grade prompt engineering, streaming, cost controls, and observability built in from day one.
Brillnex Systems is a Karachi-based LLM integration company that has shipped these features into real SaaS products. We do not prototype — we integrate, test, and deploy.
TL;DR
LLM integration = connecting your product to GPT-4, Claude, Gemini, or an open-source model so it can generate, classify, summarise, or reason. Brillnex handles the API layer, prompt design, RAG pipelines, streaming, and cost controls. Offshore rates, 2–12 week delivery depending on scope.
What LLM Integration Includes
Scope varies per project. Common deliverables across our LLM integration engagements:
- API integration with OpenAI, Anthropic, Google Gemini, or open-source models
- Prompt engineering and system prompt design for your use case
- Streaming response implementation (real-time output to the UI)
- Function calling and tool-use setup for structured outputs
- Context window management and token optimisation
- Semantic caching and prompt caching for cost reduction
- Model routing — cheap models for simple tasks, powerful models for complex ones
- LLM observability: logging, tracing, latency monitoring with LangSmith or Helicone
Why Choose Brillnex for LLM Integration?
Model-agnostic
We do not favour one vendor. We recommend the right model for your use case — and we can swap models without rebuilding your integration if a better option launches.
Production-first
Every integration includes error handling, fallbacks, rate-limit management, and observability. We build for production from day one, not as an afterthought.
Offshore pricing
A production LLM integration that runs $20k–$35k at a US agency is $8k–$15k with Brillnex — senior engineers at 40–60% less, no juniors learning on your project. Fixed-scope, so you know what you are paying before we start.
Frequently Asked Questions
What is LLM integration?
LLM integration is the process of connecting a large language model (like GPT-4, Claude, or Gemini) to your software product so it can generate text, answer questions, analyse data, or automate reasoning tasks. It differs from building an LLM — you are using an existing model via API and engineering the connection to your system.
Which LLMs do you integrate with?
We integrate with: OpenAI (GPT-4o, GPT-4 Turbo), Anthropic (Claude 3.5 Sonnet, Haiku, Opus), Google (Gemini 1.5 Pro, Flash), Mistral AI, Meta Llama 3 (via Ollama or Together AI), and other open-source models. As of 2026, we recommend Claude 3.5 Sonnet for most production use cases given its cost-to-performance ratio.
What is the difference between LLM integration and fine-tuning?
LLM integration connects a pre-trained model to your product via API — using prompt engineering, RAG, and function calling to tailor its behaviour. Fine-tuning trains a model on your specific data to change its baseline behaviour. For most B2B use cases, RAG-based LLM integration is faster, cheaper, and more maintainable than fine-tuning.
How do you handle LLM cost at scale?
We implement prompt caching, semantic caching, model routing, context compression, and fallback chains. For high-volume use cases, we often reduce API costs by 40–70% through these techniques.
How much does LLM integration cost?
LLM integration at Brillnex starts from $4,000 for a single-model integration and ranges to $30,000+ for multi-model orchestration with RAG and observability. Offshore pricing means these numbers are 40–60% below US or UK agency rates. We scope every project with fixed pricing so you know the cost before we start.
How long does LLM integration take?
A basic single-model integration with streaming and error handling takes 1–3 weeks. Adding RAG pipelines over your data adds 2–4 weeks. Full multi-model orchestration systems with observability take 8–16 weeks. We scope all projects before starting.
Ready to embed an LLM into your product?
Book a free scoping call. We'll walk through your stack, your use case, and the right model for the job — before any commitment.
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