LLM Integration Services
Adding a large language model to your product sounds simple — until you're dealing with prompt engineering, streaming, cost optimization, hallucination mitigation, and production reliability. SaTekk handles the full LLM integration layer: model selection, API setup, prompt architecture, output parsing, error handling, and observability. We've integrated GPT-4o, Claude, Gemini, Llama, and Mistral into production SaaS products and enterprise workflows across every industry.
What our LLM integration covers
Model Selection & Architecture
We analyze your use case, latency requirements, and budget to recommend the optimal model — and build the right prompting architecture around it.
API Integration & SDK Setup
Clean integration with OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, or any LLM provider — with proper error handling and retry logic.
Structured Outputs & Function Calling
Reliable structured JSON outputs and tool/function calling so your LLM integrates predictably with downstream systems and databases.
Prompt Engineering & Guardrails
Production-hardened system prompts, input validation, output filtering, and safety guardrails that minimize hallucinations and off-topic responses.
Cost Optimization
Token budgeting, prompt caching, model routing (cheap model first, expensive model for complex queries), and usage monitoring to control your API spend.
Observability & Evals
LLM tracing with Langfuse or Helicone, automated eval pipelines, and dashboards so you can monitor quality and cost in production.
Why SaTekk
Frequently asked questions
Which LLM should I use for my product?+
It depends on your task. GPT-4o and Claude 3.5 Sonnet excel at reasoning and instruction-following. Gemini 1.5 Pro handles long contexts well. Llama 3 or Mistral are cost-effective for high-volume, on-premise, or privacy-sensitive deployments. We run model benchmarks on your specific tasks during discovery and recommend the best fit.
How do you prevent LLM hallucinations in production?+
We use a combination of retrieval-augmented generation (grounding answers in real data), structured output enforcement, confidence thresholds, and output validation. For high-stakes applications we also add human-in-the-loop checkpoints and automated eval pipelines that flag degraded quality.
How do you manage LLM API costs at scale?+
We implement prompt caching (Anthropic and OpenAI both support this), semantic caching for repeated queries, model cascading (route simple queries to cheaper models like Claude Haiku), token budgeting, and batch processing where latency allows. These strategies typically reduce API costs by 40–70%.
Can you integrate LLMs into our existing codebase?+
Yes. We integrate into any tech stack — Node.js, Python, Go, Ruby, .NET. We work with your existing architecture rather than requiring a rebuild. Typical integration time for a focused feature (summarization, Q&A, extraction) is 1–3 weeks, including testing and production deployment.
Ship your LLM feature in weeks.
Book a free call. We'll tell you which model fits your use case, estimate your API costs, and give you a timeline to ship.
Book Your Free CallOr email hello@satekk.agency