We integrate large language models into your existing software — APIs, databases, internal tools — and ship to production. No boilerplate starters. LLM integration consulting that works with your stack, not against it.
End-to-end large language model integration across four domains. Each engagement is scoped to what your product actually needs.
Connect your documents, databases and internal wikis to an LLM via retrieval-augmented generation. Accurate answers grounded in your data — no hallucinations, no stale context.
Integrate LLM capabilities into your current backend without a rewrite. We wire the model as a service layer your existing endpoints call — typed, versioned, observable.
When off-the-shelf prompting isn't enough: systematic prompt optimization, few-shot dataset curation and supervised fine-tuning on domain-specific corpora.
Route tasks to the right model at the right cost. Build agent pipelines where models collaborate, tools execute and outputs are validated before reaching users.
Four stages. No surprises. Every LLM integration engagement follows the same disciplined path from discovery to live system.
We map your current stack, data sources and pain points. Output: a clear scope doc with what's feasible, what it costs and what to skip.
We design the integration: model choice, retrieval strategy, prompt structure, context management and the monitoring layer before writing a line of code.
We build against your existing codebase. Typed clients, streaming where it helps, structured output, test coverage and a working eval suite.
Ship with observability from day one: latency tracking, cost dashboards, error rates and a feedback loop so the system improves after launch.
No stack fetishism. We work with whatever your team already runs and add the LLM layer that fits.
LLM integration is the process of connecting a large language model — GPT-4o, Claude, Mistral or an open model — to your existing software so it can read context, take actions and return useful output. It's more than calling an API: you need to design prompts, manage context windows, handle retrieval, deal with latency and build guardrails before you have something reliable in production.
We start with an audit of your current stack and data flows. Then we design the integration architecture — choosing the right model, deciding how to pass context and where to add retrieval. We implement against your existing APIs and databases, write automated evals and set up monitoring. Every project follows the same path: Audit → Architecture → Integration → Deploy & Monitor.
RAG (Retrieval-Augmented Generation) retrieves relevant documents at query time and injects them into the prompt — no retraining, data stays fresh, fast to iterate. Fine-tuning updates the model weights on your data — better for stable style, tone or narrow domain tasks where the training corpus doesn't change often. Most production systems start with RAG. Fine-tuning is added when RAG alone can't close the quality gap.
A focused integration — one feature, one model, existing API — typically takes 2–4 weeks from audit to first production deploy. Full RAG pipelines with custom retrieval and monitoring land in 4–8 weeks. Timeline depends on data readiness, access to your codebase and how many approval cycles your organization needs.
Yes. We work with OpenAI, Anthropic, Google, Mistral, Meta (Llama), Cohere and self-hosted open models. We help you choose the right model for cost, latency and capability, and design the integration so you can swap providers without rewriting your application logic.
Project-based engagements start at $8,000 USD for a scoped integration. Ongoing consulting retainers are available for teams that need recurring architecture support, prompt engineering and model evaluations. Write to hola@luz.uy with a brief description of your use case and we'll send a scoped proposal within 48 hours.
Tell us what you're building. We'll scope it and get back within 48 hours.
hola@luz.uy