LLM development services.
Custom and private large language model development: RAG pipelines over your documents, fine-tuned open models, and GPU deployment on your servers — so you get ChatGPT-class capability without sending a single record to a third party.
Running today
Our own RAG assistant answers from indexed documents at Trooply.AI — Ollama-served open models, Qdrant vector search, fully self-hosted.
Custom LLM development, end to end.
RAG development
Retrieval-augmented generation over your documents, tickets, catalogs and wikis — answers grounded in your data with citations, not hallucinations.
Private LLM deployment
Llama-family and other open models served with Ollama/vLLM on your servers or our GPUs — data residency and compliance by architecture.
Fine-tuning & evaluation
Task-specific fine-tuning where it beats prompting, with evaluation harnesses so quality is measured, not vibes-based.
LLM API integration
OpenAI, Anthropic and Gemini integration done right: streaming, caching, fallbacks, cost controls and prompt versioning.
Vector search & embeddings
Semantic search with Qdrant and modern embedding models — the retrieval layer that makes every LLM application accurate.
LLM ops & guardrails
Monitoring, evaluation, prompt-injection defenses, PII filtering and cost dashboards — the operations layer most projects skip and regret.
Why private LLMs win for business.
A private LLM is a large language model that runs on infrastructure you control — your data never leaves your environment, never trains someone else’s model, and never meets a per-token bill that scales against you. For pharma, finance, legal and B2B businesses, this is usually the only architecture that clears compliance review.
In 2026 open models are good enough for the large majority of business tasks — especially when paired with RAG over your own data. We run this stack in production ourselves (Ollama-served models, Qdrant retrieval, GPU inference) and deploy the same architecture for clients, with hosted frontier models reserved for the cases that genuinely need them.
LLM development questions.
What is custom LLM development?
Building large language model applications tailored to your business: models grounded in your data via RAG, fine-tuned for your tasks, integrated with your systems and deployed where you control them. It is application engineering around models — not training a foundation model from scratch, which almost no business needs.
How much does LLM development cost?
A RAG prototype on your documents typically runs $3,000–$8,000; production systems with integrations, guardrails and monitoring from $10,000–$40,000. Self-hosted inference then costs server rent, not per-token fees — at volume this is the decisive economic argument.
Do we need our own GPUs?
No — we can host on our RTX GPU infrastructure, deploy to your cloud (AWS/GCP GPU instances), or install on-premise. Many clients start on our servers and migrate in-house once the system proves out.
Which models do you work with?
Open models: Llama family, Mistral, Qwen and task-specific models served via Ollama/vLLM. Hosted: OpenAI, Anthropic Claude and Gemini. We benchmark on your actual tasks and choose per use-case.
Want an LLM that knows your business?
Bring us a folder of documents and a use-case — we will show you a working private assistant on your data in weeks.