System StatusOnline
NAV
Fine-tune Llama 3, Mistral, or Phi on your proprietary data with LoRA/QLoRA. Domain-specific model deployed on your infrastructure via vLLM API.
I fine-tune open-source LLMs (Llama 3.1, Mistral 7B, Phi-3) on your proprietary data using LoRA/QLoRA for memory-efficient training on A100 GPUs. Fine-tuning produces a model that speaks your domain's language, follows your output format, and outperforms general-purpose LLMs on your specific task—at a fraction of the API cost of calling GPT-4 at scale. The trained model deploys on your infrastructure via vLLM or TGI, fully under your control.
Domain-specific model trained on your exact data—outperforms GPT-4 on your specific task at 10–100x lower inference cost per request.
LoRA/QLoRA training—fine-tune a 7B or 13B model on a single A100 GPU in hours, making custom LLMs economically viable for any scale.
Automated evaluation suite with task-specific metrics (accuracy, F1, BLEU, ROUGE, or custom rubrics) to measure improvement objectively.
Deployment-ready inference server (vLLM or TGI) with OpenAI-compatible API—drop-in replacement for your existing GPT-4 calls.
Full model ownership—your fine-tuned weights are yours, deployable on-premise or in your private cloud, with zero ongoing per-token API costs.
I collect, clean, and format your data into instruction-following pairs. I review data quality and flag samples that would teach the model bad behaviors before training begins.
I fine-tune the base model using LoRA on A100 GPUs with continuous loss monitoring, validation metrics, and early stopping to prevent overfitting.
I test the model on a held-out test set using task-specific metrics, compare against the baseline, and iterate on hyperparameters or training data until targets are met.
I deploy the fine-tuned model on vLLM or TGI with an OpenAI-compatible REST API, configure rate limiting and authentication, and document the API for your engineering team.
Minimum 100–500 high-quality examples for instruction following or style adaptation. For complex reasoning tasks, 1,000–10,000 examples produce the best results. Quality always beats quantity—a smaller clean dataset outperforms a large noisy one.
Fine-tune when you need the model to follow a specific output format, write in a specific style, or perform a task type the base model struggles with. Use RAG when you need accurate retrieval of specific facts from a knowledge base. Many production systems use both together.
At scale, dramatically cheaper. A self-hosted 7B model on a single A100 handles 1,000+ tokens/second. At 1M tokens/day, the cost is ~€0.50/day in cloud GPU vs ~€30/day with GPT-4 API—a 60x cost reduction.
More modules inside "AI & Automation" — together they form a complete system.
GPT-4 AI agent resolving 80% of support tickets automatically. 24/7 multi-channel (chat, email, WhatsApp), trained on your knowledge base.
View modulen8n / Make automation for report generation, employee onboarding, invoice processing, and document analysis. Cut manual work by 70%.
View moduleAI bot qualifying inbound leads 24/7 against your ICP, booking meetings into your calendar, and auto-populating your CRM. Increases booked demos by 30–60%.
View moduleAI assistant that answers questions using only your internal documents—contracts, policies, manuals. Every answer cited, zero hallucinations.
View moduleProduction RAG pipeline with vector database, hybrid search, and reranking. Search millions of documents in <100ms with semantic accuracy.
View moduleInitiate protocol. Establish connection. Let's build something loud.