Blog/blog/rag-w-produkcji
Production RAG architecture with BGE-M3 and Qdrant
Hybrid dense + sparse retrieval, reranking, and answer quality observability with zero PII leakage.
Search technical articles, deployment services, production engines, glossary terms, FAQ, and interactive calculators in one place.
Hybrid dense + sparse retrieval, reranking, and answer quality observability with zero PII leakage.
How to combine local vLLM models with the OpenClaw gateway, PII masking, and automated fallback.
We start with a single fixed-cost process; typical return on investment occurs within 2-4 months.
Yes: we apply pre-cloud PII masking, zero-retention modes, local BGE-M3 embeddings, and human-gate controls.
Evaluate domain visibility for AI answer engines, JSON-LD structured data, and llms.txt configuration.
Estimate GPU memory (VRAM) requirements, token throughput, and compare self-hosting vs cloud APIs.
Calculate saved hours, deployment cost, and payback month for your team.
Compare 3-year total cost of ownership (TCO) between Cloud LLM APIs and dedicated GPU servers with break-even month calculation.
Map your AI use case to EU Regulation 2024/1689 risk tiers and generate a technical documentation compliance checklist.
A self-hosted embeddings and semantic search service with a dashboard and full retrieval observability.
A memory and knowledge panel for agents: RAG Q&A over your company knowledge, with hundreds of endpoints and BGE-M3 search.
An autonomous code audit, security scanning, and self-healing platform. Multi-agent adversarial verification, 4 SARIF 2.1.0 scanners, and safe fix sessions in isolated git branches.
A pipeline that turns messy documents: PDFs, scans, photos, DOCX, into structured, verifiable data: every field traceable to its source span, with a confidence threshold.
A complete real-estate system: a monorepo, several apps, CRM sync, task queues, and publishing of listings and visualisations.
An evaluation harness for LLM/RAG systems: a golden set, automated metrics, an LLM-as-judge calibrated against human labels, and a regression gate that blocks a deploy when quality drops.
A single controlled entry point for every LLM call: model-to-task routing, PII masking before the model, input and output guardrails, per-tenant budgets, and a full audit log.
Lead generation and scoring grounded in real data: sourcing, dedup, enrichment, and automated campaigns.
One gateway for all your LLM traffic: routing across models, a skills library, and agents, through a single auditable entry point.
Your own system for tracking rankings and visibility: in Google and in model answers. FastAPI + PostgreSQL, with full history.
A phone voice agent grounded in your knowledge base (RAG): it answers routine calls, books appointments, and hands difficult cases to a human with full context.
Optimizing content and structured data for citation visibility in AI model answers and answer engines.
Architecture combining retrieval from a verified knowledge base with answer generation by an LLM.
Serving LLM models on dedicated hardware with vLLM, TensorRT-LLM, Kubernetes, and full GDPR compliance.
We design multi-agent LangGraph and MCP workflows with human-gate oversight and full audit trails.