You will get RAG System Design & Implementation
Project details
I design and build production-grade RAG pipelines with hybrid retrieval, evaluation harnesses, and cloud deployment.
What you get:
Hybrid retrieval: dense vector (pgvector/Qdrant/Weaviate) + BM25 + RRF fusion
Cross-encoder reranking for precision at the top-k level
RAGAS evaluation suite (faithfulness, context precision, context recall)
FastAPI backend, Redis caching, Prometheus/Grafana observability
Deployed to GCP Cloud Run, P95 latency gated at 800ms
Not just retrieval — grounded generation with measurable quality gates.
GraphRAG add-on: +$4,000 / +3 weeks
For relational domains where answers require connecting multiple documents (org charts, legal precedents, code dependencies, knowledge bases). Adds Neo4j schema design, entity extraction pipeline, 6-stage multi-hop retrieval, and GNN re-scoring.
What you get:
Hybrid retrieval: dense vector (pgvector/Qdrant/Weaviate) + BM25 + RRF fusion
Cross-encoder reranking for precision at the top-k level
RAGAS evaluation suite (faithfulness, context precision, context recall)
FastAPI backend, Redis caching, Prometheus/Grafana observability
Deployed to GCP Cloud Run, P95 latency gated at 800ms
Not just retrieval — grounded generation with measurable quality gates.
GraphRAG add-on: +$4,000 / +3 weeks
For relational domains where answers require connecting multiple documents (org charts, legal precedents, code dependencies, knowledge bases). Adds Neo4j schema design, entity extraction pipeline, 6-stage multi-hop retrieval, and GNN re-scoring.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$2,500
|
Standard
$5,000
|
Advanced
$9,000
|
|---|---|---|---|
| Delivery Time | 10 days | 21 days | 42 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | ||
Model Testing & Optimization | - | ||
Model Tuning | - | - | |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$400 - $1,500
Additional Revision
+$250
Multimodal Ingestion
(+ 3 Days)
+$600
DSPy NL→SQL Layer
(+ 4 Days)
+$800
OpenSearch Backend
(+ 2 Days)
+$500Frequently asked questions
About Sergiu
AI Engineer | LLM & Agentic Systems | GraphRAG | Neo4j | MCP | A2A
Timisoara, Romania - 11:53 pm local time
I design and deploy reliable LLM-powered applications with deterministic orchestration, persistent memory and rigorous evaluation. My core expertise is building multi-agent systems using LangGraph and AutoGen, with strong focus on stateful orchestration, tool integration and reproducibility.
My flagship project is a knowledge graph platform on Neo4j with contradiction detection, document authority hierarchies, alias deduplication, ontology enforcement and graph health monitoring — treating graph integrity as a first-class engineering concern. Measured, not claimed: 0.940 RAGAS faithfulness on a golden set, 2.2s p95 retrieval latency, 364 passing tests. I also build end-to-end RAG pipelines with hybrid retrieval and grounded generation, and designed a reproducible benchmarking framework evaluating recall–latency–throughput trade-offs across vector search engines (Qdrant, Elasticsearch, pgvector, Redis).
With 10+ years of enterprise engineering experience operating 10M+ user platforms at 99.9% uptime (Alcatel-Lucent), I bring the same discipline to AI systems: observability, CI/CD and cloud-native deployment. All AI engineering work is self-directed and independently deployed — built to enterprise standards from day one.
Core stack: Gemini • GPT • Claude • Llama • LangGraph • AutoGen • CrewAI • Google ADK • LangChain • LlamaIndex • PydanticAI • DSPy • Haystack • Semantic Kernel • FastAPI • gRPC • Python • PyTorch • TensorFlow • Scikit-learn • React • TypeScript • PostgreSQL • pgvector • Neo4j • Qdrant • Milvus • Weaviate • Pinecone • FAISS • OpenSearch • Docker • Kubernetes • Google Cloud Run • Vertex AI • AWS SageMaker • Redis • RabbitMQ • OpenTelemetry • Langfuse • LangSmith • Prometheus • Grafana • Jaeger • RAGAS • LLM-as-a-Judge • MCP • A2A • HITL • Deepgram • sentence-transformers • TimescaleDB • Alembic • Claude Code • Cursor • RRF • BM25 • Cross-encoder reranking • OWL • SPARQL • Cypher • JWT • OIDC • ABAC • WebSocket • gRPC-Web • Envoy • GitHub Actions • CI/CD
Steps for completing your project
After purchasing the project, send requirements so Sergiu can start the project.
Delivery time starts when Sergiu receives requirements from you.
Sergiu works on your project following the steps below.
Revisions may occur after the delivery date.
Intake & Architecture Design
Share your documents, data sources, and use case. I define retrieval strategy, chunking approach, embedding model selection, and vector store choice.
Pipeline Build
Hybrid retrieval pipeline built and integrated — ingestion, embedding, vector indexing, BM25, RRF fusion, reranking, and FastAPI backend.