You will get a custom RAG system with vector search and AI-powered answers
Project details
You get a production RAG system built on real infrastructure, not a LangChain tutorial. Your documents are parsed, NLP-analysed (entity extraction, topic modelling, sentiment), and embedded using Nomic Text Embed MoE v2 into a Weaviate vector store with clustering for fast retrieval. The query pipeline connects to your choice of LLM — Ollama (Llama, Gemma), GPT-4, Claude, Gemini, or Mistral — with semantic search, reranking, and source citations on every answer. Optional graph database layer (Neo4j or Kuzu) for relationship-heavy data. Delivered as a FastAPI REST API with documentation, tested against your real data. Data preparation is 30-50% of a RAG project — I handle the hard part: chunking strategy, embedding optimisation, and retrieval tuning so your system gives accurate, grounded answers instead of hallucinations. I have built and operate distributed embedding pipelines across multiple nodes, vector stores with 270K+ indexed objects, and 7-stage NLP analysis pipelines processing 146K+ documents. This is not a wrapper around an API — it is a custom-built retrieval system tailored to your data.
AI Algorithms
Autoencoder, Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
Hugging Face, PyTorch, Word2vecAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 21 days | 45 days |
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 | - | - | - |
Frequently asked questions
About Loki
AI Platform Engineer | Full-Stack Systems, NLP & Multi-Agent AI
East Kilbride, United Kingdom - 8:02 pm local time
What that means in practice:
Full-stack AI platforms — conversational intelligence systems with ABSA, entity extraction, geolocation, 21-emotion sentiment analysis, emotional arc tracking, topic drift detection, and long-term memory via vectorized stores (ChromaDB, Weaviate) with clustering and lateral links. Not toy projects — production systems processing 180k+ messages with real NLP depth.
Document intelligence — multi-pass extraction, cross-referencing, conflict detection, and synthesis pipelines that take raw documents in and produce structured analysis and formatted PDF output. Handles inconsistent formats, OCR, and multi-hundred-page documents.
Multi-agent AI systems — tool-calling agents with self-correction, persistent memory, and orchestration across distributed infrastructure. Unified inference engine spanning 6 AI providers (Claude, OpenAI, Mistral, Vertex AI, Ollama, Puter) with automatic routing and failover.
Web platforms & dashboards — FastAPI backends, real-time UIs, governance dashboards, research tools, and integration gateways. Full deployment with launchd/systemd service management, health checks, and monitoring.
Native iOS apps — SwiftUI applications with AI backends, currently in App Store review.
Infrastructure — 7-node distributed mesh with GPU inference (RTX 4070 Ti SUPER + M1 Ultra 128GB), automated job queuing, cross-node messaging, audit trails, and zero-downtime operation.
I ship working systems with proper validation, logging, and evidence of completion. If you need a platform built — not a prototype, not a notebook — I can deliver.
Steps for completing your project
After purchasing the project, send requirements so Loki can start the project.
Delivery time starts when Loki receives requirements from you.
Loki works on your project following the steps below.
Revisions may occur after the delivery date.
Data ingestion and vector store setup
Ingest your documents using Nomic Text Embed MoE v2 into Weaviate, with NLP analysis, embedding clustering for fast lookup, and optimised chunking
Retrieval pipeline and LLM integration
Build semantic search pipeline with clustered retrieval, connect to Ollama/GPT-4 models, optional Neo4j or Kuzu graph layer for relationship queries