Preciso que você desenhe e entregue um fluxo completo em n8n que centralize meu atendimento de WhatsApp Business e, ao mesmo tempo, converse com outro canal chamado “Z-pro”. O roteiro desejado é o seguinte: assim que o usuário chega, ele recebe uma tela de boas-vindas; em seguida o bot guia o cliente por opções básicas e consulta a base de FAQs hospedada no Supabase usando RAG. Se as respostas não bastarem, o cliente pode solicitar atendimento humano. Nesse ponto o sistema deve encaminhar as mensagens a um dos quatro departamentos internos de forma automática, usando palavras-chave para reconhecer qual fila é a correta. O que preciso ao final: • Arquivo JSON do workflow n8n pronto para importar; &...
...deploying the solution within a high-tech application framework. Key Responsibilities 1. Core AI & ML (Adaptation & Intelligence) Multilingual Support: Lead cross-lingual fine-tuning to provide native-level support for Uzbek (including regional dialects), Kazakh, and Russian,Tadjik Latency Optimization: Streamline inference pipelines to target a response latency of 180-300 milseconds. Smart RAG (100 GB): Architect a vector knowledge base for educational materials, implementing a "triple-check" verification mechanism to eliminate hallucinations. NVIDIA Stack: Optimize inference for L40S environments using vLLM, TensorRT-LLM, and INT4/FP8 quantization. 2. Telegram Mini App & Real-time Web Audio Streaming: Implement low-latency real-time audio transmiss...
...Progettazione e scrittura di codice Python per l'integrazione di modelli linguistici (LLM) con sistemi aziendali e database proprietari. Gestione Infrastruttura Cloud: Configurazione e ottimizzazione di servizi all'interno dell'ambiente Google Cloud Platform (GCP). Architetture di Retrieval: Implementazione di sistemi per la gestione e l'interrogazione di basi dati strutturate e non strutturate (logiche RAG). Integrazione API: Sviluppo di connettori per il dialogo tra piattaforme diverse, garantendo la sicurezza e l'efficienza del flusso di dati. Testing & Ottimizzazione: Validazione tecnica degli output e affinamento delle prestazioni dei modelli implementati. Requisiti Tecnici (Must Have) Python: Padronanza avanzata del linguaggio per lo s...
Technical Specifications: Financial RAG & DRM Platform This document details the technical requirements for the development of an advanced financial data visualization and analysis platform with DRM protection and AI integration. The goal is to obtain a breakdown of costs and execution times based on the milestones presented here. Note: The platform must support a multi-language interface, allowing users to toggle between English and Spanish for navigation and system menus. Table of Contents 1. Frontend Architecture and Interface (SPA) 2. Backend, Infrastructure, and Data Pipeline 3. Security and DRM (Digital Rights Management) 4. Semantic Engine and AI Integration 5. Monetization and Ad Management 6. Development Plan for Quoting (Milestones) 7. Annex: Classification Logic and ...
I already have an n8n template and a live WhatsApp Cloud API number; what I need now is a rapid, clean configuration so I can demo an MVP chatbot that uses retrieval-augmented generation (RAG) for smarter replies. Template Name: ai-powered-whatsapp-chatbot-for-text-voice-images-and-pdf-with-rag I need someone who can assist me in my laptop to do it, I already have n8n instance, WhatsApp cloud API and rest of the things can be got it. The configuration should be done via Anydesk and time is crucial here. Someone who already configure that kind of job, please do share the proposal. Thanks
Job Description We are seeking an experienced full-stack developer or development team to build a secure, AI-powered financial data visualization and document platform with strong Digital Rights Management (DRM) and semantic search (RAG) capabilities. The platform will host and stream financial documents (PDFs), allow structured hierarchical navigation, enable AI-driven discovery and summaries, and enforce strict access control to prevent unauthorized downloads or redistribution. Monetization via subscriptions and ads is a core requirement. This is a serious, long-term project requiring strong architecture, security, and AI integration experience. Core Functional Requirements Frontend (SPA) Single Page Application (SPA) 6-level hierarchical navigation: Country → Sector &r...
I’m building a production-ready voice assistant that uses a large language model reinforced by Retrieval-Augmented Generation (RAG) to give accurate, context-aware answers in real time. The entire stack must live inside my AWS account so I stay in control of costs, data, and scaling. Here’s the core of what I need • A conversation engine that takes spoken input, runs it through speech-to-text, passes the transcript to an LLM, and returns a spoken response (text-to-speech). • A RAG layer that can call custom knowledge sources—think S3 documents, DynamoDB records, or an Aurora cluster—via vector search / embeddings so answers are grounded in my own data rather than generic web knowledge. • Event-driven automation hooks (e.g., tri...
Technical Specifications: Financial RAG & DRM Platform This document details the technical requirements for the development of an advanced financial data visualization and analysis platform with DRM protection and AI integration. The goal is to obtain a breakdown of costs and execution times based on the milestones presented here. Note: The platform must support a multi-language interface, allowing users to toggle between English and Spanish for navigation and system menus. Table of Contents 1. Frontend Architecture and Interface (SPA) 2. Backend, Infrastructure, and Data Pipeline 3. Security and DRM (Digital Rights Management) 4. Semantic Engine and AI Integration 5. Monetization and Ad Management 6. Development Plan for Quoting (Milestones) 7. Annex: Classification Logic and ...
Technical Specifications: Financial RAG & DRM Platform This document details the technical requirements for the development of an advanced financial data visualization and analysis platform with DRM protection and AI integration. The goal is to obtain a breakdown of costs and execution times based on the milestones presented here. Note: The platform must support a multi-language interface, allowing users to toggle between English and Spanish for navigation and system menus. Table of Contents 1. Frontend Architecture and Interface (SPA) 2. Backend, Infrastructure, and Data Pipeline 3. Security and DRM (Digital Rights Management) 4. Semantic Engine and AI Integration 5. Monetization and Ad Management 6. Development Plan for Quoting (Milestones) 7. Annex: Classification Logic and ...
INGENIERO SENIOR DE IA: SISTEMA RAG MULTIMODAL ON-PREMISE CON APRENDIZAJE CONTINUO 1. CONTEXTO Y DESAFÍO REAL proyecto del sector de la trefilería y el galvanizado con más de 40 líneas de producción activas. desafío no es la falta de información, sino que el conocimiento crítico es volátil: reside en la experiencia de supervisores y operarios veteranos y se transmite de forma verbal. Cuando surge una solución técnica en planta, esta no se documenta y se pierde para el siguiente turno. Buscamos desarrollar un ecosistema de IA que no solo responda preguntas, sino que capture y democratice el conocimiento técnico que surge en el día a día. 2. LA SOLUCIÓN: "THE KNOWLEDGE LOOP&...
...You’ll work across the stack—from backend APIs and cloud infrastructure to frontend experiences—while also contributing to AI-powered features using agents, AWS Bedrock, and Retrieval -Augmented Generation (RAG). Responsibilities - Design, develop, and maintain scalable backend services using Python and FastAPI - Build and optimize frontend applications using React - Architect and deploy applications on AWS - Integrate payment systems such as Stripe - Build and maintain user management systems including SSO - Design and integrate AI-driven features including agents, AWS Bedrock, and RAG pipelines Required Qualifications - 2-3 years experience as a Full-Stack Developer - Strong proficiency in Python and FastAPI - Strong experience with React - Solid exper...
...Progettazione e scrittura di codice Python per l'integrazione di modelli linguistici (LLM) con sistemi aziendali e database proprietari. Gestione Infrastruttura Cloud: Configurazione e ottimizzazione di servizi all'interno dell'ambiente Google Cloud Platform (GCP). Architetture di Retrieval: Implementazione di sistemi per la gestione e l'interrogazione di basi dati strutturate e non strutturate (logiche RAG). Integrazione API: Sviluppo di connettori per il dialogo tra piattaforme diverse, garantendo la sicurezza e l'efficienza del flusso di dati. Testing & Ottimizzazione: Validazione tecnica degli output e affinamento delle prestazioni dei modelli implementati. Requisiti Tecnici (Must Have) Python: Padronanza avanzata del linguaggio per lo s...
...Metadaten: User, Zeit, Projekt, Modell, Prompt, Quellen * Ziel: **IATF-/Audit-tauglich** #### 3) Wissensverdichtung & Suche * **L0**: vollständiges Original-Log (Source of Truth) * **L1**: strukturierte Verdichtung (Status, Entscheidungen, Änderungen, Risiken) * **L2**: Schnellsuche / Index (Vektor + Volltext) * Wöchentliche automatische Verdichtung (Scheduled Job) #### 4) Retrieval-Mechanismus (RAG) * Vor jeder KI-Antwort: * Suche über **L2 → L1 → ggf. L0** * Nur relevante Inhalte gehen in den Prompt * Antworten mit **Quellen-Referenzen** #### 5) Szenarien * Mensch ↔ Mensch Diskussion (nur Logging) * Mensch → KI (Analyse / Abfrage) * Mensch → KI (Änderungsauftrag, z. B. Projektplan) * KI → Mensch (zeitgesteuer...
...and lab-testing credentials. 2. Off-Page SEO (Authority Building) Develop a high-quality backlink strategy focusing on science, biohacking, and health-related publications. Note: No "link farms" or spammy guest posts. We need niche-relevant authority to survive algorithm updates. 3. GEO (Generative Engine Optimization / AI Visibility) Optimize our content for Retrieval-Augmented Generation (RAG)—ensuring AI bots (ChatGPT, Gemini, Perplexity) can easily scrape and cite our site. Focus on Entity Health: Improve our brand's "citations" across the web so AI models recognize us as a reputable entity in the peptide space. Format FAQ sections to answer conversational "long-tail" AI queries (e.g., "What is the shelf life of lyophilized...
AI Dev/Prompt Engineer - AI Image Gen - Print/Packaging Summary We’re looking for an experienced AI developer or prompt engineer to help train (using RAG or other methods) an AI-powered feature for our custom label maker web app. Our platform lets users design food packaging labels (e.g. peanuts, snacks) for corporate events, celebrations, and branded gifts. The goal is to let users generate AI-created background designs for packaging that strictly follow real-world printing requirements and guidelines. This feature should generate backgrounds that respect bleed, trim, safe zones, and general guidelines based on the product selected by the user. You’ll help design the AI workflow and ensure outputs are production-ready. We do not need assistance with the integration ...
...Offline AI Educational Chatbot (Curriculum-Based RAG) Project Overview I am looking for a developer to create a customized AI chatbot designed for students. The primary goal is to provide a "tutor in a box" that contains a vast knowledge base of specific educational curricula (textbooks, PDFs, and related materials). Critical Requirement: The system must function entirely offline after the initial setup, allowing students in areas with no internet access to interact with the AI and receive accurate answers based on the uploaded curriculum. Key Features & Requirements * Offline Functionality: The chatbot must run on local hardware (e.g., a laptop, Raspberry Pi, or a high-end tablet) without needing an internet connection. * Custom Knowledge Base (RAG): I wil...
Buscamos un/a freelance para apoyar la implementación y mantenimie...troubleshooting de servicios. • SSL / HTTPS: configuración y validación de certificados (Let’s Encrypt / reverse proxy), redirecciones y resolución de errores comunes. • Bases de datos: PostgreSQL y Supabase (tablas, relaciones, consultas SQL básicas, backups/restauración y conexión con automatizaciones). • Vector DB (Qdrant): nociones de embeddings, colecciones y uso en flujos tipo RAG. • Flowise: montaje de flujos LLM, RAG y conexión con APIs/modelos. • Chatwoot: configuración de inbox, canales, webhooks y operativa básica. • WhatsApp API / EvolutionAPI: integración, webhooks y envío/rece...
...an OCR pipeline to automatically extract and process information from time cards and integrate it into the company's system; Reduced processing time and minimized manual errors in employee time management; - Intelligent Chatbot for Internal Support Modeled an intelligent chatbot trained on the company manual, utilizing LLMs with optimized prompts, Transformer-based architecture via Hugging Face, RAG (Retrieval-Augmented Generation) approach, Redis for context caching, and Gunicorn as the application server for production; - Inventory Prediction and Purchasing Optimization Built predictive models using Prophet, XGBoost, LSTM, RNN, and PyTorch to forecast inventory levels and identify the optimal reorder point; Predicted future expenditures based on historical consumption, r...
I am looking for an experienced RAG engineer to improve my AI system built with LangChain and OpenAI, focusing on better retrieval accuracy and structured outputs. The role involves redesigning our vector store strategy (beyond basic Chroma) to support fresher, time-aware, and more relevant results. You will also refine prompt engineering to ensure strictly valid JSON outputs and reduce hallucinations in multi-step RAG workflows. Responsibilities include reviewing and refactoring the current RAG architecture, optimizing embeddings and chunking, and improving performance and scalability. Ideal candidates have strong experience with RAG systems, vector databases, prompt engineering, and production-grade Python.
...patterns plus a skeletal memo outline. • A Prefect flow then orchestrates a chain of LLM modules, RAG retrieval, and enrichment tasks. • The flow returns a JSON-wrapped memo draft that a human reviewer can open immediately in our internal tools. You’ll be free to choose the most appropriate vector store, embedding model, and validation strategy so long as everything runs on Python 3.9+ and is fully test-covered. Clean code, tight unit tests, and concise documentation are critical because future engineers will extend this work into risk identification, context-wrapper expansion, and prompt refinement. Deliverables - A reproducible Prefect flow (v2) in a Git repo - Supporting modules for LLM calls, RAG retrieval, and data validation - Unit tests that co...
...using Google’s Gemini models and search infrastructure for our platform (combination of social media and e-commerce). *****Send your resume and live links of projects where you integrated Vertex AI Search in order to be considered! Key Responsibilities - Design search architectures using Vertex AI Search for structured and unstructured data. - Build and optimize Retrieval-Augmented Generation (RAG) workflows to ground LLM responses in enterprise data, reducing hallucinations. - Develop data pipelines using Dataflow, BigQuery, and Cloud Storage to feed data into search indices. - Fine-tune ranking, indexing, and relevance using features like Vector Search (Matching Engine), embeddings, and semantic understanding. - Design "agentic" AI systems that can perform co...
...collaborate on a high-level AI productivity tool. The goal is to create a system that can process both live audio (meetings) and static documents (PDFs/Reports) to provide intelligent insights. Key Deliverables: Module 1: Speech-to-Summary: Implementation of a Whisper-based engine to transcribe audio and use an LLM (like GPT-4 or Claude) to generate concise meeting minutes and action items. Module 2: RAG-based Document Chat: A Retrieval-Augmented Generation system allowing users to upload large PDF/DOCX files and "chat" with the data for specific facts. Module 3: Sentiment & Intent Analysis: A dashboard component that tracks the "mood" of a conversation or document. Integration: A clean, functional API or Streamlit-based frontend to tie these features...
...container orchestration, CI/CD pipelines, microservices or modular monoliths: I’m open to whatever will give us low latency, predictable costs, and clean deployment workflows, provided we end up with true end-to-end encryption for all customer data, both at rest and in transit. Key areas where I need your expertise • Selecting and refining our NLP stack (e.g., spaCy, Hugging Face Transformers, RAG approaches) and the automated data-extraction pipeline. • Designing a privacy-first data model that isolates tenant data, supports encrypted metadata search, and passes external pen-testing. • Creating a scaling roadmap—load testing, horizontal/vertical autoscaling, cost observability, and graceful degradation strategies. • Establishing DevSecOps c...
...Finance Agent – generate timely management reports and keep real-time tabs on expenses. • HR, Operations, Customer Success Agents – I have broad objectives for each (talent administration, workflow optimisation, proactive client health respectively) and will refine the exact tasks with you during discovery. What I need from you – Select the right stack (e.g., OpenAI/GPT-4, LangChain, vector store, RAG pipelines) and architect it so each agent runs independently but can safely share context when helpful. – Build, test, and harden the six agents, then deploy them to either our Azure environment or a lightweight cloud of your preference. – Provide clean prompts / code, an integration map (CRM, marketing suite, ERP), and concise user guides s...
I am building an enterprise-grade Legal AI SaaS platform that must reliably answer region-specific legal questions across all of India, with an initial focus on Corporate Law and Intellectual Property Law. The core of the product is a Retrieval-Augmented Generation (RAG) pipeline that draws exclusively from verified Indian Supreme Court and High Court judgments, returns inline citations, and resists hallucination. What I need now is a senior, hands-on AI engineer who has already shipped production RAG systems. Your first milestone will be to design and implement the end-to-end retrieval and generation stack—ingest, indexing, querying, ranking, and grounded response generation—so that lawyers receive authoritative, citation-rich answers in real time. Key expectat...
...bureaucracy, you’ll love this. What You’ll Build You will personally design and build: An AI employee runtime (agents, tools, memory, permissions) RAG + knowledge systems (documents, APIs, live data) LLM orchestration (model selection, routing, evaluation, cost control) Enterprise-grade backend (auth, tenants, billing, audit logs) Cloud infrastructure (deployments, scaling, monitoring, security) You will decide: Which models we use When to fine-tune vs prompt When to self-host vs managed How agents reason, remember, and act Core Responsibilities AI / ML Systems Design agent architectures (tool-calling, memory, workflows) Build RAG pipelines (embeddings, vector DBs, retrieval strategies) Implement evaluation frameworks (quality, hallucination, l...
I’m diving into Andrej Karpathy’s nanochat repo and want an expert to sit with me for an initial one-hour call. My im...out training methodologies so I can adapt the model to a domain like law or healthcare. Start your bid with the word tinychat so I know you’ve read this. If you have already cloned, modified, or trained nanochat, please highlight that. I’m also keen to hear about any other LLMs you’ve fine-tuned, which verticals you tackled, and what results you achieved (LoRA, PEFT, Hugging Face Transformers, RAG pipelines, etc.). Timing is flexible; we’ll coordinate a slot that works for both of us. We will also discuss next steps after the first call. Deliverable • One live, screen-shared session (~60 min) where we discuss nanochat...
...-What You Will Build AI pipelines that analyze recorded conversations (speech → text → structured insights) LLM-based systems for summarization, classification, objection detection, and recommendations RAG pipelines with embeddings, vector search, reranking, and evaluation Scalable FastAPI microservices for AI inference Cost-efficient and low-latency LLM workflows Production-ready systems with monitoring, evals, and performance optimization -Required Experience We are only looking for candidates with real production AI experience, including: Python + FastAPI LLM APIs (OpenAI / Claude / Gemini) RAG pipelines (embeddings, vector DBs, retrieval optimization) Experience shipping AI systems to production Handling hallucinations, evaluation frameworks, and cost optimi...
...specifics and deploying the solution within a high-tech application framework. Key Responsibilities 1. Core AI & ML (Adaptation & Intelligence) Multilingual Support: Lead cross-lingual fine-tuning to provide native-level support for Uzbek (including regional dialects), Kazakh, and Russian. Latency Optimization: Streamline inference pipelines to target a response latency of 0.07 seconds. Smart RAG (100 GB): Architect a vector knowledge base for educational materials, implementing a "triple-check" verification mechanism to eliminate hallucinations. NVIDIA Stack: Optimize inference for RTX 4090 environments using vLLM, TensorRT-LLM, and INT4/FP8 quantization. 2. Telegram Mini App & Real-time Web Audio Streaming: Implement low-latency real-time audio tra...
...for authentication, payments, token tracking, ads, and optional AI boost. This is a serious, production grade project, planned in 4 structured sprints, with long term collaboration potential beyond Sprint 4. Core Architecture (Must Follow) SuperAI follows an INSTALL + HYBRID architecture: Local First (Primary) • LLMs: DeepSeek / Qwen / LLaMA • Image Generation: SDXL • Speech-to-Text: Whisper • RAG: FAISS / Chroma Cloud (Optional & Metered) • Groq / DeepSeek API (token-limited) • User authentication & authorization • Payments & subscriptions • Token & usage tracking • Ads on heavy operations Local AI is the default. Cloud AI is an opt in accelerator. ________________________________________ Platform Requirement • W...
...2–3 months — client owns 70+ domains and is building multiple products). Overview We need a skilled frontend/UI developer to build the MVP user interface for a self-hosted AI research platform for ~4,000 historical religious text PDFs. Phase 1 focuses entirely on polished, modern UI/UX (clean aesthetics, intuitive flow, responsive mobile/iPad) with mocked backend data. Later phases will add real RAG/AI and e-commerce. The goal is a beautiful, scholarly-feeling dashboard that encourages exploration, reading, and note-taking. It must feel professional, minimal, and engaging — worthy of a legacy archive. The UI should evoke a calm, scholarly atmosphere with high readability, minimal distractions, and a timeless feel (like a classic academic library interface). ...
...product description : This is a web-based chatbot using a Retrieval-Augmented Generation (RAG) architecture with open-source/local models (no paid APIs such as OpenAI or Anthropic). Primary focus: Data ingestion Retrieval & citation enforcement Leadership Q&A interface Scope of Work The freelancer will be responsible for: Building a data ingestion pipeline for: WhatsApp exports (.txt) Emails (.eml / .mbox / PDFs) PDFs, Word docs, Excel files Creating a vector database and metadata store Implementing a RAG-based chatbot that: Only answers when evidence is found Displays citations (document name, excerpt, date, author) Shows confidence level and “as-of” timestamp Developing a simple web
...hallucinations suitable for regulatory environments This is a real production-grade AI system, not a chatbot toy. Your Role You will act as an AI / LLM Architect, responsible for: designing the AI architecture implementing a robust RAG pipeline ensuring traceability, source integrity, and answer reliability supporting the transition from POC to production You will work directly with the product owner (business + regulatory background). Required Skills Strong Python (production-level) Hands-on experience with LLMs: OpenAI / Azure OpenAI and/or local models (LLaMA, Mistral) RAG architectures: embeddings, chunking strategies, retrieval tuning vector databases (FAISS, Qdrant, Weaviate, Pinecone) Document processing: PDFs, OCR, regulatory texts Prompt engineering + respon...
I’m refreshing the hero section of my Retrieval-Augmented Generation (RAG) demo and need a single, polished banner at 4000 × 1000 px. For your entry upload a preview with black bar on top and bottom, as Freelancer crops image, and I want to see if the fuill 4000:1000 ratio image! Core concept • Background: the classic library scene I’ll supply—rich bookshelves, warm tones, no AI glitches. • Overlay: an elegant, professional and subtle holographic treatment that feels refined rather than sci-fi loud. • Story elements: a few books outlined, pages flowing out toward a stylized “database,” and a holographic user seated at the table working on a computer that links visually to that database. What I already have AI-generated dr...
I need a technically-minded prompt engineer to turn my existing knowledge base into a reliable WhatsApp AI agent powered by Meta’s WhatsApp Cloud API. The agent’s single job is to answer user queries clearly and accurately, drawing only on the content I prov...documentation of the setup (API calls, environment variables, webhook configuration) so I can redeploy or extend it later. Deliverables – Deployed WhatsApp agent connected to the Cloud API and my KB – Tested flows for text, brochure (PDF) and video replies – Prompt templates and code/scripts with inline comments – Setup guide and brief hand-over call If you have shipped similar WhatsApp or RAG-based assistants before, I’m ready to start right away. please share your previous ...
...$2-3K/month cash. What We’re Building Users practice sales pitches by talking to AI avatars that act as doctors. Think of it as “a personal coach powered by AI.” MVP features: - Real-time voice conversation with video avatar - AI evaluates performance and gives feedback - Dashboard to track progress Tech stack (flexible): - Avatar: HeyGen or similar - Voice: OpenAI Whisper + TTS - Brain: GPT-4 with RAG - Frontend: React/ Why This Will Succeed • I have 20+ years in pharma marketing (I know the buyers) • 2-3 companies already interested in piloting • $150M+ market in LATAM with no clear leader • We can be profitable with just 5 clients What I Need You should have: - 5+ years full-stack experience - Experience with AI APIs (OpenAI, etc.) - Built...
...readiness actions, and output weekly plans. A full developer pack is already prepared (architecture, data model, API outline, Jira backlog, business rules). We are looking for delivery, not product design. Scope (MVP) Web app (frontend + backend) User roles & permissions Weekly PDF upload and versioning Activity extraction from PDF (basic parsing + manual correction) 6-week lookahead logic RAG (Red / Amber / Green) classification Action / constraint management Role-based dashboards Weekly exports (PDF / CSV) Audit logging Important constraints The system must NOT directly edit project schedules Input is via weekly PDF uploads only Logic is time-based, not priority-based This is a control system, not a scheduler Tech Stack (Open to suggestion) Frontend: React ...
...Frontend • React / OR • Flutter (Mobile App) LLM (Hybrid API Setup) Primary (Fast + Low Cost) • Groq Cloud LLMs o LLaMA 3 o Mixtral o Mistral Secondary • DeepSeek Coder API • OpenAI mini models (optional backup) Image/Video Models • SDXL (Stable Diffusion XL) • Replicate API (optional) • FFmpeg for video processing Search APIs • Brave Search API • Bing Web Search API Vector Database (Memory/RAG) • FAISS (recommended) • ChromaDB Auth & Payment • Firebase Auth • Google Play Billing (for app) • Stripe (international payments) Budget Open for discussion based on experience. Looking for long term partnership. Who Should Apply Developers with experience in: • LLMs & Open source models • AI ...
I want to hire expert in Python AI Chatbot for my existing chatbot ( Must have good experience with chatbot , Rag , NLP , ES ,github) , once it will work you will have to switch this to live code. Prefer - Vietnam Developer Max - 1 - 3 hrs work Budget - $50
...for the scope (fixed-price preferred) Project Type: One-time / Remote Overview We need a skilled frontend/UI developer to build the MVP user interface for a self-hosted AI research platform for ~4,000 historical religious text PDFs. Phase 1 focuses entirely on polished, modern UI/UX (clean aesthetics, intuitive flow, responsive mobile/iPad) with mocked backend data. Later phases will add real RAG/AI and e-commerce. The goal is a beautiful, scholarly-feeling dashboard that encourages exploration, reading, and note-taking. It must feel professional, minimal, and engaging — worthy of a legacy archive. MVP Phase 1 Requirements: UI & User Experience Only Modern, minimalist design: clean typography, neutral colors (grays/whites/soft blues), subtle shadows, logical spacin...
...* Graph data modeling (ontology / domain modeling; clean labels, relationships, and boundaries) * ETL / ingestion from CSV/JSON (ID strategy, validation, deduplication) * Performance & quality fundamentals: constraints, indexing, and query profiling/optimization using EXPLAIN / PROFILE Nice-to-have * Python for scripting and transformations (import utilities, data cleanup, automation) * LLM / RAG integration (chunking strategies, metadata design, retrieval patterns) * Neo4j Graph Data Science (optional: similarity, community detection, embeddings) How to apply: Please reply with: 1. a short note on your best Neo4j project, 2) a link/sample of Cypher you’re proud of (anonymized is fine), 3) how you would model “case → decision → rationale” in 5&...
...recommendation systems). Conduct feasibility assessments, risk evaluations, and proof-of-concept development to accelerate time-to-market. Provide technical leadership and mentorship to ensure high-quality, maintainable code and architecture. Required Qualifications 8+ years of experience as an AI Architect or similar role, with proven expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, GPT models, RAG systems). Strong background in bridging business needs to technical implementations, demonstrated through successful projects with short development cycles. Proficiency in cloud platforms (AWS, Azure, GCP) for AI deployment, MLOps, and scalable architectures. Experience in data engineering, NLP, computer vision, or predictive modeling relevant to business applications. Ex...
I'm looking for a developer to build a project that implements a Retrieval-Augmented Generation (RAG) pipeline enabling semantic search and conversational AI with a resume (CV). The goal is to create a cost-effective architecture that runs the application logic locally while offloading heavy LLM inference to the cloud. The application should support semantic search over the resume and allow a conversational interface to answer questions about the resume content. This project is focused on building the RAG pipeline, integrating the embeddings, and ensuring a secure connection between the local backend and cloud model server. - Responsibilities Design and implement the RAG pipeline for resume semantic search Integrate embedding generation using HuggingFace models...
HI , NEED AI ML EXPERT IN RAG - ESPECIALLY - RAG - OLAMA - QWEN 3:8B - TO WORK ON ACADEMIC DATA - ESPECIALLY MATH ML - IMAGES - FLOW CHARTS . CONVERTING MATHS QUESTIONS FROM PDF INTO MATHML
Hypothesis: A constraint-aware, evidence-linked RAG architecture specifically optimized for Small Language Models (SLMs) can achieve legal reasoning accuracy and hallucination reduction comparable to or better than larger LLM-based RAG systems, while operating at significantly lower computational cost. So, basically you can create a new RAG architecture than performs much better than a currently available Plain SLM. Steps: 1. Check these SLMs first to benchmark on Retrieval/QA tasks: Inlegal‑Sbert (Retrieval) / InLegalBERT embeddings (Retrieval) / InLegalLlama (QA) 2. Dataset in consideration: Dataset: a) IPC Sections with conditions, exceptions, constraints, relations b) Supreme court judgement copies c) IPC_and_case_related_QA_dataset 3. Evaluate Inlegal‑Sbe...
...test-driven, observable, and resilient by default * Embed AI deeply into production workflows (LLMs, RAG, orchestration, evaluation, guardrails) * Ensure AI systems behave predictably, safely, and measurably in real-world use * Establish and enforce DevOps and SecOps best practice across the full stack * Identify architectural and AI-specific risk early and eliminate it decisively **What you bring** * Expert-level software architecture experience * A genuinely **AI-first mindset**, comfortable applying AI across engineering, operations, and workflows * Strong hands-on capability with **Rust, Flutter, and Supabase/Postgres** * Real production experience with **LLM-based systems**, including RAG and orchestration * Proven DevOps & SecOps depth (CI/CD, infrastructure au...
...investor matching and automated outreach management. As a founder with a background in AI Product Management, I am looking for a technical partner to lead the development of our MVP. Key Responsibilities: AI Agent Architecture: Design and implement a multi-agent system capable of analyzing complex startup data and matching it with investor investment mandates. RAG Implementation: Build advanced Retrieval-Augmented Generation (RAG) pipelines to process unstructured PDF decks and internal investor databases. Investor Matching Engine: Develop a proprietary scoring algorithm to predict "investor-founder fit" based on historical funding data and sector preferences. Full-Stack MVP Development: Create a responsive, high-performance web dashboard (React/) integrate...
...1. Answers reference the correct location in the original document. 2. Summaries stay within 10% variance of the original word count limit I set (e.g., “200-word summary”). 3. Response time for a single query on a 10-document corpus (under 25 MB total) is under 8 seconds on a mid-range cloud VM. If you already have experience with vector databases (Pinecone, FAISS, or similar) or have built RAG pipelines with OpenAI or Azure OpenAI, mention it in your proposal along with a brief outline of your approach....
...internal API integrations for this phase. The backend should be built using FastAPI to fetch sports data (scores, stats, match details) from public APIs, process it, and pass it to an LLM for content generation. The frontend should be a simple React-based chat interface where users can request summaries, previews, or articles and receive AI-generated responses in a readable format. I also want RAG (Retrieval-Augmented Generation) implemented to ensure the output is accurate, relevant, and grounded in real data rather than generic AI responses. Ideal candidates will have experience with FastAPI, React, LLM integrations, and API-based data pipelines, and should provide a clear plan for MVP delivery within 2–4 weeks. I have already Public API for Phase 1 and diagram for d...
