AI Agent Development Projects

opportunity

Monday.com Business Process Automation

I need a freelancer who understands how to build process automations in Monday.com to help me onboard to Monday.com and build a project management automated workflow.

Make.com and OpenAI

I’m looking for an AI automation specialist to build a practical workflow using Make.com and OpenAI. I want to automate a repetitive business process that currently requires manual work. What I need: Make.com automation workflow OpenAI integration Trigger and action configuration Custom AI instructions/prompts Connection to the required business applications Data processing and workflow logic Error handling where appropriate Testing with real sample data Clear explanation of the completed workflow The exact workflow will be discussed with the selected freelancer. I’m open to recommendations if there is a better way to structure the automation. The ideal freelancer should have experience with Make.com, OpenAI API, webhooks and third-party integrations. Please provide examples of AI automations you have built and explain briefly what was automated. Deliverable: A tested Make.com + OpenAI automation that performs the agreed business process reliably.

Build an Intelligent AI Agent for Automated Inventory Management

We are looking for an experienced AI developer or automation expert to design and deploy a custom AI agent tailored for real-time inventory management. The goal is to automate tracking, optimize stock levels, and reduce manual workload across our supply chain. Key Responsibilities: Develop an AI agent that monitors inventory levels in real-time across channels/warehouses. Implement predictive capabilities to forecast demand, prevent stockouts, and flag overstock risks. Create automated alerts and triggers for automated purchase order drafting when stock falls below reorder thresholds. Integrate the AI agent with our existing system (e.g., Shopify, QuickBooks, Custom ERP, or database). Required Skills: Proven experience building custom AI agents (using frameworks like LangChain, AutoGen, or similar). Proficiency in Python, API integrations, and database management (SQL/NoSQL). Background in supply chain, inventory management systems, or e-commerce integrations.

N8N Agents

I run an accounting practice and looking at developing agents to help with 3 areas. I'm not alien to how agent works and initially looking at setting this up as there are base templates I can use to start as a base before customising but I thought I see if someone can do it so I focus on other things. 1. Email - handling my outlook inbox. Categorising, draft responses and email sent to me summarisin things to focus on, outstanding etc 2. Bookkeeping - Xero/Quickbooks - pulliing unreconciled transactions, categorising based on blueprint, attempting other transactions and sending those to team via email with questions. Once confirmed and approved, post to Xero/Quickbooks via API intergrations. 3. Lead Gen - scaping ICP, qualifying them, identify angle of approach to win business. Run periodically and send to me via email or any other recommended medium. I want to start using Hubspot so could be there.

opportunity

Website search and extract/compare agent

i want to develop Ai agents that will log onto a list of websites (approx 80) and run a set of searches daily. i need them to then compare one days results against the other and then only present me with new listings that weren't thre the day before. if the results could be presented on Dashboard or Claude Artifact that lets me then click and see the source listing that is also important i use Claude, i have login details for every website but we will need to counter captcha forms etc. im happy to run it from Clause Chrome Browser but my input should be minimal. APIs have not worked neither has scraping i have tried this project every way except AI websites include ebay (global search) ebay.de in particular catawiki reddit vinted cragislist etc

Automated ai lead generation

Request for Quotation (RFQ) ​Project Title: Automated AI Lead Generation & Qualification Engine (Pre-Owned Vehicles) Target Throughput: System built to handle high-volume acquisition supporting 30+ completed vehicle sales per month Core Tech Stack: Make.com, AI Processing (Gemini / OpenAI), Web Scraping / Lead Capture, CRM & Outreach Engine (Instantly / WhatsApp / CRM) ​1. Project Overview & Objective ​We are launching an automated lead acquisition and qualification engine for a pre-owned vehicle operation in South Africa. Our goal is to achieve 30 completed vehicle sales per month. ​To support this volume, we need an end-to-end automated workflow that captures inbound vehicle inquiries (buyers and sellers), enriches and validates lead data, scores intent using AI, and routes qualified leads into an automated outreach and CRM pipeline. ​We are seeking an experienced Make.com & AI Automation Specialist to architect, build, test, and deploy this complete system. ​2. Scope of Work ​Phase 1: Lead Capture & Web Scraping ​Set up automated web and social scraping workflows (using tools like Browse AI, Apify, or Meta Lead Ads API) to extract incoming vehicle buyer and seller leads. ​Capture inbound submissions from web forms, landing pages, and external vehicle listing platforms. ​Phase 2: AI Lead Enrichment & Filtering (Make.com + AI) ​Build Make.com scenarios integrated with Gemini/OpenAI API to: ​Parse, clean, and format raw contact details (normalizing South African phone numbers, names, locations). ​Filter and score incoming leads based on buying/selling intent (vehicle preference, budget, urgency, financing readiness). ​Auto-filter junk or incomplete leads to keep the pipeline focused on high-converting prospects. ​Phase 3: Automated Personalization & Outreach ​Set up instant multi-channel response triggers (Email / WhatsApp API / SMS) ensuring speed-to-lead response times under 2 minutes. ​Generate contextual AI-personalized messaging based on specific vehicle models, buyer requests, or seller submissions. ​Connect scored leads to email/outreach platforms (e.g., Instantly.ai or automated messaging sequences). ​Phase 4: CRM Pipeline Integration ​Sync all processed leads automatically into a central CRM (HubSpot, GoHighLevel, or Airtable). ​Set up pipeline stage tracking from New Inbound → AI Qualified → In Progress → Closed / Completed. ​3. Key Deliverables ​Fully Automated Make.com Scenarios: Modular scenarios connecting lead capture, AI enrichment, and CRM destination. ​AI Lead Scoring System: Prompt architecture for automated lead classification and filtering. ​Outreach & CRM Sync: Seamless routing into cold email outreach / CRM pipelines. ​Handoff Documentation: Brief video walkthrough and technical documentation for scenario management. ​4. Required Freelancer Skills ​Advanced proficiency with Make.com or n8n multi-step scenarios. ​Hands-on experience integrating OpenAI / Gemini APIs via HTTP/REST nodes. ​Familiarity with lead scraping tools (Browse AI, Apify) and CRM APIs. ​Strong understanding of email deliverability, WhatsApp API tools, and pipeline management. ​5. Submission Requirements ​Please reply with your quotation including: ​Technical Architecture: Proposed tools and high-level Make.com scenario structure. ​Pricing Structure: Fixed setup/implementation fee (plus optional monthly maintenance retainer). ​Estimated Timeline: Total days required for development, testing, and deployment. ​Portfolio: 1–2 examples of previous lead generation or Make.com automation workflows built.

AI Engineer — AWS Bedrock AgentCore

We're looking for engineers to help build and productionize AI systems on AWS Bedrock AgentCore — real conversational AI, RAG pipelines, and agent architectures that go well beyond proof-of-concept and serve live traffic and real users. Whether your strength is on the AI application side (agents, RAG, orchestration) or the platform side (deployment, observability, security), this role sits at the center of turning working demos into production-grade, reliable systems on Bedrock's agentic stack. If you've built and shipped on AgentCore specifically — not just Bedrock in general — and want your work to run in production rather than sit in a notebook, this is built for that. What You'll Do - Design, build, and deploy conversational AI systems, chatbots, and AI agents using AWS Bedrock AgentCore - Architect and ship production-grade RAG (Retrieval-Augmented Generation) systems — not prototypes, but systems serving live traffic - Build and deploy LLM applications primarily on AWS Bedrock and AgentCore, with Azure OpenAI or equivalent platforms as secondary context - Develop and orchestrate agent architectures within AgentCore, using frameworks such as LangChain, LangGraph, or LlamaIndex where applicable - Build and maintain MCP (Model Context Protocol) server integrations to extend AgentCore agent capabilities - Design and build the production service layer around AgentCore (Lambda, API Gateway, IAM, DynamoDB, OpenSearch, or equivalents) - Establish CI/CD pipelines and manage development, beta, and production environments for AgentCore-based services - Implement observability for AgentCore agents: tracing, dashboards, per-turn cost and latency metrics, error rates, and audit trails - Implement key security controls — data-leakage protection, session isolation, auth/authz boundaries, secure prompt/response storage - Write clean, maintainable, production-quality Python across the AI application and platform stack - Monitor, evaluate, and iterate on agent, RAG, and platform performance in production - Stay current with fast-moving developments in Bedrock, AgentCore, and agentic AI systems, and bring relevant advances into the project What You Bring - Proven, hands-on experience building and deploying AI agents on AWS Bedrock AgentCore in a production environment — not personal projects or tutorials - Direct experience with AWS Bedrock's agentic tooling (AgentCore, Bedrock Agents, or equivalent Bedrock-native orchestration) - Strong Python skills for AI application development and/or service integration - Working experience with AWS cloud environments; Azure experience is a plus but not the primary requirement - Experience with at least one of: agent orchestration frameworks (LangChain, LangGraph, LlamaIndex), RAG system design, or AWS production infrastructure (Lambda, API Gateway, IAM, DynamoDB, OpenSearch) - Experience with observability and monitoring for AI or distributed systems - Strong understanding of security, data handling, and production-readiness tradeoffs - Comfortable working in a fast-moving, evolving technical environment with pragmatic engineering judgment

AI search optimastion

Seeking an experienced AI search optimization specialist to improve our discoverability when customers search for terms like "London interior and exterior finishes" and "period property restoration." Enhance relevance and ranking across search platforms, refine keywords and metadata, and implement semantic matching for varied query phrasing. Deliver measurable evidence of improvement via a clear data sheet showing search performance metrics,results, and recommended next steps to justify ongoing investment. £30 per month for 6 months

AI Agent Architect (Claude + n8n)

About us We're a UK manufacturer of industrial PVC products (spray booths, partition systems, curtains). Small team, 15 people, and I run it. I'm technical, I've got the basics of Claude down, and I've already built working automations. I'm not starting from zero. What I'm building A team of AI agents that runs our web presence semi-autonomously: Individual agent profiles and agendas built inside Claude, each with its own remit (SEO, Google Ads, CRO, content, design) Agents monitor ongoing SEO performance, paid search, and conversion rate, then adjust content and build new pages off the back of what they find A manager agent I speak to directly. It takes input from the specialists, then allocates the work Our full website is being rebuilt in Claude Design at the same time What I need from you This is a teaching engagement, not done-for-you. I want to be able to run and extend this myself once we're finished. Start with a 1 to 2 hour screenshare. You review how I'm currently using Claude, tell me what I'm doing wrong, and help me think through the agent architecture properly If that goes well, ongoing hourly support through the rest of the year You should be strong on Claude (projects, skills, MCP, tool use, agent orchestration patterns) n8n or equivalent workflow tooling Connecting agents to real data sources: GA4, Google Ads, Search Console, HubSpot Knowing where autonomy breaks and what needs a human gate How to apply Skip the generic pitch. Tell me how you would actually build this. Specifically: How you'd structure the manager and specialist agents, and how they pass work between each other Where you'd hold shared state and context so agents aren't working blind What you'd let run autonomously versus what you'd gate for approval, and why One thing you think I've got wrong in the description above Applications that don't answer those four won't get a reply. Details Rate: £30-£50 per hour Hours: roughly 5-10 per week to start Timezone: UK-based

Looking for AI tasker

I looking for a professional and skilled and experienced AI tasker.

Full Stack AI Developer for Healthcare SaaS

Seeking an experienced Full Stack AI Developer to build a production-grade Healthcare SaaS leveraging LLMs, RAG, and AI agents. Responsibilities: design end-to-end architecture, implement contextual AI chatbot, develop RAG pipelines for healthcare documents, build agent tooling and workflow automation, secure backend APIs and authentication, and create a responsive React/Next.js frontend. Required expertise: Python (FastAPI), LLMs (OpenAI or equivalents), LangChain/LlamaIndex/LangGraph, vector DBs (Pinecone, Weaviate, Chroma, FAISS), and cloud deployment (AWS/Azure/GCP). Healthcare product experience and demonstrated RAG/agent projects are highly desirable.

AI Engineer / NLP Engineer for an AI BF/GF Product

We are launching a product in the AI romantic companions / AI BF/GF niche and need a specialist to take over the AI development part. The website, registration, design, menu and payment system are handled separately. This role focuses only on the AI core: dialogue models, AI characters, memory, user adaptation, realistic image generation and MVP AI architecture. Required skills LLMs for conversational AI products. Testing and comparing open-source and commercial models. Llama / Hugging Face / vLLM / Text Generation Inference. System prompts for AI characters. Prompt engineering, fine-tuning and LoRA. Memory layer design: mem0, Qdrant, embeddings, RAG. Python, Docker, PostgreSQL, Redis. GPU infrastructure and model deployment. AI image generation: FLUX.1 / Stable Diffusion / ComfyUI. Consistent face setup for AI characters. Moderation / safety filters for AI dialogues. Tasks Select and test LLMs for romantic AI dialogues, set up AI characters, design user memory, plan adaptation to the user’s communication style, and suggest a pipeline for realistic AI selfies. Experience with AI companions, dating, chatbots, virtual characters, roleplay scenarios or conversational AI is a plus. The candidate should be ready to complete a short test task. Please include your stack, relevant experience, project examples, readiness to complete the test task and your rate / estimated project cost.

opportunity

AI-Powered Deal Origination & Opportunity Intelligence Platform

Overview Build an MVP AI-powered deal origination and opportunity intelligence platform to identify UK private company acquisition, succession, refinancing and distressed opportunities. The platform should analyse public company data, identify high-probability opportunities, enrich decision-maker information, generate AI reports and support targeted outreach. This is an MVP validation project requiring a fast, practical build with scalable foundations, not an enterprise solution. Core Objectives The platform must: • Collect company/director data • Analyse and score opportunities • Identify decision makers • Enrich contacts • Generate AI intelligence reports • Create outreach recommendations • Store opportunities in CRM • Maintain continuously updated pipelines Data Sources Required: • Companies House API • Gazette Insolvency Feed • Company websites • Public web research Preferred: • LinkedIn enrichment • Contact providers • News feeds • Business directories Future: Planning data, Land Registry/property ownership, email automation, workflows, dashboards, additional providers and AI agents. Functional Requirements 1. Company Intelligence Engine Retrieve, store and update: • Company name, number, address and SIC codes • Filing history, accounts and charges/mortgages • Directors and shareholders where available • Insolvency notices and Gazette events • Website and content summaries Maintain structured profiles for each company. 2. Opportunity Scoring Engine Core IP component. Must be configurable, AI-independent and adjustable without code changes. Required: • Weighted and rule-based scoring • Score explanations • Confidence ratings Scores: Acquisition: revenue, EBITDA/profitability, growth, recurring income, sector attractiveness, leverage. Succession: director age, ownership length, ownership concentration, management depth, succession indicators. Refinancing: lender charges, debt profile, leverage, property ownership, maturity indicators. Distress: insolvency notices, winding-up petitions, director resignations, overdue filings, negative trends. Probability of Sale: founder age, ownership duration, succession indicators, growth plateau, market conditions. Example: Sale Score: 86/100 Reasons: • Founder age estimated 67 • Sole shareholder • 24 years ownership • Stable profitability • No succession structure identified AI explains scores; scoring remains framework-driven. 3. Contact Enrichment Identify/store: • Founder, CEO, Managing Director, shareholders • Email, telephone, website, LinkedIn • Decision-maker information Supports future outreach and relationship development. 4. AI Intelligence Briefs Generate for high-ranking opportunities: Company Summary: Business description, financial overview, strengths. Opportunity Summary: Selection rationale, engagement potential, strategic rationale. Engagement Angle: Succession planning, growth capital, partnership, acquisition or refinancing 5. CRM MVP CRM must support: • Opportunity storage • Search/filtering • Notes and comments • Status tracking • Outreach tracking • Score history Workflow: Identified → Qualified → Contacted → Conversation Started → Active → Mandated → Closed 6. Outreach Intelligence Generate/store: • Personalised emails • LinkedIn messages • Telephone briefs No automated sending required AI Architecture Use model-agnostic architecture that remains operational if providers change Support: OpenAI, Anthropic Claude, Google Gemini, Meta Llama, DeepSeek, Qwen and future providers Admin controls: • Select AI provider • Change providers without code changes • Configure API keys • Add models Scoring must remain independent of AI Technology Backend: Python, FastAPI Database: PostgreSQL, Supabase Frontend: React, Next.js Infrastructure: AWS, Vercel, Supabase AI: Provider-agnostic APIs with future agent support Scalability Support future: • Multi-agent workflows • AI orchestration/MCP • Additional APIs • Email and workflow automation • Large-scale analysis • Advanced reporting Future integrations: CRM systems, enrichment providers, email systems, Land Registry, planning/property/commercial intelligence sources Dashboard Provide a simple user-friendly dashboard for non-technical sales/outreach users with navigation, opportunity views, filtering, pipeline management and AI insight access Deliverables • Working MVP • Source code • Deployment instructions • Technical documentation • Configurable scoring engine • CRM • Company intelligence engine • Contact enrichment • AI opportunity reports • Outreach generation • User administration • Large-scale UK company analysis capability Proposal Requirements Include: • Relevant examples • Architecture • Technology stack • Cost estimate • Delivery timeframe • Support options • MVP improvements Budget Open to proposals. Preference for developers experienced in AI intelligence platforms, CRM systems, API integrations and scalable MVP delivery rather than enterprise builds

opportunity

Lead generation for nursery

Looking for digital marketing(meta) including video and images, and AI funnel for the nursery website. Also got some idea from chatgpt as attached. Any freelancer can to do this. Fixed charges please. (can also pay in INR)