EroFlow Intelligence is an enterprise-grade, autonomous AI orchestration pipeline designed to mitigate global supply chain disruptions for aerospace manufacturing. Built using a multi-agent framework, the system automates the entire lifecycle of risk detection, impact analysis, and procurement mitigation without requiring human intervention for standard operational anomalies. The architecture coordinates three specialized, asynchronous AI agents that communicate via a centralized event bus to solve complex logistical bottlenecks in real-time. Core Agent Architecture & Workflow 1. The Sentinel Agent (Data Ingestion & Extraction) Role: Continuous Global Monitoring. Function: Utilizes advanced LLM-driven web scraping and unstructured data extraction to monitor global news feeds, geopolitical shifts, weather anomalies, and shipping port telemetry. Trigger: If it detects a disruption (e.g., a port strike or critical mineral shortage), it extracts key entities (materials affected, estimated delay times) and passes a structured JSON payload to the orchestration layer. 2. The Impact Assessment Agent (Predictive Modeling) Role: Deep Cross-Referencing & Analytics. Function: Upon receiving a trigger, this agent cross-references the disrupted material with the company’s internal ERP (Enterprise Resource Planning) database and current inventory levels. Output: It runs a predictive analysis to determine exactly which production lines will stall and calculates the financial risk, assigning a high/medium/low priority score to the event. 3. The Mitigation & Logistics Agent (Autonomous Execution) Role: Operational Resolution. Function: If the risk score exceeds a specific threshold, this agent is authorized to take action. It autonomously queries pre-vetted alternative suppliers via APIs, negotiates standard volume pricing based on historical contract data, drafts a comprehensive procurement proposal, and queues the purchase order for final human sign-off (or executes it automatically if under a certain dollar cap). Technical Stack (The Blueprint) Frameworks: LangGraph / CrewAI (for multi-agent state management and deterministic routing). Core Language: Python 3.11+ Data Layer: PostgreSQL (for ERP syncing) & Pinecone / Qdrant (Vector database for storing and querying supplier contract PDFs and historical compliance documentation). LLM Orchestration: OpenAI GPT-4o / Anthropic Claude 3.5 Sonnet utilized via structured outputs (Pydantic parsing) to ensure strict API data integrity. Hosting & DevOps: Containerized via Docker, orchestrated via Kubernetes, and deployed on AWS with asynchronous task queues managed by Celery and Redis. Quantifiable Business Results (The Hook) 92% Reduction in supply chain anomaly response time (from 48 hours down to 14 minutes). Automated Recovery: Successfully mitigated over 140 potential production line stalls autonomously in simulated stress tests. Cost Efficiency: Saved an estimated $1.2M in expedited shipping fees by predicting bottlenecks 10 days before they impacted manufacturing floors.

Create an outbound sales system for Real Estate. Should include both lead generation, ai voice calls, sms and email follow ups

ElevenLabs Conversational AI Expert — Long, Multi-Node Voice Agents with Tool Calls Project type: Hourly Experience level: Expert Duration: Short-term engagement with potential for ongoing work About the project We're building voice agents on ElevenLabs Conversational AI (Agents Platform) that run long, complex calls of 20+ nodes in the workflow builder, with multiple tool/function calls along the way. The agent is embedded directly into our app (using the ElevenLabs SDK) rather than the ElevenLabs widget. The agents work, but we're fighting duplicate questions/answers. The agent re-asks questions it already asked, or repeats information it already gave, at different points in the call. We need someone who has actually built and shipped long-running ElevenLabs voice agents (not just simple single-prompt bots) to help us fix the structural setup so calls stay coherent end to end. That covers workflow/node architecture, state handling, prompt design, tool orchestration, and our client-side integration. What you'll do ● Audit our current agent: workflow node structure, system/node prompts, tool definitions, and conversation flow. ● Audit our client-side integration (the ElevenLabs SDK embedded in our app): session/connection handling, event handling, client tools, and how local app state stays in sync with the conversation. Reconnects, double-fired events, or repeated client-tool calls can also cause re-asks. ● Diagnose the root causes of the duplicate question/answer behavior. Possible culprits include context/state not being tracked across nodes, overlapping node responsibilities, prompt ambiguity, retrieval/knowledge-base issues, or client-side state/event problems. ● Redesign the node graph and transitions so each node has a clear, non-overlapping job and the conversation can't loop or re-ask. ● Improve state/variable management across nodes: dynamic variables, captured data, and how it's passed forward so the agent "remembers" within a call. ● Tighten tool/function calling: when tools fire, how results are handled, error/timeout handling, and avoiding redundant calls. ● Address context-window and long-call degradation, plus turn-taking behavior that causes drift. ● Recommend the right structural patterns for flows this long (single agent vs. multi-agent/agent transfer, sub-agents, branching). ● Document the fixes and the patterns so our team can maintain and extend the setup. You're a strong fit if you have ● Demonstrable hands-on experience with ElevenLabs Conversational AI / Agents Platform. Please reference specific agents or projects you've built. ● Experience with the workflow/node builder for branching, multi-step calls, not just a single system prompt. ● Experience embedding ElevenLabs in a custom app via the SDK (React/JS, WebRTC/WebSocket), not just the drop-in widget. ● Solid grasp of tool/function calling (client tools and server tools/webhooks), including error handling. ● Strong prompt engineering for voice, plus understanding of LLM context windows, state, and conversation memory. ● Experience debugging long conversations for looping and repetition, including intermittent, hard-to-reproduce cases. ● Bonus: knowledge base / RAG, dynamic variables, multi-agent transfer, post-call analysis, and the ElevenLabs API/SDK. To apply, please include 1. A short description of a long, multi-node ElevenLabs agent you built: how many nodes, what tools, and what it did. 2. How you'd approach diagnosing duplicate question/answer issues in a 20+ node flow (a quick paragraph, since we want to see how you think). 3. Your availability and rate. Applications that just say "I'm an AI expert" without specific ElevenLabs experience will be skipped. We're looking for someone who has lived in this platform.

We are a small AI consulting practice that helps financial services firms put AI to work inside their business. Our clients are owner-led firms like accountants, business appraisers, financial advisors, and insurance agents. We do not sell one-off scripts or disposable projects. We build practical AI systems that take real work off these firms' plates, delivered through ongoing monthly work. Demand is growing and the bottleneck is delivery. We are looking for one delivery partner to own that side of the work with us. How it works: we handle marketing, sales, and the paid advisory session that starts each client. Once a client moves to ongoing work, you take the lead on delivery. You build the systems against the priorities we set each month, and you run the weekly client meeting as their main point of contact. We stay in for support, to translate the client's business context, and to own the relationship at the top, but week to week the client works with you. What you would own: -Building AI and agentic systems for clients -Running the weekly client meeting and being the client's day-to-day contact -Taking each engagement from kickoff through delivery on the month's agreed hours, to a standard we can stand behind Compensation is $100/hour for your hours, which include both build time and client meetings. Straightforward and paid against tracked hours. As our client book grows, so do the hours available. Who we are looking for: -Genuinely fluent building real systems with modern AI tools. -Not just familiar with them. You should be comfortable architecting and shipping working systems for non-technical business owners. -Client-ready. You can run a working session, explain technical things plainly to a non-technical owner, and hold a client relationship week to week. -Native or fluent English. You are in front of clients every week, so clear, natural communication is non-negotiable. -Strong general technical judgment. The specific stack matters less than the ability to find the right solution and build it. -Reliable. We scope the work and stand behind it, so we need to count on what you deliver and how you handle the client. Who this is not for: anyone looking to own sales or pricing, anyone who only wants to build quietly and never talk to a client, and anyone new to this work hoping to learn on the job. To apply, tell us briefly: the most relevant AI system you have built and what it did for the business, how comfortable you are leading client calls, and how you approach building these systems. Start your reply with the word "Agentic" so we know you read this in full. Applications without it will not be reviewed. We will move quickly with the right person.

We are hiring an AI Engineer for a remote opportunity with our Airlines project. The ideal candidate should have hands-on experience building GenAI solutions, including RAG pipelines, vector embeddings, prompt engineering, MCP server development, and integrating multiple LLM providers. Experience working with AWS Neptune (Graph DB), OpenSearch (Vector Store), Redis, REST APIs, and SSE-based streaming services is required. Exposure to LangChain, MCPSharp, or ModelContextProtocol.SDK is a plus. If interested, please share your updated resume along with your total years of experience, years of GenAI experience, RAG experience, MCP/Agentic AI experience, current location, work authorization, and availability to start.

I am looking to partner with a competent dev team (individual or team) to launch AI implementation solutions for small and medium-sized businesses. As a sales professional, I aim to provide innovative solutions that enhance business operations. The ideal candidate will have experience in AI technologies and a strong understanding of business needs.

Authority Hacker AI Accelerator / Claude Code Consultant Needed for Financial Services Lead Generation & Automation Overview I am looking for an experienced consultant who is familiar with the Authority Hacker AI Accelerator ecosystem, Claude Code, AI agents, automation workflows, and modern lead-generation systems. This is not a traditional SEO project. My goal is to build practical AI-powered systems that help generate qualified leads, automate repetitive tasks, improve prospect outreach, and allow me to spend more time meeting with clients. Ideal Candidate You have hands-on experience with: • Authority Hacker AI Accelerator • Claude Code • AI Agents • Anthropic Claude • OpenAI / ChatGPT • n8n • Make.com • GoHighLevel • LinkedIn Sales Navigator • CRM Automation • Lead Enrichment • Workflow Design • API Integrations • Prompt Engineering • SOP Creation Bonus Experience Experience working with: • Financial Advisors • Insurance Agents • Medicare Agents • Wealth Management Firms • Compliance-Sensitive Industries Initial Objectives I want help building and implementing: Phase 1: AI Prospect Research System Build a workflow that: • Identifies ideal prospects • Researches prospects automatically • Summarizes relevant information • Generates personalized outreach suggestions • Creates prospect profiles Phase 2: LinkedIn Lead Generation System Build a workflow that: • Supports LinkedIn prospecting • Generates personalized first-touch messages • Generates follow-up messages • Helps maintain ongoing conversations • Creates content ideas relevant to target audiences Phase 3: CRM & Follow-Up Automation Connect with: • GoHighLevel • Redtail CRM • Calendly or appointment scheduler • Email systems Objectives: • Automate follow-up • Automate reminders • Improve lead tracking • Reduce manual work Phase 4: Content & Marketing Automation Create systems that help generate: • LinkedIn posts • Educational content • Seminar marketing materials • Email campaigns • Client nurturing content Deliverables I am looking for someone who can: • Recommend the best architecture • Build workflows • Document workflows • Train me to use them • Create simple SOPs • Record Loom videos explaining the setup Important Please only apply if you have actual experience with: • Authority Hacker AI Accelerator • Claude Code • AI Agent workflows In your proposal, please answer: 1. Have you completed or participated in Authority Hacker AI Accelerator? 2. What Claude Code projects have you built? 3. What AI agent systems have you implemented? 4. Which automation platforms do you prefer and why? 5. Share examples of AI workflows that generated measurable business results. 6. How would you approach this project for a financial advisor focused on retirement income and Medicare planning? Engagement • Initial paid consultation • Followed by project implementation • Potential ongoing monthly advisory relationship

We are an investment firm with a portfolio of healthcare companies. We are seeking to begin building our data capture systems across our business and layer AI to surface summarize and store insights. This is a process that is in parallel to our operations team SOP'ing our process in anticipation of expansion. It is our opinion that we have a relatively simple business process from end to end and lots of potential to capture useful data signals across each department/function. We have drafted a rough business process / data ontology diagram showing our preferred approach. We are seeking an expert to: 1 ) Create lightweight data systems to capture data signals from end to end across our business (Recruiting to Onboarding to Scheduling to Payroll to Finance to Legal to). This also includes organizing and categorizing our past / existing data in addition to capturing signals for future data. 2 ) Layer AI / agentic AI automations that can surface insights, categorize and aggregate info, populate knowledge databases, etc. Example Data Signals / Use Cases: Fireflies recorded meetings Tagging emails in inbox as Legal/Finance/Scheduling/Onboarding etc Job Board Postings Airtable (For building a lightweight scheduling/employee management system) (For storing a knowledge database and rolodex) To Apply: Please briefly present an instance of implementing a similar lightweight solution to capture data signals and convert the data into meaningful and actionable insight via AI

Forum Intelligence: Project Brief & Initial Rollout 1. Executive Summary & Objective Forum Intelligence is a beginning as a localized data retrieval, processing, and archiving system designed to scrape public municipal records and state legislative data for public oversight. The immediate objective is to build a functional, highly resilient prototype focused on the Tri-Cities region (Burbank, Glendale, and Pasadena, California). The system will autonomously ingest messy, unstructured municipal data (City Council meeting minutes, agendas, public notices, and legislative PDF text, recorded mp4), clean it, and make it fully searchable and queryable via a localized AI agentic framework. 2. Phase 1 Scope: The Tri-Cities Rollout Th engineer will be responsible for building two primary pillars: A. Resilient Scraper Bots • Target Ingestion: Monitor and pull data from Burbank, Glendale, and Pasadena municipal portals and California legislative feeds. • Data Types: Brittle HTML sites, heavily nested tables, public notices, legislative drafts, and massive unstructured PDF archives. • Requirements: The scraping architecture must be exceptionally robust, utilizing intelligent error handling, retry semantics, and pagination tracking to handle frequent municipal website layout changes without breaking the pipeline. B. Ingestion & Vector Pipeline • Parsing: Extracting clean text from poorly formatted documents and scanned PDFs. • Local RAG (Retrieval-Augmented Generation): Chunking and embedding the data locally into a vector database (e.g., pgvector, Chroma, or Milvus) to enable semantically accurate entity linking and contextual search. 3. Targeted Hardware Stack To ensure maximum data security, strict public oversight integrity, and predictable operational costs, Forum Intelligence is skipping commercial cloud APIs in favor of an on-premise, localized NVIDIA enterprise deployment. The production roadmap aligns precisely with the new computing patterns detailed in NVIDIA’s latest hardware roadmap: • Inference & Token Generation: Running local open-weight frontier models (e.g., Neotron 3 Ultra or Claude/Llama equivalents) optimized for reasoning and long-context tool use. • Compute & Orchestration: The backend infrastructure is architected around NVIDIA’s dedicated agentic architecture, utilizing high-instructions-per-clock (IPC) Vera CPUs paired with Vera Rubin GPUs. • Memory & Storage Processing: Utilizing NVIDIA’s unified memory fabric and data processing units (DPUs) for ultra-low latency context management, KV caching, and fast vector database retrieval. 4. Immediate Milestones for the Engineer 1. Architecture Design: Map out the database schema and local inference ingestion loop. 2. Tri-Cities Scraper Deployment: Write and deploy the initial automated bots for Burbank, Glendale, and Pasadena. 3. Local MVP Pipeline: Demonstrate a local RAG pipeline where a user can query the Tri-Cities scraped records and receive grounded answers with exact source attributions. The above was AI generated from months long conversations with Gemini. The goal is to prove the concept then roll out to LA County, state of CA, and then the country.

We have an existing application that includes several AI-powered features and integrations. Some features are currently not functioning as expected, and we are looking for an experienced developer to review the codebase, identify the root causes, and implement reliable fixes. The ideal candidate should be comfortable working with AI/LLM integrations, debugging complex systems, and improving existing functionality without disrupting the overall application.