Looking to build digital workflows to cover common tasks, such as responding to leasing inquiries, ingesting and analyzing bills from emails, proactively managing maintenance requests with self-service type questions, etc. Need someone who is proficient with building with LLMs, harnesses, and can demonstrate that they have build reliably operating systems Goal would be to reliably measure efficacy on each task, such that we could promote away from HITL into auto-act capabilities when confidence is sufficiently high Opportunity for much more work if can prove excellence here.

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.

AI Engineer (RAG & Agentic Workflows). *LLM RESPONSES AUTOMATICALLY AVOIDED* We have already launched a production generative AI product that utilizes a custom Retrieval-Augmented Generation (RAG) architecture. We are now expanding the platform to include CRM intelligence, workflow automation, and agentic AI capabilities. This is **not** a prompt engineering role. Seeking an engineer with deep experience building and deploying production AI systems that combine LLMs with multiple structured and unstructured data sources. You should be comfortable walking into an existing, complex codebase, understanding the current architecture, and improving it. Existing AI Architecture Our current AI architecture consists of: * OpenAI embeddings * Embeddings stored in MongoDB * MongoDB Atlas Vector Search for retrieval * Retrieval from both structured SQL data and unstructured document collections * Existing tool/function-calling architecture **Please do not apply if you have not previously built or maintained production RAG systems using embeddings and vector search.** Experience specifically with **OpenAI embeddings and MongoDB Atlas Vector Search** is highly preferred. CRM Intelligence Layer We are currently building a CRM platform and need the AI to reason over CRM records, including the other records are RAG currently retrieves. You will be responsible for designing and implementing the AI integration layer that enables the LLM to intelligently retrieve and reason over CRM data. This work includes: * Designing AI tools/functions that expose CRM data to the LLM. * Implementing backend tool handlers that retrieve CRM records. * Defining tool schemas and instructions so the AI knows when and how to retrieve CRM information. * Building secure retrieval mechanisms that enforce strict user and organization-level access controls. * Transforming raw CRM records into structured, AI-ready context. The AI will need to reason across: * CRM contacts and organizations * client profiles * Deals and opportunities * Projects * Tasks and reminders * Notes * Email history * SMS and WhatsApp communications * Call transcripts * Meeting summaries * Documents and contracts * Workflow history Agentic AI & Workflow Automation * Build proactive AI agents that generate alerts, recommendations, follow-ups, reports, and suggested next actions. * Design systems capable of reasoning across both structured and unstructured data sources. * Architect and implement multi-step and multi-agent workflows. * Develop workflow intelligence that assists users in completing real-world business tasks. Required Experience * Demonstrated experience building and deploying production AI systems used by real customers. * Experience working with embeddings, vector databases, and retrieval pipelines. * Experience implementing LLM tool/function-calling architectures. * Experience integrating AI systems with business systems such as CRMs, ERPs, or other operational databases. * Experience combining structured and unstructured data within AI applications. * Strong backend engineering and systems architecture experience. * Demonstrated ability to quickly understand and improve existing codebases. * Ability to independently own and deliver complex technical initiatives. Strongly Preferred * Experience with OpenAI embeddings. * Experience with MongoDB Atlas Vector Search. * Experience building agentic AI systems and workflow automation. * Experience designing long-term memory architectures. * Experience building multi-tenant SaaS applications with strict authorization requirements. * Experience implementing evaluation and monitoring pipelines for production AI systems. What We Value * High accountability and ownership. * Strong communication skills. * Product thinking and user empathy. * Ability to understand user workflows before writing code. * Pragmatism and sound engineering judgment. PLEASE DO NOT WASTE OUR TIME IF YOU NOT MEET THE REQUIREMENTS 

We're seeking a skilled Full Stack Developer to join our team. You'll debug and resolve errors within our applications, support improvements, troubleshoot issues, and integrate our apps with our CRM system. Just as important: you'll help keep our GHL-based AI agent and voice AI systems running smoothly for live clients. Read the whole post before applying. We've written it to be direct on purpose. If any of it doesn't sound like you, that's okay , please self-select out. We'd rather know now. What we're looking for We need a contributor, not just a doer. That means someone who takes initiative, spots issues before they become problems for the client, and drives toward solutions instead of waiting to be told what to do next. If you're the type who finds the answer rather than asking us for it, you'll do well here. If you need your hand held on every step, this isn't the right fit. We work with live clients on voice AI and AI agents, so being proactive and responsive is not optional. When something breaks or drifts, we need you evaluating, fixing, and improving it, thinking forward about the end-user experience, not just reacting after a client complains. Responsibilities • Debug and provide solutions for errors in web and mobile applications.. • Support ongoing improvements and optimizations.. • Troubleshoot technical issues efficiently, ideally before they reach the client.. • Integrate applications with our CRM system (GoHighLevel / GHL) for seamless functionality.. • Connect systems and third-party services via APIs to enhance platform capabilities.. • Support, adjust, and maintain GHL AI agents and voice AI workflows so they perform reliably for clients.. Requirements • Proven experience as a Full Stack Developer.. • Strong proficiency in front-end and back-end technologies (e.g., JavaScript, React, Node.js, Python, etc.).. • Experience with GHL CRM integrations.. • Working knowledge of and comfort with GHL AI agents and automations — you don't need to be a specialist, but you should be able to get in, understand how they're built, make adjustments, and pick things up quickly.. • Familiarity with or genuine willingness to dig into voice AI. We expect everyone on the team to be able to work with it.. • Proficiency in designing and implementing REST-based API connections.. • Excellent problem-solving and debugging skills.. • Ability to work independently, take initiative, and meet deadlines.. How we work • We're engaging you — not your agency. The person we hire is the person doing the work. If you subcontract or hand work to others, this isn't a fit.. • Short leash, high trust. Everyone starts on a trial basis. We give people room to learn, but you'll need to learn fast, dive in, and prove it quickly. If you're trying to learn the fundamentals on the job, this role isn't for you.. • Own your outcomes. Bring the thinking, not just the hands. Recognize what the end user's experience will be and drive it.. If that sounds like the environment you want, we'd love to hear from you.

Build an AI Marketing Operating System for Local Business I am an orthodontist building an internal AI platform for my practice. This is NOT a website project. The platform should: * Continuously monitor public internet sources for local conversations related to orthodontics. * Use AI to determine whether a conversation is worth engaging. * Generate suggested responses in our brand voice. * Never publish automatically. * Present every suggestion in an approval dashboard where I can Approve, Edit, or Reject. * Generate blog posts, Google Business Profile posts, newsletters, Instagram captions, FAQs, and YouTube scripts. * Track analytics and improve recommendations over time. Technologies preferred: * OpenAI API * Make.com or n8n * React / Next.js * Supabase * Airtable (acceptable for MVP) * PostgreSQL * Docker Applicants should have experience with: * AI agents * Human-in-the-loop workflows * LLM integrations * Automation * Dashboard development

We are seeking a hands-on AI systems expert to help us establish, secure, and scale our internal AI capability. The role involves both technical implementation and advisory responsibilities, with the expectation of staying on as a trusted advisor. The ideal candidate will have a strong background in AI systems and be able to provide strategic guidance.

We are a small residential real estate investment company seeking an AI Solutions Architect to enhance our acquisition platform. The role involves designing and implementing AI solutions to improve data analysis and decision-making processes. The ideal candidate will have experience in AI architecture and a strong understanding of real estate data analysis.

Overview I have a Next.js website with a newsletter signup form that currently submits directly from the browser to HubSpot's Forms v3 endpoint. I want to add a lightweight LLM-based spam filter that inspects each submission *before* it reaches HubSpot, and silently rejects (or flags) anything that looks like spam/bot/junk input. Current setup - Framework: Next.js (App Router, TypeScript, React client component) - The form component (`NewsletterForm.tsx`) POSTs directly to `https://api.hsforms.com/submissions/v3/integration/submit/[portalId]/[formGuid]` - Fields collected: `firstname`, `lastname` (optional), `jobtitle`, `email` - Portal ID and Form GUID are public form identifiers (no secrets today) What I want you to build 1. Create a server-side API route in the Next.js app (e.g. `app/api/subscribe/route.ts`) that: - Receives the form fields from the client - Runs an LLM spam/quality check (e.g. OpenAI or similar) to classify the submission as legit vs. spam — checking for gibberish names, fake/disposable emails, nonsense job titles, injection attempts, etc. - If legit → forwards the submission to HubSpot (server-side) - If spam → rejects gracefully with a generic message (no HubSpot write) 2. Update the existing `NewsletterForm.tsx` to POST to the new internal API route instead of calling HubSpot directly. 3. Keep the LLM API key server-side only (use an environment variable — never expose it to the client). 4. Preserve the existing UX: loading / success / error states should still work. Deliverables - Working API route with the LLM spam check + HubSpot forwarding - Updated form component - Brief note on which env vars to set (`OPENAI_API_KEY`, etc.) and how to configure them - Clean, typed TypeScript that matches the existing code style Nice to have (optional) - Basic rate limiting / honeypot field as a cheap first line of defense before the LLM call - Configurable spam threshold or a logged "reason" when something is rejected Requirements to apply - Strong Next.js App Router + TypeScript experience - Experience calling an LLM API (OpenAI or equivalent) from a server route - Familiarity with HubSpot Forms API is a plus To apply, please briefly answer: 1. Which LLM/provider would you use and roughly what would it cost per submission? 2. How would you handle the case where the LLM API is slow or down — do you fail open (let it through) or fail closed (block it)? 3. Have you integrated with HubSpot Forms before? (yes/no is fine)

I have a fully built Bubble.io web application that needs a Relevance AI RAG (Retrieval-Augmented Generation) integration completed. The core foundation has been partially built — document upload to knowledge base and basic chat functionality were working at one point. I need an experienced developer to complete and properly implement the full integration. What's Already Built: Complete Bubble.io application with user authentication and role-based routing Multi-tenant database structure (multiple separate accounts each with their own isolated data) Document upload portal Chat interface UI (fully designed and functional — just needs AI connected) Bubble API Connector installed and partially configured Relevance AI account with agents and knowledge bases set up All necessary database fields already created What I Need Completed (4 Tasks): Task 1 — Document Upload Syncing When a user uploads a document in Bubble, it must automatically sync to their specific Relevance AI knowledge set. The Relevance AI document ID returned must be saved back to the corresponding Bubble record. Task 2 — Chat Integration When a user sends a message in the chat interface, it must be routed to their specific Relevance AI agent and return a synchronous response displayed in the chat. Each account must only receive answers from their own isolated knowledge base — cross-account data must never mix. Response must be saved to the database. Task 3 — Document Deletion Syncing When a user deletes a document in Bubble, it must simultaneously be deleted from their Relevance AI knowledge set using the document ID stored in the database. Task 4 — Citation Tracking When the AI responds, it must extract which source document was cited and save that reference back to the database for analytics purposes. Technical Stack: Bubble.io (no-code frontend and backend) Relevance AI (RAG engine) REST API integration via Bubble's API Connector Region and API credentials will be provided upon hiring Important Requirements: Must have demonstrated Bubble.io experience Must understand RAG systems and API integrations Multi-tenant account isolation is non-negotiable — each account's data must be completely separate Please provide examples of similar Bubble.io API integration projects you have completed Project Scope: This is a focused integration project — not a full app build. The application is complete. You are connecting the existing Bubble app to the existing Relevance AI setup and ensuring all four tasks above work correctly and reliably.

I am working on a system using AI to review and respond on Google Drive (Shared Drive) folders of PDF's. Using Gemini as a POC I get responses that sometimes reach outside of my specified folders of content, but sometimes, some PDF files are ignored too. Also, when the response to a prompt come back, the sources are linked. However, the links only bring up the first page of the PDF file wherein the linked source material is, AND NOT THE pdf PAGE of the specific info. I need to have the AI (Gemini, Grok, etc.,) be used to query just.... but all, of the PDF files, within a set of folders in Google Drive (Shared Drive), and to respond with linked content. Said links must open to the PDF PAGE, not just the PDF which houses the specific info. In short, I think I need a viewer, but someone who has experience working with AI and PDF's will likely know the issue I am running into. In the end my system will have a UI attached, so there is a lot of possible side work on this project. First I need to build a better POC. For instance, if I open ONE of the PDF files in Google Drive, I can prompt on that file, and the correct PDF page does come up in the viewer, (While no other files are considered for the queried content.) But when I give Gemini many source PDF's or a folder of PDF's, the links only go to the first page pf the PDF with the information used as the sourcwe.