We have a free to paid Conversational Voice Ai service for the Insurance Industry. We are generating a lot of interest in free trails but are having a hard to converting to paid. Need my marketing automation workflow reviewed for suggestions and ways to optimize so we convert free to paid.

**Overview** We are a fast-growing SaaS company with a lean engineering team (~10 devs) utilizing a modern Python (FastAPI/Django) and Node.js backend, React frontend, and PostgreSQL stack. We have already deployed an initial multi-model agent stack—Claude Code, LiteLLM gateway, Git worktrees, and MCP integrations. We need an expert to run an intensive architecture review and optimization session for our current infrastructure. We are not looking for someone to build a full-time, weeks-long project from scratch. Instead, we need a seasoned engineer who has shipped this exact type of infrastructure end-to-end to audit our setup, identify architectural gaps, and guide our team on hardened implementation. This project must move fast. If your timeline is measured in weeks, please do not apply. We want someone who looks at this scope, jumps into a review session, and delivers actionable architectural guidance in days. This starts as a focused, urgent consultation. However, we expect ongoing advisory work—follow-ups, architecture adjustments, and enhancement reviews—as the AI tooling landscape shifts. For the right engineer, this will turn into a recurring relationship. We are completely open to a fixed price per milestone or an hourly structure. **What You Need to Have Actually Shipped and Can Review (Not Just Read About)** * **Full Agentic Coding Harnesses:** The entire loop: orchestrator → subagent → CI gate → merge loops. * **Isolation Layers:** Configured execution layers (such as E2B, Modal, or secure Docker runtimes) as isolated sandboxes for AI-generated code. * **Parallel Claude Code Sessions:** Managed multiple simultaneous subagents on scoped tasks via Git worktrees. * **Self-Hosted LiteLLM Gateways:** Routing to multiple models (Claude, GPT, Gemini, DeepSeek). * **MCP Server Infrastructure:** Connected file system, PostgreSQL, Atlassian, and Slack tool layers for active agents. * **Agent Framework Structures:** Used CLAUDE.md, COMMON\_MISTAKES.md, subagent role definitions, hook scripts, and settings.json. * **Human-in-the-Loop Orchestration:** Built Plan Mode or equivalent approval gates before agent execution. * **Multi-Agent Frameworks:** 7-agent feature factory patterns or frameworks like LangGraph, CrewAI, or Autogen. * **Durable Workflow Engines:** Applied Temporal, n8n, or similar tools for long-running agent workflow execution. * **Mechanical Quality Gates:** Treating CI green as the ultimate gate for agent output quality. \[[1](https://manveerc.substack.com/p/ai-agent-sandboxing-guide)\] **Our Current Stack (What you are reviewing)** * **Backend:** Python (FastAPI / Django) & Node.js (TypeScript) * **Frontend:** React (Next.js) * **Database & ORM:** PostgreSQL / Prisma / SQLAlchemy * **Infrastructure:** Docker Compose, AWS (ECS/EKS) * **CI/CD:** GitHub Actions / GitLab CI * **AI Layer:** Claude Code with shared `.claude/` directory, CLAUDE.md, and LiteLLM gateway in Docker * **MCP:** Atlassian (Jira/Confluence), custom PostgreSQL MCP server, Slack * **Workflow Automation:** Temporal / n8n * **QA Automation:** Playwright / Autonoma **Scope of Work (Review & Advisory Only)** 1. **Comprehensive Audit:** Audit our current agent harness and identify architectural gaps against a production-grade standard. 2. **Sandbox Strategy Consultation:** Review our environment strategy to ensure highly secure, isolated execution runtimes for agent code runs. 3. **Workflow Hardening Review:** Evaluate our parallel agent workflow setup (Git worktrees, subagent role configs, hook scripts, and settings lockdown). 4. **CI Pipeline Integration Strategy:** Advise on wiring our sandbox execution layer into the existing CI pipeline so agent-executed code runs in clean snapshots, not live infra. 5. **Architectural Runbook:** Deliver an optimization report / documented standard that our backend lead can easily own and execute going forward. **How to Apply** Skip the generic pitch. Show us something real to be considered: 1. A GitHub repo, architecture diagram, or Loom walkthrough of an agentic harness you have actually shipped. 2. Specific tools from our stack you have personally configured (E2B, LiteLLM, Claude Code, etc.). 3. One sentence explaining the hardest problem you solved to get full agent loops running reliably. 4. Your availability to conduct this high-impact architectural review session this week.

Overview We run a high-volume B2B cold email program for a national background screening company. The infrastructure is built and live. We need an experienced operator to take full ownership — running the system, maintaining deliverability, managing lead pipelines, and continuously improving performance. This is not a setup job. This is an ongoing management role for someone who already knows these tools and can hit the ground running with minimal handholding. What You'll Own Lead Sourcing & Scraping — Build and manage Apify scraping workflows to pull targeted prospect lists from the right sources. You understand what makes a list clean versus garbage, and you enforce quality before anything touches an inbox. Email Infrastructure — Manage sending domains and inboxes in Scaledmail and Instantly. This means monitoring domain health, rotating inboxes appropriately, maintaining warmup, and keeping bounce rates low. You know what a burned domain looks like before it's actually burned. Automation & CRM Integration — Maintain and improve Make.com workflows that move verified leads from sourcing → enrichment → Instantly → HubSpot. Automations must be reliable and auditable. You're comfortable debugging broken scenarios and building new ones. Email Verification — Manage AnyMailFinder to verify contacts before they enter any sending sequence. You understand verification thresholds and make judgment calls on borderline lists. Campaign Management — Different sequences go to different audiences. You'll manage which contacts flow into which campaigns, monitor performance, and make copy or sequencing adjustments based on what the data shows. Reporting — Weekly summary of key metrics: open rates, reply rates, bounce rates, domain health, inbox placement, leads delivered to HubSpot. Flag issues before they become problems. This Role Is Right for You If: You've managed cold email programs at 30K–100K+ emails/month and know what breaks at scale You treat deliverability as non-negotiable — domain reputation is something you protect, not react to You're a systems thinker who builds processes that don't require babysitting You proactively flag when something looks off — you don't wait to be asked You can look at reply rate and bounce data and know what to adjust without being told You've personally managed Instantly, Apify, Make.com, Scaledmail, and AnyMailFinder — not just heard of them Hard Requirements Hands-on experience with Instantly (inbox management, campaign structure, sending limits, warmup) Hands-on experience with Apify (building or running scraping actors for lead generation) Hands-on experience with Make.com (multi-step automation scenarios, error handling, webhook flows) Experience with AnyMailFinder or comparable verification tools (not just "I've used email verification") HubSpot CRM experience — contacts, lifecycle stages, list management, basic workflow logic Demonstrated ability to keep bounce rates under 3% at volume English fluency — you'll be reading and interpreting campaign data and communicating findings clearly Nice to Have Experience with B2B lead generation for HR, workforce, or compliance-adjacent industries Copywriting or sequencing instincts — you can spot a weak subject line or a broken CTA Experience managing multiple client programs simultaneously (you know how to context-switch without dropping balls) To Apply Please include in your proposal: A brief description of the largest cold email program you've personally managed (volume, tools, outcomes) Your approach to maintaining deliverability at scale — specifically what you monitor and how often One example of an automation or workflow you built in Make.com for a lead gen or email program Your current availability (hours/week) and any other active commitments Proposals without these four items will not be reviewed. A note on fit: We're not looking for someone to check boxes. We want someone who treats this program like it's their own — who notices when something's underperforming and already has a fix in mind before we ask. If that's not how you work, this isn't the right role.

We're a growing service company looking for an experienced developer to build a Slack bot that answers employee questions about our HR policies, SOPs, and internal documentation. Team members will tag the bot in a channel, ask a question in plain language, and receive a conversational, accurate answer grounded in our documented materials. **This is a build + teach engagement.** I have no coding background, and a core requirement of this project is that you walk me through your decisions and architecture as you build, so I can understand, maintain, and eventually extend the system myself. If you're a strong developer but don't enjoy explaining your work, this isn't the right fit. ## What You'll Build A production-ready Slack bot with the following architecture: - **Slack integration** using Slack's Bolt framework (Python or Node.js — your recommendation welcome) - **Retrieval-Augmented Generation (RAG)** pipeline: questions are matched against our documentation via semantic search, and relevant context is passed to an LLM for a conversational answer - **Vector database** (Pinecone, Weaviate, or a comparable option you can justify) storing embeddings of our policies, SOPs, and transcripts - **OpenAI API** integration for embeddings and chat completions - **Document ingestion pipeline** that can handle multiple source formats: Word docs, PDFs, spreadsheets, and plain-text transcripts (e.g., exported Loom video transcripts) - **Source citations** in bot answers, so users can see which policy or document the answer came from - Deployment to a cloud environment (AWS, Heroku, Railway, or similar) with clear instructions for how it runs and how to restart or update it ## Technical Requirements You should have demonstrable experience with: - Slack app development (Bolt framework, event subscriptions, OAuth/permissions setup) - OpenAI's API (chat completions and embeddings) - RAG architecture and vector databases (Pinecone, Weaviate, Qdrant, pgvector, or similar) - Python or Node.js backend development - Cloud deployment and basic DevOps (environment variables, API key security, uptime) **In your proposal, please link to or describe at least one similar project you've built** — ideally a Slack bot, a RAG system, or an LLM-powered internal tool. ## Deliverables 1. A working Slack bot deployed to production and connected to our Slack workspace 2. Document ingestion process (with instructions or a simple tool for me to add new documents myself as our documentation grows) 3. Full source code in a repository I own, with clear comments 4. **Written documentation** covering: system architecture, how each component connects, how to add/update documents, how to update API keys, and common troubleshooting steps 5. **Teaching sessions**: recorded screen-share walkthroughs (or live calls) at each major milestone explaining what was built and why — I estimate 3–5 sessions of 30–60 minutes 6. A handoff session at the end where we test the bot together and review maintenance procedures ## Communication & Working Style - Regular progress updates (at minimum, 2x per week) - Willingness to explain decisions in plain English, not just technical jargon - Patience with beginner questions — teaching is part of the paid scope, not a favor - Fluent written and spoken English - Availability for scheduled video calls (please note your time zone in your proposal) ## Scope Notes - Initial document set is modest, but the system should be designed to scale as our documentation library grows significantly - Future phases may include: automatic transcript ingestion from Loom, additional Slack channels/workflows, and analytics on what questions get asked — mention if you have experience with any of these - I will provide: Slack workspace admin access, OpenAI API account, and all documentation to be ingested ## How to Apply In your proposal, please include: 1. A brief description of a similar project you've built (links or screenshots appreciated) 2. Your recommended tech stack for this project and a one-paragraph explanation of why 3. Your approach to the teaching/documentation component 4. Estimated timeline and total cost (fixed price preferred; open to milestone-based payment) 5. Your time zone and general availability Proposals that are clearly personalized and address the teaching component will be prioritized. Generic copy-paste proposals will be declined.

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 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 seeking an expert in GoHighLevel to assist with setting up and optimizing our website chat bot AI agent. It is already connected to the website, we just need someone that can help us configure it properly so that potential clients can interact with it

Create an A.i sales agent that automates tasks in real estate

Overview We run an AI voice assistant for self-storage operators. We have an internal, AI-assisted workflow for triaging call feedback — investigating what happened on a call, diagnosing the root cause in our codebase, and drafting fixes. We’re looking for someone technical to run that AI-assisted workflow day to day and help us make it better. You’ll be driving AI coding agents, reading real code to understand behavior, and improving the process and tooling itself. What you’ll do Use our AI agent tooling to work through a queue of customer feedback on AI voice calls. Read our TypeScript/Node codebase (voice-agent prompt assembly, workflow/“SOP” engine, tool implementations) to diagnose why the agent behaved a certain way — not just guess. Draft fixes: workflow-instruction edits, knowledge-base entries, or code changes via pull request with a clear verification plan. Improve the triage process itself — refine the AI agent prompts/skills, conventions, and the internal MCP tooling that powers it. Write clear, customer-facing summaries of what changed for our team to review and approve. You’re a great fit if you Read and reason about code confidently — ideally TypeScript/Node; React a plus. Have hands-on experience driving AI coding agents (Claude Code, Cursor, or similar) and understand how LLM prompts/tools/agents fit together. Think in cause-and-effect: “the agent did X because line Y / instruction Z.” Write precisely and concisely for both technical and non-technical audiences. Are process-minded — you spot the repetitive thing and turn it into a better workflow. Bonus: prompt engineering, LLM tool/agent development, or voice/conversational AI experience. How we work We’ll start with a paid trial on a small batch, then scale steady ongoing volume. To apply: Tell us about a time you used an AI coding agent to diagnose or fix something non-trivial in a codebase you didn’t write — what you did, and how you verified it worked. A link to relevant work is a plus.

### Job Description: AI Chatbot Developer We are excited to announce an opening for an experienced and innovative developer to join our dynamic team in the pursuit of creating an advanced AI Chatbot. This chatbot will be designed to perform essential business functions, including but not limited to lead generation, quoting, and providing exceptional customer support. Our ideal candidate will possess a robust background in AI technologies, particularly in the realm of chatbot development, and will be equipped with outstanding problem-solving skills that enable them to tackle complex challenges with creativity and efficiency. As a key member of our development team, you will collaborate closely with various departments to gain a comprehensive understanding of our specific operational needs and requirements. Your ability to translate these needs into a functional and user-friendly chatbot solution will be critical to enhancing our overall operational efficiency. We are looking for someone who is not just technically proficient but also possesses a keen sense of business acumen to ensure that the chatbot aligns with our strategic goals. In this role, you will be responsible for various aspects of the chatbot development lifecycle, including but not limited to: - Designing and developing the conversation flow and user interface of the chatbot, ensuring it is intuitive and engaging for users. - Implementing natural language processing (NLP) capabilities to enable the chatbot to understand and respond to user inquiries accurately. - Integrating the chatbot with existing systems and databases to facilitate seamless access to information necessary for lead generation, quoting, and customer support functions. - Conducting rigorous testing and quality assurance to ensure the chatbot performs reliably and meets user expectations. - Analyzing user interactions and feedback to continuously improve the chatbot's performance and expand its capabilities over time. - Staying current with the latest advancements in AI technologies and chatbot development to incorporate best practices and innovative solutions. You will also play a crucial role in training team members on how to utilize the chatbot effectively and will be expected to provide ongoing support and maintenance to ensure the chatbot remains up-to-date and functional. If you have a passion for artificial intelligence, a deep understanding of customer engagement strategies, and a desire to make a significant impact within our organization, we would love to hear from you! Join us in revolutionizing the way we interact with our customers and streamline our business processes through cutting-edge technology. This is a fantastic opportunity for someone looking to advance their career in a fast-paced, forward-thinking environment. Apply today and be part of our exciting journey towards enhancing our customer experience through AI!