We're a 30-year, family-owned home improvement company in Michigan. We run a modern but disconnected software stack and we're looking for a developer to connect the pieces, automate manual work, and build reporting that actually gets used. This is project-based contract work to start, with steady ongoing work for the right person. Not a full-time role. What you'd be working on: Our tools don't talk to each other and too much still runs on manual data entry and spreadsheets. We want someone who can look at a workflow, find the bottleneck, and build the fix. Examples of projects on our list: Replace an antiquated Google Sheets scheduling system with something modern and connected Build and maintain integrations between our core tools (CRM, call tracking, phone, accounting) Build BI and reporting dashboards that pull from multiple sources into one clear view Automate manual data entry, lead routing, and reporting tasks Set up AI-assisted tooling where it makes sense (call summaries, automated reporting, data cleanup) Our stack: JobNimbus (CRM, system of record) CallRail (call tracking, ~50 numbers) RingCentral (phone) Rilla (sales conversation analysis) QuickBooks (accounting) Google Ads and Facebook Ads Google Sheets / Google Workspace You should have: Strong experience with API integrations and connecting SaaS tools (Zapier/Make is fine for some of it, but we want someone who can write custom code when the no-code tools fall short) Experience building reporting dashboards or BI tools Comfort with one or more CRM platforms (JobNimbus experience is a big plus) Ability to scope a problem, propose a solution, and ship it without heavy hand-holding Clear written communication and the ability to explain technical work to non-technical people Nice to have: Experience with JobNimbus, CallRail, RingCentral, or QuickBooks specifically Experience building AI-assisted workflows (LLM-based summaries, data extraction, etc.) Experience in home services, construction, or contracting businesses

We are a fast-growing telecom / AI-First CPaaS serving sms and voice API's. We are building the first AI-first communications platform (SMS, Voice, RCS, AI agents) designed for speed, simplicity, and real-world business outcomes. We are not looking for a “task completer.” We are looking for a true senior engineer who: thinks in systems moves fast makes decisions independently writes clean, scalable code uses AI tools (Claude, etc.) as a force multiplier ⚠️ Read This First *DO NOT APPLY IF YOU ARE PRETENDING TO BE IN A DIFFERENT COUNTRY. PROOF OF RESIDENCY IS REQUIRED. Most applicants will not be a fit. If you need: detailed tickets hand-holding constant direction This is NOT the role for you. If you are the type of engineer who: sees a problem and solves it end-to-end improves architecture without being asked ships quickly without sacrificing quality You will thrive here. What You’ll Do Build and ship full-stack features across our platform (messaging, voice, AI workflows) Make architectural decisions (not just implement) Improve system performance, reliability, and scalability Work directly with founders (no PM layers) Move from idea → production very quickly What We Expect (Non-Negotiable) 5+ years real full-stack experience (not just titles) Strong backend experience (Node.js / APIs / infra) Strong frontend experience (React or similar) Experience building production systems at scale Ability to work autonomously with minimal direction High ownership mentality Bonus (but highly valuable) Experience with telecom / CPaaS / messaging Experience with AI integrations (LLMs, agents, workflows) Experience optimizing performance at scale Startup experience (especially early-stage or fast growth) How We Work Small, high-output team Very fast iteration cycles No unnecessary meetings High trust, high expectations We use AI tools heavily (Claude, etc.) — you should too What We Care About Most Not your resume. We care about: How you think How you build How fast you execute The quality of your code To Apply Please include: Links to projects you’ve built (real production work) A short explanation of: a system you designed end-to-end a difficult technical decision you made independently Your GitHub Optional (but strong signal): Share how you use AI (Claude, etc.) in your workflow Compensation Competitive (based on experience) Long-term opportunity with a fast-growing, profitable company If you are truly senior, this will feel obvious. If not, this role will be very uncomfortable. **THIS IS A FT, HOURLY ROLE. PROVIDE YOUR REQUESTED HOURLY RATE IN PROPOSAL**

We are a fast-growing telecom / AI-First CPaaS serving sms and voice API's. We are building the first AI-first communications platform (SMS, Voice, RCS, AI agents) designed for speed, simplicity, and real-world business outcomes. We are not looking for a “task completer.” We are looking for a true senior engineer who: thinks in systems moves fast makes decisions independently writes clean, scalable code uses AI tools (Claude, etc.) as a force multiplier ⚠️ Read This First *DO NOT APPLY IF YOU ARE PRETENDING TO BE IN A DIFFERENT COUNTRY. PROOF OF RESIDENCY IS REQUIRED. Most applicants will not be a fit. If you need: detailed tickets hand-holding constant direction This is NOT the role for you. If you are the type of engineer who: sees a problem and solves it end-to-end improves architecture without being asked ships quickly without sacrificing quality You will thrive here. What You’ll Do Build and ship full-stack features across our platform (messaging, voice, AI workflows) Make architectural decisions (not just implement) Improve system performance, reliability, and scalability Work directly with founders (no PM layers) Move from idea → production very quickly What We Expect (Non-Negotiable) 5+ years real full-stack experience (not just titles) Strong backend experience (Node.js / APIs / infra) Strong frontend experience (React or similar) Experience building production systems at scale Ability to work autonomously with minimal direction High ownership mentality Bonus (but highly valuable) Experience with telecom / CPaaS / messaging Experience with AI integrations (LLMs, agents, workflows) Experience optimizing performance at scale Startup experience (especially early-stage or fast growth) How We Work Small, high-output team Very fast iteration cycles No unnecessary meetings High trust, high expectations We use AI tools heavily (Claude, etc.) — you should too What We Care About Most Not your resume. We care about: How you think How you build How fast you execute The quality of your code To Apply Please include: Links to projects you’ve built (real production work) A short explanation of: a system you designed end-to-end a difficult technical decision you made independently Your GitHub Optional (but strong signal): Share how you use AI (Claude, etc.) in your workflow Compensation Competitive (based on experience) Long-term opportunity with a fast-growing, profitable company If you are truly senior, this will feel obvious. If not, this role will be very uncomfortable. **THIS IS A FT, HOURLY ROLE. PROVIDE YOUR REQUESTED HOURLY RATE IN PROPOSAL**

We are building a HIPAA-compliant SaaS platform for medication stewardship in skilled nursing facilities (SNFs). The platform allows clinical pharmacy consultants and providers to upload scanned medical documents, run AI-powered medication and disease state reviews, and generate clinical findings — all without storing any patient data. This is a focused, well-defined MVP. No scope creep. We need a developer who moves fast, communicates clearly, and has real experience with HIPAA-eligible AWS architecture. Core concept — stateless processing: This platform is intentionally stateless. Documents are uploaded, processed through OCR, analyzed by AI, and the findings are displayed to the user. Nothing is written to a database. No patient data or documents are retained after the session ends. The platform processes PHI transiently and discards it — significantly simplifying the HIPAA footprint while maintaining compliance. What you will build: 1. AWS infrastructure (HIPAA-eligible, stateless) — S3 used only as a temporary processing buffer (files deleted immediately after OCR completes) — AWS Textract for OCR processing of scanned PDFs and images — AWS Bedrock (Claude Sonnet) for AI-powered clinical analysis — AWS Cognito for user authentication only (no clinical data stored) — AWS Amplify or CloudFront for React frontend hosting — KMS encryption for data in transit — All services configured under AWS BAA coverage — No RDS or persistent database required for clinical data 2. React frontend — Clean single-page application — Document upload UI (drag/drop, supports PDF and image files) — OCR text display with basic edit capability before analysis — Free-text question input (user asks Claude questions about the document) — Claude response display panel — Copy to clipboard button on all output — User login and profile page (name, email, facility) — Membership and billing settings page — Stripe monthly subscription integration 3. HIPAA compliance — Stateless architecture — no PHI persisted after session — HTTPS enforced on all endpoints — AWS BAA signed and covering all services — User BAA acknowledgment on signup — Audit logging for access events — Privacy policy and terms of service integration What we are NOT building in this phase: — Mobile app — EHR or PointClickCare integration — Stored intervention history or dashboard — Cost savings calculator — Admin panel — Anything beyond the three core features above: upload, analyze, copy output Ideal candidate: — 3+ years React and AWS experience — Prior HIPAA-eligible AWS builds — please describe your specific experience in your proposal — Hands-on experience with AWS Textract or comparable OCR pipelines — Familiarity with AWS Bedrock or direct LLM API integrations — Experience with stateless or ephemeral data processing architectures — Stripe subscription integration experience — Strong communicator — weekly video check-ins required — Available to start within 2-4 weeks Engagement details: — Estimated scope: 40–60 hours — Timeline: 8–10 weeks — Budget: $2,500–$4,500 USD fixed price preferred — Payment milestones: 25% upfront, 25% at working OCR pipeline, 25% at working Claude integration, 25% at launch — Communication: Weekly video check-in + async messaging How to apply: In your proposal please answer these four questions specifically: 1. Describe a HIPAA-eligible AWS application you have built — what services did you use and how did you handle PHI? 2. Have you implemented stateless or ephemeral document processing before? How did you approach it? 3. What is your experience with AWS Textract or other OCR pipelines? 4. How would you integrate AWS Bedrock or a Claude API call into a React frontend securely? Proposals that do not answer these four questions will not be considered. About us: We are an early-stage clinical SaaS platform founded by a Clinical Pharmacy Specialist. We are building a tool that genuinely improves patient care and safety in long-term care settings. We want a developer who takes pride in clean, secure, well-documented code and wants to be part of building something meaningful in healthcare. If that is you, we would love to hear from you.

**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.

The Client seeks an experienced AI development team to design and build a secure web-based document intelligence platform capable of analyzing multiple related documents, extracting key information, identifying inconsistencies, and generating issue reports. The platform will support complex document sets where information must remain consistent across multiple files and versions. The initial scope focuses on document ingestion, data extraction, cross-document analysis, issue identification, and reporting. Business Objective Develop a scalable SaaS application that enables users to: • Upload and organize multiple related documents • Extract key terms, dates, parties, financial values, and references • Compare information across documents • Identify inconsistencies and missing information • Generate issue reports and review summaries • Maintain document version history • Provide an intuitive dashboard for issue management Phase 1 – Document Ingestion and Processing Requirements Develop a secure document upload module supporting: • PDF • Microsoft Word (.docx) • Microsoft Excel (.xlsx) • Text files System shall: • Extract text from uploaded files • Preserve document structure • Capture headings and section hierarchy • Process tables and schedules • Index document content for search and retrieval Phase 2 – Data Extraction Engine The platform shall automatically identify and extract: • Defined terms • Parties and entities • Dates • Numerical values • References to exhibits and schedules • Section references • Key metadata Extracted information shall be stored in a searchable database. Phase 3 – Cross-Document Consistency Review The platform shall compare extracted information across multiple documents and identify: • Inconsistent terminology • Conflicting dates • Conflicting numerical values • Missing references • Undefined terms • Duplicate provisions • Broken cross-references Examples include: • Same entity referenced using multiple names • Different numerical values for the same item • References to sections that do not exist • Missing exhibits or attachments Phase 4 – AI Review and Issue Identification The platform shall integrate a Large Language Model (LLM) to perform contextual analysis. The AI engine shall: • Summarize document contents • Identify potential drafting inconsistencies • Highlight missing information • Generate issue descriptions • Assign issue severity levels • Provide suggested corrective actions Phase 5 – Dashboard and Reporting Develop a web-based dashboard including: Transaction Workspace • Document list • Upload history • Processing status • Review status Issue Tracker • Issue category • Issue severity • Source document • Description • Resolution status Search Functionality Search by: • Term • Date • Party • Numerical value • Document name Reporting Generate downloadable reports in PDF and Excel format. Technical Requirements Frontend • React or Next.js Backend • Python • FastAPI preferred Database • PostgreSQL Vector Database • Pinecone, Weaviate, or Chroma AI Integration • OpenAI API • Anthropic API • Retrieval-Augmented Generation (RAG) architecture preferred Security Requirements • User authentication • Role-based permissions • Encrypted document storage • Audit logging • Secure API access Deliverables Functional web application Source code repository Database schema API documentation Deployment documentation Administrator guide User guide Ownership and Intellectual Property All work product, source code, documentation, specifications, workflows, business logic, prompts, training materials, and derivative works developed under this project shall be deemed works made for hire and shall be the sole and exclusive property of the Client. Contractor shall assign all intellectual property rights to the Client upon creation. Contractor shall not reuse, disclose, distribute, or commercialize any portion of the work product without the Client’s prior written consent.

We're building an internal AI system that runs entirely on our own hardware (no cloud inference) against our own company data. We have a working proof-of-concept and want to get the architecture right. We need an experienced consultant to review what we've built, pressure-test our decisions, and tell us where we're wrong. This is an advisory/validation role first — we have someone doing the hands-on work; what we want is a senior second opinion to make sure we're building this the right way. What we're running today: Inference: RTX 5090 (32GB, Blackwell), Ubuntu 24.04, running llama-server (llama.cpp + CUDA) serving Gemma 4 31B-it (Q4_K_M GGUF) at a 262,144 context window. Also hosts our MCP retrieval server, PostgreSQL, and Qdrant. Embeddings: separate machine with an RTX 3060 running vLLM serving Qwen3-Embedding-4B. RAG: hybrid retrieval — Postgres full-text search + Qdrant semantic search with RRF fusion, exposed through a custom MCP server with tool-calling. Data: ingesting our own internal operational data into Postgres + Qdrant. Planned stack: LiteLLM for model routing, n8n for automation, Open WebUI for the interface, Langfuse for observability, Vault or Infisical for secrets, Keycloak/Azure AD for SSO. What we need help with: Validating our two-machine split (inference vs. embeddings) and whether our VRAM/context budget holds up under real load — specifically whether a 256K context window is real and performant on a single 32GB card or just nominal. Model selection and routing strategy: which open-weight models for which tasks, and how to structure LiteLLM routes. RAG quality: chunking, embedding dimensionality, hybrid search tuning, reranking — making retrieval actually accurate on messy real-world data. Sanity-checking our overall architecture and telling us our blind spots. You should have done: Stood up local LLM inference in production — llama.cpp/llama-server and vLLM, not just Ollama on a laptop. You understand GGUF quantization (Q4_K_M, IQ-series), KV cache, KV-cache quantization, and how context length maps to actual VRAM consumption. Real fluency in GPU sizing math — given a model, a quant, and a context window, you can tell us whether it fits on a given card and what throughput to expect. Bonus if you've worked with Blackwell / sm_120a. Built production RAG — vector DBs (Qdrant, pgvector), hybrid search, RRF fusion, embedding model selection, reranking, evaluation. Worked with agentic/tool-calling systems and ideally MCP servers. Know the open-weight model landscape (Gemma, Qwen, Llama, Mistral, Phi, Nemotron, Hermes) and their licenses well enough to advise. Production ops: systemd, Docker, model gateways (LiteLLM or similar), observability (Langfuse), secrets management, SSO.

Type: Hourly, ongoing (part-time to full-time, room to grow) Stack you'll work in: Notion, Slack, HubSpot, Google Workspace/Gmail, Claude + other LLM APIs, Zapier/Make/n8n About us We're a fast-moving sports and fan-engagement startup. We're small, we ship quickly, and we want AI woven into how the whole company operates, not as a side experiment, but as the default way we work. You'd be the person who makes that real. What you'll do Map our current workflows across sales, marketing, ops, and content, then find the highest-leverage places to automate. Build automations and agent workflows that connect our tools (Notion, Slack, HubSpot, Gmail/Google Workspace) using platforms like Zapier, Make, or n8n plus LLM APIs. Design and ship AI agents for real jobs: lead routing and CRM enrichment, content drafting, customer/fan response triage, internal knowledge search, reporting digests. Stand up the connective tissue (prompts, integrations, guardrails, and monitoring) so automations are reliable, not brittle demos. Train and enable our team: build SOPs, run working sessions, and create lightweight docs so non-technical people actually adopt what you build. Help set our AI strategy and roadmap as we scale. You're a strong fit if you Have shipped real automations and AI agent workflows in production (not just prototypes). Are fluent with Zapier / Make / n8n and at least one major LLM API (Anthropic/Claude, OpenAI). Know your way around HubSpot, Notion, Slack, and Google Workspace integrations and APIs. Can write clean prompts and think in systems: edge cases, error handling, human-in-the-loop checkpoints. Can explain technical work to non-technical people and get them to adopt it. Communicate proactively and move fast without breaking trust on things that touch customers or revenue. Nice to have Experience taking a small company "AI-native" end to end. Background in sports and/or blockchain. Comfort with light scripting (Python/JS) when no-code hits its limits. How to apply In your proposal, please: Describe one AI agent or automation you built, the tools involved, and the measurable result. Tell us how you'd approach training a non-technical team to actually use what you build. This part matters as much as the build. Share your hourly rate and weekly availability. Proposals that skip these will be passed over. We're looking to start with a small paid task and grow the engagement from there.

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.

Project Overview We are building an AI-powered voice receptionist agent for a dental practice client. The agent will handle inbound calls 24/7 — booking, rescheduling, and canceling appointments, answering FAQs, and escalating complex situations to a human. The voice layer is built in Retell.ai and the orchestration/automation flows run in n8n, with integration into a dental practice management systems. This is a hands-on build role, not consulting. You will design, configure, and iterate the agent working directly with our founding team. What You'll Build: Conversational voice flows for new and returning patients (booking, rescheduling, cancellations, FAQs, insurance questions, urgent/emergency triage) Clean escalation paths to human staff when the agent can't handle the call n8n workflows connecting the voice agent to the practice management API for real-time schedule reads/writes HIPAA-conscious configurations and tool choices throughout the stack Performance tracking and iterative prompt/flow improvements based on real call feedback Seeking an Expert Who: You've built multiple production voice agents, ideally in healthcare or dental, and can own the full stack from architecture to delivery. You think critically about latency, barge-in behavior, edge cases, and HIPAA compliance — not just happy-path flows. Requirements: Deep expertise in Retell.ai — prompt tuning, latency optimization, interruption/barge-in handling, LLM selection, and cost modeling Advanced n8n automation — complex branching logic, dynamic data handling, external API integrations, and error recovery Experience with dental or healthcare organizations — you understand how practices operate, what front desks actually deal with, and how to translate that into agent logic HIPAA awareness — you know which tools and configurations are appropriate for PHI workflows Track record of full-cycle delivery: scoping → build → pilot → iteration Nice to have: Dentrix Ascend integration, NexHealth, prior AI receptionist or appointment-booking agent for a clinic/practice. 📋 To Apply, Please Answer Have you built a production voice agent with Retell.ai? If yes, briefly describe it. What's your approach to managing Retell.ai per-minute costs on an inbound call agent? Have you worked with dental or healthcare clients before? What was the workflow? Paste a relevant n8n workflow screenshot or describe your most complex flow.