Role Overview You are the Executive AI Enablement Lead at AIVC, the person whose job is to make the executives at AIVC’s client businesses true power users of Claude, Cowork, and code- and agent-driven workflows. AIVC partners with operator businesses to drive AI-led EBITDA growth, and part of that work is bringing each company’s most senior leaders up the AI curve. You’re the person who personally designs and runs that path on every engagement: assessing where a given client executive is today; curating the right materials, videos, and course content; running 1:1 coaching; building executive playbooks; and acting as their daily operator-in-the-loop until the new workflows stick. The first concrete instance is already lined up, a named client managing partner has explicitly asked for the fastest path to becoming a power user of Claude, Cowork, and Claude Code / Skills. From there you scale: same treatment to additional client executives across the portfolio, then a documented set of executive-grade playbooks and patterns that compound across every future engagement. You are bias-toward-results – a win is the client executive’s calendar-week looking different, not a beautifully written rubric nobody uses. What You’ll Own (Outcomes) • Within 30 days of pairing with the first client managing partner, they have a working daily routine in Claude, Cowork, and Code/Skills that’s already replacing or improving how they handle at least three recurring tasks • Within the first quarter of the engagement, the client executive is a true power user — running multi-step workflows, custom Skills/Projects, and agent-assisted tasks without needing coaching scaffolding for the basics • A documented set of executive playbooks (research, writing, analysis, synthesis, workflow automation, agent-assisted tasks) that compound across every client engagement, not one-offs • A curated, current library of learning materials, videos, example workflows, and Claude-native patterns — including a clear point of view on which external courses, tutors, or expert resources are worth plugging in • Observable change in how client executive cohorts use AI: from reactive chat to repeatable, structured, outcome-oriented workflows • A foundation of training assets and patterns that scales beyond executive coaching into broader client teams in year two • A reputation among AIVC’s clients as the trusted go-to for “how do I do this better in Claude” — measured by inbound demand and engagement expansion What You’ll Do (Responsibilities) • In the first weeks: build the first client managing partner’s tailored upskilling plan — assess current usage, identify the highest-leverage workflows for their day-to-day, curate the right mix of materials / videos / course content, and recommend any tutor or expert-guided support to fold in • Provide 1:1 coaching for client executives — managing partners, founders, C-suite leaders — on Claude, Cowork, and code- and agent-based workflows • Design tailored training plans per executive that go beyond basic onboarding into advanced usage, with explicit progression from chat → workflows → agents • Curate the best external materials (videos, courses, blog posts, example projects) and rewrap them into client-ready, AIVC-flavored learning paths • Teach practical, high-leverage use cases live: research, writing, analysis, synthesis, workflow automation, and agent-assisted tasks • Help client executives move from general chat usage into repeatable workflows — Claude Projects, Skills, scheduled Cowork tasks, MCP integrations, custom agents • Serve as a real-time tutor and expert resource for client executives — over Slack, in meetings, on-site, and in async written feedback • Run office hours, workshops, and informal Q&A sessions inside client teams to keep adoption sticky between coaching sessions What We’re Looking For (Required) • Deep hands-on expertise with Claude across every surface (Claude.ai, Claude Projects, Claude Code, Claude Skills, Claude API) — and an active habit of pushing the edges of each • Strong working fluency with Claude Cowork specifically, including scheduled tasks, connected apps / MCPs, and the broader workflow surface • Strong capability with code-enabled AI workflows: you can write Python and/or TypeScript, build agents, configure MCP integrations, and ship a working internal automation end-to-end without needing an engineer • Demonstrated ability to teach non-technical but highly demanding users — you’ve made executives, founders, or senior operators meaningfully better at something complicated, not just trained engineers • Strong workflow design instinct — you can translate messy business questions into clean prompts, workflows, and systems • Polished, discreet, and effective in high-touch client executive settings — high EQ, low ego, comfortable representing AIVC inside senior client environments and around senior decision-makers • Strong bias toward practical results over theoretical AI knowledge — the metric is the client executive’s behavior change, not the elegance of the explanation • Excellent written and verbal communication; you can write a playbook a client executive will actually read and use • Comfort with significant travel to client sites and embedded, on-site engagement work • 5+ years of professional experience across some mix of: applied AI / ML, technical training and enablement, developer relations, solutions engineering, executive coaching, management consulting, or chief of staff / senior operator roles to executives Helpful If You Have (Preferred) • Prior experience coaching or supporting C-level executives, founders, or managing partners as a client-facing professional — executive coach, principal solutions engineer to executive customers, chief of staff to a CXO, or partner-level consultant • Background that combines technical depth with people skills — developer relations, solutions engineering, technical training, or learning & development at a frontier AI or developer-tools company • Direct experience building executive-facing training programs or curricula that demonstrably moved adoption inside other organizations • Hands-on familiarity with the Anthropic product surface specifically: Claude Projects, Claude Skills, Claude Code, MCP server development, Claude API • Track record of getting non-technical users to genuinely adopt a technical tool — i.e., users who chose to keep using it after the training ended • Background in management consulting, professional services, executive coaching, or learning & development — especially in environments where the customer was a senior external client • An active personal portfolio of AI work (workflows, automations, blog posts, talks, open-source contributions) you can point to • Comfort building light tooling (a Notion playbook system, a Claude Skills catalog, a small dashboard) without needing engineering support • Familiarity with AIVC’s model — operator business engagements, EBITDA-led measurement, and the broader compounding intelligence layer — or eagerness to come up the curve quickly

We're hiring a senior AI developer to build and deploy AI solutions for a fintech/credit-union platform. The work spans autonomous banking agents, fraud detection, credit scoring, and bill-pay/invoice automation — at the intersection of LLMs, cloud infrastructure, and financial-domain expertise, with security and compliance built in from the start. This is a long-term, ongoing engagement. What you'll do: AI agents & orchestration - Design, build, and deploy multi-agent systems using Amazon Bedrock Agents, LangChain, and related frameworks - Architect agentic workflows for core banking use cases: credit scoring, fraud detection, bill-pay automation, invoice management - Define agent personas, memory strategies, tool-use patterns, and escalation paths for production banking agents LLM engineering - Fine-tune, prompt-engineer, and evaluate LLMs for financial-domain tasks - Build RAG pipelines over credit-union knowledge bases, policy docs, and member data - Implement guardrails, content filtering, and compliance checks for safe, regulated outputs - Monitor performance, hallucination rates, and latency against SLAs Cloud infrastructure (AWS & Azure) - Architect and manage AI/ML workloads on AWS (Bedrock, SageMaker, Lambda, S3, IAM, VPC) and Azure (OpenAI Service, Azure ML, AKS) - Design secure, cost-optimized environments compliant with NCUA, PCI-DSS, and SOC 2 - Implement infrastructure-as-code with Terraform or AWS CDK DevOps & MLOps - Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, CodePipeline, Azure DevOps) - Containerize services with Docker, orchestrate with Kubernetes (EKS/AKS) - Apply MLOps best practices: model versioning, A/B testing, canary deployments, automated rollback - Stand up observability with logging, tracing, and alerting Python development - Write clean, well-tested Python for AI pipelines, REST APIs, and data workflows - Build FastAPI/Flask microservices exposing agent capabilities to frontend and core banking systems - Integrate with financial data sources, core banking APIs, and third-party fintech services Banking applications - Build credit-scoring models using alternative data and explainable AI (XAI) - Develop real-time fraud detection with behavioral analytics, anomaly detection, and auto-decisioning - Create conversational agents for bill pay, account management, and member self-service - Automate invoice workflows: extraction, classification, approval routing, reconciliation - Partner with compliance/risk to keep AI decisions auditable, fair, and regulatory-compliant What you should have: - 5+ years software engineering; 3+ years in AI/ML or LLM engineering - 2+ years building AI for banking, credit unions, or financial services - Hands-on experience with Amazon Bedrock, LangChain, Python, AWS, and infrastructure-as-code - Working knowledge of NCUA, PCI-DSS, SOC 2, GLBA, and Fair Lending requirements - Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or related field Nice to have: - AWS or Azure AI/ML certifications - Open-source LLM experience (Llama, Mistral, Phi) and self-hosted inference (vLLM, Ollama) - Vector databases (Pinecone, OpenSearch, pgvector) - Graph-based fraud networks and graph ML - AI governance / responsible AI framework experience - Prior work at a credit union, community bank, or fintech lending platform To apply, please share: - Your resume highlighting AI and banking project experience - A brief note on your most impactful AI agent or LLM project in a financial-services context - Links to GitHub, portfolio, or published papers (optional but encouraged)

About us We're a consulting firm building agentic AI solutions for clients in law, healthcare, education, retail, commerce, and regulatory consulting. Our work sits at the intersection of LLM orchestration, workflow automation, and compliance-sensitive deployment. We're not building toy demos. We ship production systems for organisations where reliability, auditability, and regulatory posture actually matter. The role We're hiring a lead full-stack developer to take technical ownership of our agentic automation builds and grow a small delivery team. You'll be the person clients trust to architect the solution, and the person our team look to for direction. You'll be doing a mix of: Designing end-to-end agentic workflows (planning, tool use, memory, retrieval, human-in-the-loop checkpoints) Building and shipping client solutions across the stack — backend orchestration, integrations, frontends, infrastructure Leading and reviewing the work of other developers as we scale Working directly with clients to translate ambiguous business problems into reliable automation Setting our technical standards: evals, observability, prompt and tool governance, deployment patterns Required experience 5+ years full-stack development, with strong backend fundamentals (Python and/or TypeScript) Demonstrable production experience with agentic systems — not just chatbots. We want to see agents that plan, use tools, recover from errors, and operate over real workflows Hands-on with at least one major agent framework or orchestration approach (LangGraph, CrewAI, custom orchestration, Claude/OpenAI tool use, MCP, etc.) Experience integrating with enterprise systems via APIs and webhooks (CRM, email, document stores, internal databases) Cloud deployment experience (AWS preferred; Azure or GCP acceptable) Frontend competence in React or similar — you don't need to be a designer, but you should be able to ship a clean client-facing UI Strong written English and clear communication. You'll be talking to non-technical stakeholders regularly Nice to have Experience working in or with regulated industries (healthcare, legal, financial services, education) Familiarity with RAG pipelines, vector stores, and retrieval evaluation Exposure to compliance frameworks (SOC 2, ISO 27001, GDPR, sector-specific regs) Prior tech lead or solo founder experience Comfort with infrastructure-as-code (Terraform, CDK) Comfortable working with and reviewing the output from agentic coding systems to speed up development (Codex, Claude Code etc.) What we're offering Long-term engagement, starting with a paid trial project Competitive hourly rate (share your expectation in your application) Real ownership and the opportunity to shape how we deliver Interesting, varied work across sectors — no two builds the same How to apply In your proposal, please include: A short summary of an agentic system you've shipped to production. What did it do, what was the architecture, and what broke that you had to fix? Your stack of choice for a new agentic build today, and why Your hourly rate and approximate weekly availability A link to code or a portfolio if you have one

SenseLab is a memory and coordination layer for AI agents — a versioned, provider-agnostic layer that lets agents remember, share context, and coordinate across sessions. About this initiative: We're launching a Creator Initiative: we're commissioning developers with a following base on X and/or Linkedin to build real projects on SenseLab and share what they made. The goal is to showcase the range of use cases developers can build on top of an agent memory layer — multi-agent coordination, shared memory spaces, persistent context, agent-to-agent handoffs, and more. We want to see (and help others see) what's possible. What you'll do: - Build a small but real project using the SenseLab API — pick a use case that interests you (e.g. a multi-agent app using shared memory, an integration with an existing agent framework, a persistent-memory assistant, a coordination workflow). - Publish the code to a public GitHub repo. - Write a clear technical walkthrough (blog post or LinkedIn) explaining what you built and how you used SenseLab What we're looking for: - MUST HAVE: Developers with a following base on X and/or LinkedIn that are part of a community and already post technical content - Hands-on experience building with LLM agents (LangGraph, CrewAI, MCP, or similar). - Ability to write clearly for a developer audience — the write-up matters as much as the code. - Curiosity about agent memory/coordination and ideas about what you'd build. To apply: Tell us (1) your agent-building experience with a link to something you've made, (2) one use case you'd be excited to build on SenseLab, (3) your X and/or LinkedIn connection reach

I’m looking for an experienced developer to build a complete production-ready Voice AI lead acquisition system for a life insurance agency. This is not a simple chatbot — it needs to handle the full pipeline from lead generation through qualification, appointment booking, nurture, and routing. Core System Requirements: • Ingest leads from paid ads and forms • Outbound Voice AI qualification calls that screen for: • Age • Household income • Current insurance coverage • Health conditions • Number of beneficiaries • Purchase intent and urgency • Natural conversation flow with strong objection handling • Automatically book confirmed appointments into agent calendars • Human-feel confirmation and reminder sequences to maximize show rates • Real-time lead routing to the correct agent based on state license, capacity, and availability (under 60 seconds) • Multi-touch nurture sequences lasting 90+ days across SMS, email, and Voice AI callbacks for leads that don’t book or no-show • Full CRM integration (AgencyZoom, HubSpot, Salesforce, or similar) so qualified leads and appointments appear automatically in the agent’s CRM • Admin dashboard showing real unit economics: • Cost per lead (CPL) • Cost per appointment (CPA) • Show rates • Close rates • Contribution margin by source, campaign, and agent • Scaling decision engine that can automatically increase or decrease ad spend based on agent capacity and performance Lead Generation Responsibilities: • Set up and manage Facebook and/or Google ad campaigns • Build and optimize landing pages + lead forms • Track and report cost per lead and cost per appointment • Scale spend intelligently based on the system’s performance data Technical Requirements: • Strong experience with Retell AI or Vapi • Backend development (Node.js, Python, or similar) • CRM API integrations • Experience with calendar systems and webhooks • Knowledge of TCPA compliance, call recording disclosures, and consent best practices Deliverables: • Fully working Voice AI qualification agent • Backend system with routing and nurture logic • Admin dashboard with real performance metrics • CRM + calendar integrations • Lead generation setup and management • Clear documentation and deployment instructions

Title: AI Voice Agent Developer — Twilio + OpenAI Realtime API + Authorize.net (Service/Delivery Business) Category: AI Voice Agents / Twilio Development / Backend Integration Job Description: We run a service business (heating oil delivery, service calls, and free estimates) that currently handles all inbound calls manually — taking orders, scheduling, and processing payments by hand, logging everything into Google Docs. We want to build a self-hosted AI voice agent to automate this, and we're looking for a developer to build it with us. What we have already: 90+ phone numbers on Twilio, plus 2 landlines on Optimum Online, currently all forwarded to Grasshopper Authorize.net account already integrated on our website (API Login ID / Transaction Key available) Two separate websites/platforms for heating oil delivery — one for same-day delivery (higher price), one for scheduled delivery (lower price) — each with its own pricing Google Docs currently used for logging orders, estimates, and service calls What we want built: What we want built: Route Twilio numbers away from Grasshopper to a custom voice agent server (Twilio Media Streams + a realtime AI model — we're leaning toward OpenAI's Realtime API) Call flow that can identify caller intent: heating oil order, free estimate, or service call For estimates and service calls: schedule appointments by checking calendar availability — no payment involved on these calls For heating oil orders only: determine same-day vs. scheduled delivery, pull the correct price from the matching platform/pricing table, quote the total, and confirm quantity before payment Self-service "block myself out" capability — a simple way for us (owner/dispatcher) to mark ourselves unavailable when overbooked, so the AI automatically stops offering same-day/next-day slots (for both estimates/service calls and oil delivery) and either pushes callers to the next open slot or takes a callback request instead. Needs to be something we can toggle quickly (a shared calendar block, a simple dashboard, or even a phone/text command) — not something requiring a developer each time. Phone payment capture — for heating oil orders only — via DTMF keypad entry for card number/expiry/CVV (never passed through the AI model), submitted to Authorize.net for charge, using the price from whichever of the two platforms (same-day or scheduled) matches the order Automatic logging of every completed call (order/estimate/service booking) to Google Sheets (preferred over Docs for structure), including a fallback "transfer to human" option Handle 2 Optimum Online numbers via call forwarding into the Twilio system Important: We specifically do NOT want a per-minute AI platform (Retell, Vapi, Bland, etc.) — we want to own the code and infrastructure directly, paying only Twilio and the AI model provider for usage. We have a written architecture/build guide already (happy to share on request) covering the full phase-by-phase plan — you'd be implementing against it, not starting from a blank page. Ideal experience: Twilio Programmable Voice + Media Streams (not just SMS) Direct integration with a realtime voice AI model (OpenAI Realtime API or similar) — not just wrapping a no-code platform Authorize.net API integration, ideally with DTMF/PCI-aware payment flows Google Sheets/Docs and Google Calendar API for logging and availability control Comfortable handing off clean, documented code we can maintain going forward Engagement structure: We'd like to start with a fixed-price proof-of-concept phase: one Twilio number, basic oil order flow (intent → correct price from same-day or scheduled platform → log to Sheet, no payment yet). If that goes well, we'll extend the same contract to cover payment integration, scheduling with overbooking controls, and full rollout across our numbers. To apply, please include: Relevant past projects (especially Twilio voice + AI, and any payment-over-phone or calendar-aware booking work) Your proposed approach/timeline for the proof-of-concept phase Your rate (hourly or fixed-price estimate for POC phase)

I’m looking for an AI Engineer to help build an AI Safety Evaluation & Governance product powered by open-source models. This is a 1-month, hands-on project with an expected commitment of around 20 hours per week. The goal is to build an MVP that can automatically test AI models, identify safety failures, analyze failure patterns, and support continuous improvement. 🔍 What you’ll work on • Build an automated red-teaming engine that generates test cases across risk domains, severity levels, and attack strategies • Run tests against models such as Gemma, Llama, Qwen, and API-based models • Develop evaluators for jailbreak success, policy violations, over-refusal, under-refusal, and severity • Structure safety policies into consistent taxonomies and evaluation criteria • Turn confirmed failures into reusable eval datasets and regression tests • Build lightweight reporting for model comparison, human review, and policy-version tracking 🧠 What I’m looking for • Experience with open-source LLMs, inference pipelines, prompt optimization, fine-tuning, LoRA/QLoRA, and LLM evaluation • Ability to independently build an end-to-end MVP, including data pipelines, model orchestration, scoring, and reporting • Familiarity with AI safety, red teaming, jailbreaks, content moderation, or Trust & Safety systems • Bonus: experience with model-based evaluators, human-in-the-loop review, agentic testing, or multimodal safety ⏳ Project setup Duration: 1 month Time commitment: Around 20 hours per week Format: Flexible and remote-friendly Stage: Early-stage, 0-to-1 MVP This is not about manually writing red-team prompts one by one. The goal is to build a scalable system that can continuously generate tests, evaluate model behavior, identify safety gaps, and verify whether issues have been resolved. If this sounds like you, please DM me with a brief introduction and examples of relevant work.

AI Voice Assistant Developer Needed – Fix & Improve Existing CRM-Integrated AI System (Law Firm) Project Overview My law firm has a custom-built, voice-controlled AI assistant that integrates with our CRM — think "Jarvis from Iron Man" for legal case management, document drafting, and web research. I can speak natural-language requests to it, and it interprets the command and executes actions directly in our CRM (pulling records, updating case data, etc.). The system was built in-house but is currently unreliable — it misinterprets commands, occasionally executes the wrong action, and generally needs a more experienced hand to stabilize it. I'm looking for an experienced AI engineer to diagnose and fix the existing issues, and then continue as an ongoing freelance developer to further build out the platform. This is NOT a general "full-stack developer" job — I need someone with specific experience building reliable, production-grade LLM agent systems that take real actions via API calls. What I Need Help With Phase 1 – Diagnostic & Fix Review the existing codebase and architecture Identify why the system produces inconsistent/incorrect results (prompt design, lack of guardrails, poor error handling, API integration bugs, etc.) Fix reliability issues so commands are interpreted and executed correctly and consistently Phase 2 – Ongoing Development Improve error handling, logging, and testing so issues can be caught before they affect real client data Extend functionality as our needs grow Ongoing maintenance and ad hoc support Ideal Skills & Experience Proven experience building LLM-powered agentic systems with function calling / tool use (not just chatbots — systems that take real actions via APIs) Strong API integration experience, ideally with CRM platforms Experience with speech-to-text / text-to-speech pipelines (e.g., Whisper, ElevenLabs) if voice input/output is involved Solid understanding of prompt engineering and LLM reliability practices — building in guardrails, validation, and fallback behavior, not just prompt tweaking Comfortable working with sensitive/confidential data (this is a law firm — confidentiality is critical) Strong communicator who can explain technical tradeoffs in plain language.

I'm looking for an experienced AI automation engineer to build an MVP for an internal AI-powered M&A Deal Intelligence platform. Project Goal I want to build a system that helps my M&A advisory firm identify acquisition opportunities, research businesses and owners, and prepare me for outreach—all while integrating with our existing Zoho CRM. MVP Requirements - Find businesses from selected public sources using lawful methods. - Gather company information (website, industry, location, products/services, leadership, estimated size, etc.). - Research owners and decision-makers using publicly available sources (company websites, LinkedIn, news, press releases, interviews, social media, public business records where appropriate, etc.). - Generate an AI briefing before I contact a prospect that includes: - Company summary - Owner background - Personalized conversation starters - Suggested questions - Potential acquisition opportunities - Risks or notable findings - Automatically create or update records in Zoho CRM with the research and AI summaries. - Be designed so additional data sources, automations, and AI capabilities can be added over time. Preferred Skills - AI agents - Python - OpenAI API (or similar LLMs) - Web scraping - Playwright - Apify - n8n or similar automation platforms - API integrations - Zoho CRM integrations - Experience building AI research or deal sourcing tools is a major plus. When Applying Please include: - Examples of similar AI automation or research systems you've built. - The technology stack you'd recommend. - How you'd approach this project. I'm looking for a long-term technical partner to continue expanding this into a comprehensive AI platform for our M&A advisory business if the initial project is successful.

# AI Business Tools Trainer and Consultant for Healthcare/DME Company ## Project Overview Hiro Health is a growing healthcare company specializing in sleep diagnostics, CPAP therapy, PAP resupply, oxygen services, ventilator services, and durable medical equipment. We are seeking an experienced AI business trainer and consultant to provide personalized, hands-on instruction to the company owner and selected managers. The goal is to teach us how to effectively use AI platforms such as: * ChatGPT * Claude * Google Gemini * Microsoft Copilot * Other relevant AI and automation tools This is not a general introductory AI course. Training must be customized around Hiro Health’s actual business operations, financial reporting, healthcare research, management responsibilities, and growth plans. ## Primary Objective At the completion of the project, we should be able to independently use AI to: * Analyze business and financial information * Improve operational processes * create reports, policies, and procedures * Conduct reliable business and healthcare research * Develop employee and management materials * Evaluate acquisitions and growth opportunities * Identify tasks that can be automated * Select the best AI platform for each type of project * Protect confidential patient, employee, and company information The consultant should teach us how to use AI—not simply perform the work on our behalf. ## Areas of Training ### 1. AI Platform Selection Teach us the differences between ChatGPT, Claude, Gemini, and Microsoft Copilot, including: * Strengths and weaknesses of each platform * Best business uses for each tool * Free, paid, business, and enterprise account differences * File-upload and data-analysis capabilities * Research and source-verification capabilities * Integration with Microsoft 365, Google Workspace, and other systems * Privacy and data-retention considerations ### 2. Prompt Development Teach us how to: * Write clear and effective prompts * Provide the appropriate background, data, and instructions * Improve an AI-generated answer through follow-up prompts * Create reusable prompt templates * Build multi-step prompts for complex projects * Reduce inaccurate or fabricated responses * Verify important facts and calculations * Convert successful prompts into standardized company workflows ### 3. Financial Analysis Use realistic Hiro Health examples to teach AI-assisted: * Profit-and-loss statement analysis * Balance-sheet and cash-flow analysis * Budget development * Financial forecasting * Gross-margin analysis by service line * Labor and productivity analysis * KPI development and reporting * 13-week cash-flow forecasting * Acquisition valuation and financial modeling * SBA loan and debt-service analysis * Excel formula, table, dashboard, and chart creation * Comparison of actual results against budgets and benchmarks ### 4. Operations and Process Improvement Teach us how to use AI to: * Create and improve standard operating procedures * Document departmental workflows * Identify process bottlenecks * Develop quality-control checklists * Build employee training materials * Analyze operational reports * Create management dashboards * Develop implementation and project plans * Improve CPAP setup, resupply, sleep-testing, oxygen, and ventilator workflows * Identify repetitive tasks that may be automated ### 5. Sales and Referral Management Training should include: * Physician-referral data analysis * Referral-source ranking and segmentation * Sales-territory analysis * Identification of declining or underperforming accounts * Sales representative performance analysis * Sales plans and account strategies * Physician outreach emails and materials * Competitor and market research * Commission and incentive-plan modeling * Customer-review and satisfaction analysis ### 6. Human Resources and Management Teach us how to use AI to develop: * Job descriptions * Interview questions * Candidate scorecards * Performance evaluations * Employee policies * Corrective-action plans * Training documents * Internal company communications * Meeting agendas and summaries * Organizational and staffing plans * Compensation comparisons * Employee productivity measurements ### 7. Healthcare, DME, and Reimbursement Research Teach us how to conduct and verify research involving: * Medicare and commercial insurance requirements * DME billing and reimbursement * HCPCS and CPT codes * CPAP, oxygen, ventilator, and sleep-testing requirements * Industry benchmarks * Market and competitor information * Accreditation and licensing requirements * New product and service opportunities * Authoritative government and payer sources The trainer should emphasize that AI research does not replace legal, clinical, billing, compliance, or accounting review. ### 8. AI Automation Opportunities Teach us how to identify and evaluate opportunities involving: * Microsoft Copilot * Microsoft Power Automate * AI agents * Robotic process automation * Scheduled reports * Automated document summaries * Email and meeting follow-up * Data extraction and classification * Management notifications * Brightree, QuickBooks, Microsoft Fabric, Shopify, and Microsoft 365 workflows The consultant should explain when generative AI is appropriate and when traditional automation or systems integration is a better solution. ## Healthcare Privacy and Security Because Hiro Health operates in healthcare, the training must address: * HIPAA and protected health information * Personally identifiable information * De-identifying information before using AI * Consumer versus enterprise AI accounts * Business Associate Agreement requirements * Data-retention and model-training settings * User access and permission controls * Approved and prohibited AI uses * Review requirements before AI-generated work is used * Situations in which information should never be entered into an AI platform Actual patient information should not be used during training. Examples should rely on synthetic, anonymized, or properly de-identified information. ## Preferred Project Structure We anticipate a six-to-eight-week engagement consisting of: * One or two virtual training sessions per week * Approximately 60–90 minutes per session * Hands-on exercises using Hiro Health business scenarios * Assignments between sessions * Recordings of sessions when permitted * Reusable templates and reference materials * Limited follow-up support between sessions * A final AI implementation plan We are open to an alternative structure if the consultant recommends a more effective approach. ## Required Deliverables The consultant should provide: 1. An initial assessment of our AI knowledge and business priorities. 2. A customized training curriculum. 3. Live, hands-on training sessions. 4. A comparison guide for ChatGPT, Claude, Gemini, and Microsoft Copilot. 5. A reusable prompt library organized by business function. 6. At least five completed AI-assisted workflows based on Hiro Health’s needs. 7. Step-by-step instructions for each workflow. 8. An AI privacy, security, and acceptable-use checklist. 9. A process for verifying AI-generated work. 10. Recommendations for appropriate business or enterprise AI subscriptions. 11. A prioritized list of future automation opportunities. 12. A 90-day AI implementation roadmap. ## Ideal Candidate The ideal consultant will have: * Advanced experience with ChatGPT, Claude, Gemini, and Microsoft Copilot * Experience teaching business owners and executives * Strong prompt-engineering and workflow-design skills * Experience with Excel and Microsoft 365 * Business financial-analysis experience * Knowledge of AI privacy, security, and governance * The ability to explain technical topics in straightforward business language * Experience converting AI use cases into repeatable company processes Healthcare, DME, HIPAA, Brightree, QuickBooks, Microsoft Fabric, or Power Automate experience is strongly preferred. ## Proposal Requirements Please include: * A summary of your relevant experience * Examples of similar AI training engagements * Your experience with healthcare or regulated businesses * Your proposed training structure * Estimated project timeline * Hourly rate or fixed project price * Examples of training materials or workflows you have created * Relevant AI, Microsoft, healthcare, or technology certifications * Your availability for live virtual sessions ## Applicant Screening Questions 1. How would you compare ChatGPT, Claude, Gemini, and Microsoft Copilot for a healthcare business? 2. What precautions should a HIPAA-regulated company take when using AI? 3. Describe a business workflow you have improved using generative AI. 4. How do you teach users to recognize incorrect or fabricated AI answers? 5. How would you structure a customized AI training program for Hiro Health? 6. What experience do you have using AI with Excel, financial statements, and operational data? 7. How would you help us build a reusable company prompt library? 8. What healthcare, DME, Microsoft 365, or automation experience do you have? ## Definition of Project Success At the conclusion of the project, Hiro Health’s owner and participating managers should be able to: * Select the appropriate AI tool for a specific business need * Write effective prompts without outside assistance * Analyze common company reports and spreadsheets * Create professional business documents * Conduct and verify business research * Protect confidential and regulated information * Develop reusable prompts and AI workflows * Identify strong candidates for automation * Train additional employees on approved AI practices * Continue developing AI-assisted workflows independently