We’re a small business that is new to AI and automation, and we’re looking for an experienced AI coach, advisor, or automation consultant to help us understand where AI could realistically help our operations. We do not need someone to sell us a complicated system right away. We need someone who can first act as a guide: learn how our business currently works, audit our workflows, explain what opportunities they see, and teach us step by step what we should do next. The ideal person can help us with things like: Reviewing our current business processes and identifying where AI or automation could save time Explaining AI tools and automation options in plain English Helping us understand what is realistic, what is overhyped, and what is worth prioritizing Creating a simple roadmap for how we should adopt AI in the business Teaching us how to use tools step by step, rather than just handing over technical deliverables Advising us on whether we need chatbots, internal assistants, workflow automations, reporting tools, document automation, CRM automation, or other AI systems Helping us avoid wasting money on the wrong tools or overly complex solutions We are especially interested in someone who has worked with non-technical business owners or teams before. We want someone patient, practical, and clear, who can explain things without jargon.

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.

**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.

About us Nucci HQ is an established coaching and consulting company. We run one-on-one and group coaching programs, a consulting arm, and a real estate division, with an active student community and a fast-growing content presence on YouTube and social. We move quickly, and we are investing heavily in AI to make every part of the business faster — and we are looking for the person who will build and own that. The role We are hiring a contract AI Agent & Automation Engineer to design, build, fix, and maintain the AI agents and automations that run across our business — sales, operations, finance, coaching, and content. You will work directly with our founder, Anthony, ship quickly, and see the impact of your work right away. There is a clear roadmap of work ready from day one: stabilizing automations we have already built, standing up an internal command-center dashboard, and shipping a library of single-purpose agents. This is a contract role billed hourly — a flexible, low-friction way for both of us to start, with room to grow as the work does. What you’ll do • Fix and stabilize existing automations so they run reliably. • Build an internal CEO dashboard that pulls sales, consulting, finance, real estate, and social metrics into a single source of truth. • Build workflow automations — meeting note-taking from call transcripts, a speed-to-lead responder, coaching-call tracking, and weekly accountability recaps. • Build a library of single-purpose AI agents across sales, operations, finance, and content — each with one clear job. • Connect agents to the tools we already run on — Slack, Google Workspace, Whop, Heartbeat, ManyChat, Granola, and Fathom. • Prototype new ideas quickly, then harden the ones that work into reliable, monitored systems. • Document what you build so the team can use it and trust it. What we’re looking for • Two or more years building software, with hands-on experience building LLM-powered apps or agents. • Strong Python and/or JavaScript / TypeScript. • Practical experience with LLM APIs (Anthropic, OpenAI, or similar) — prompt design, tool / function calling, and retrieval (RAG). • Strong, hands-on experience with n8n, webhooks, and APIs — building, connecting, and debugging integrations. This is essential. • Comfortable reviving or debugging automations that someone else started. • Good judgment about when an agent is reliable enough to ship — and how to test for it. • A clear communicator who is self-directed and comfortable owning projects end to end. Nice to have • Experience with agent frameworks and orchestration (LangChain, LlamaIndex, MCP, CrewAI, or similar). • Familiarity with our stack — Slack, Whop, Heartbeat, ManyChat, Granola, or Fathom. • Front-end skills for building dashboards and internal tools. • Experience deploying and monitoring production AI systems. • A portfolio, GitHub, or side projects we can look at. Details Location: Fully remote. Type: Contract, billed hourly. Rate: $70–$110 per hour, depending on experience. Hours: Flexible — we will agree on a weekly range based on your availability. Start date: As soon as possible. How to apply Send a short note about a recent AI agent or automation you built and what it does, your resume or LinkedIn, and links to any code, demos, or projects. No long cover letter needed — we care most about what you have built.

Engagement Overview I am the CEO and principal attorney of a small law practice specializing in campaign finance, lobbying regulation, FARA, nonprofit law, and government ethics. My five-person team — a junior partner, two associates, and an executive assistant — recently integrated into a larger firm. I am looking for an experienced Claude/AI automation builder for a phased engagement to design, build, and deploy a suite of interconnected agents and automations. This brief covers three phases. Phase I (Inbox Triage) is the highest immediate priority and the natural starting point. Phases II and III follow sequentially. Strong candidates will be evaluated on Phase I but should demonstrate familiarity with the full roadmap. This is a paid engagement. Scope, timeline, and rate are open to discussion. Technology Stack Email: Gmail (personal Pro account — not firm infrastructure) AI: Claude (Anthropic) via MCP or API Task and project management: Notion (existing workspace; routing tables, matter tracking, and timesheet structure already in place) Calendar: Google Calendar Internal chat: Google Chat Document storage: Google Drive (primary); local hard drives on iMac and MacBook Pro (secondary) Matter management / DMS: iManage (larger firm system — integration via dedicated ingestion email address) Voice notes: Plaud (AI note-taker) Signing platform: TBD — candidates should ask during scoping Out of scope: Signal and iMessage — encrypted platforms with no API access; manual forwarding convention only Confidentiality Requirements This is a law practice. Attorney-client privilege and work product protection apply to all client communications and matter-related documents. These are not compliance checkboxes — they are professional obligations with real consequences. The successful candidate must: • Execute a non-disclosure agreement prior to engagement • Demonstrate genuine understanding of why data handling matters in a legal context — not just technically, but professionally • Never use client names, email content, routing data, or document content for training, testing, or demonstration purposes • Work exclusively within the client's authenticated accounts — no third-party data stores outside the approved stack • Design systems that minimize data exposure — process and route, do not store unnecessarily Generic proposals that do not address confidentiality specifically will not be considered.   Phase I — Inbox Triage Agent Real-time classification and routing of inbound Gmail, with a daily digest to the executive assistant. Objective The principal attorney's Gmail inbox receives high volumes of email across clients, matters, and categories of widely varying priority. The goal is an agent that processes every inbound message, classifies it, routes it to the correct person automatically, and ensures nothing drops — without overloading the executive assistant with triage work she should not be doing. Two-Stage Routing Logic Stage 1 — Sender Classification Every inbound email is classified against a tiered contact list maintained in a Notion database: MVC: Most Valuable Clients — 5 to 10 contacts. Highest priority. HVP: High Value People — 10 to 20 contacts. Some overlap with MVCs. Principal attorney, unless task-type rule applies All other clients: Roster managed in Notion with assigned attorney(s). Assigned attorney(s) per Notion client record Catch-All: Anyone not in the contact table — prospects, opposing counsel, vendors, bar association, etc. Generate executive assistant daily digest Stage 2 — Task-Type Classification (MVCs only) For MVC contacts, a second classification layer routes based on the nature of the request. Rules are client-specific. Examples: • Scheduling requests → Executive assistant • Contracts and approvals → Designated associate(s) per client record • Strategic and substantive legal matters → Principal attorney Task-type rules are defined per MVC client and must be configurable without developer involvement. Routing Table — Notion All contact and routing data lives in an existing Notion database. The agent reads from it at runtime. Required fields: • Contact name and/or email domain • Tier (MVC / HVP / Standard / Catch-All) • Assigned attorney(s) for Standard clients • Task-type override rules for MVCs The executive assistant must be able to add, edit, and re-tier contacts without touching code. This is a hard requirement. Routing Output Candidates should propose their recommended approach from among the following, based on current Gmail MCP capabilities: • Apply Gmail label and/or forward to assigned attorney's address • Create a pre-addressed draft for principal attorney review before sending • Log routing decision to Notion with email link and recommended assignee Please address this question directly in your proposal — it is a key evaluation criterion. Daily Executive Assistant Digest Once per day at a configurable time, the agent generates a digest delivered to a designated Notion page covering all catch-all emails from the prior 24 hours. Each entry includes: sender, subject, timestamp, and a one-line AI summary of the email's apparent purpose.   Phase II — 5 AM Daily Brief A structured morning brief delivered to Notion each day before 5 AM, aggregating schedule, tasks, workflow status, news, and forward-looking context. Objective The principal attorney starts each day across multiple locations and needs a single, consolidated view of what matters — professional and personal — without opening email. The brief is delivered to a dedicated Notion page and covers the sections below in the following order. Section 1 — Daily Schedule Full calendar for the day pulled from Google Calendar. All events, calls, and commitments in chronological order. Section 2 — Open Projects and Undone Tasks Two sub-sections: (a) MVC high-value work — open projects and incomplete tasks for Most Valuable Clients, filtered to substantive legal work only; and (b) Personal — all open personal projects and tasks without exception. Personal items are comprehensive by design: if it is not surfaced here, it will be forgotten. Source: Notion task and project database. Section 3 — Blocking What is the principal attorney specifically holding up? Items where others in the firm are waiting for a review, decision, approval, or action. Source: Notion matter and task records where assignee or status indicates the ball is in the principal attorney's court. Note to builder: this section requires careful logic design. The agent must infer from status fields and assignee data what is genuinely waiting on the principal attorney versus what is simply unresolved. Work with client during onboarding to define the exact field logic. Section 4 — News Digest Industry News Curated digest of overnight developments in: campaign finance law and FEC activity, election administration, lobbying regulation (federal and state), nonprofit political activity, and government ethics. Format: short summary of each item with a link to the full article. Aim for signal, not volume — 5 to 10 items maximum. US Political News 5 to 10 headlines with links covering: presidential politics, US Senate and House elections, and major gubernatorial races. Stories people are actually talking about, not wire service filler. Section 5 — Firm Workflow Matter-level status summary pulled from Notion, organized by client tier and activity: Status Definition Closed Completed yesterday Moving Action taken yesterday Paused No action yesterday Stuck No action in five or more days Client groupings: MVCs (non-high-value work), Standard clients (all work), and any other open matters. Section 6 — One Month Look Ahead Rolling 30-day forward view pulled from Google Calendar covering: regulatory filing dates and compliance deadlines, matter-level deadlines, client birthdays, holidays, and planned vacations or travel. Anything that requires preparation or awareness in the next 30 days. Section 7 — Personal Financial Summary (If Feasible) Summary of personal financial position pulled from Monarch Money, if an API or MCP connector is available. Candidates should investigate Monarch's API access and address feasibility in their proposal. If not currently feasible, this section is omitted without affecting the rest of the brief. Delivery Notion only — not email. A dedicated page refreshed each morning before 5 AM. Previous day's brief should be archived, not overwritten.   Phase III — Night Maintenance Three nightly agents that run after close of business: timesheet creation, document filing preparation, and Plaud note routing. All outputs are delivered to Notion for principal attorney review. Part 1 — Timesheet Creation Objective Each evening, the agent reviews the day's activity across three sources and populates a timesheet in an existing Notion template for the principal attorney's review and finalization. Sources • Google Calendar — all events and calls attended • Gmail sent items — emails sent that day, grouped by client/matter where inferable • Google Chat — internal messages sent, grouped by thread/matter where inferable Note to builder: Google Chat API access will need to be confirmed alongside Gmail and Calendar MCPs. Confirm availability and any OAuth scope requirements in your proposal. Output: Populated Notion timesheet using existing template structure. Principal attorney reviews each morning, adjusts entries as needed, and finalizes. The agent does not finalize — it drafts. Part 2 — Document Filing Objective Each evening, the agent surfaces documents created or edited that day for the principal attorney's review. The attorney flags finals, and the agent forwards them to the firm's iManage ingestion email address for filing. Sources • Google Drive — documents created or modified that day • Local hard drives — iMac and MacBook Pro Note to builder: local hard drive access requires a locally-running component (daemon, Claude Code instance, or folder-watching script) on each machine. Please address your proposed approach to this in your proposal. Alternative approach for consideration: a designated 'Ready to File' folder on each machine that syncs to Google Drive. The attorney drags filing-ready documents into this folder throughout the day; the agent watches the folder and processes from there. Simpler architecture, device-agnostic, and builds a consistent filing habit. Candidates should evaluate and recommend. Output: A Notion page listing all documents surfaced for that day, with document name, location, and last-modified time. Principal attorney marks finals. Agent forwards marked documents to the iManage ingestion email address. iManage filing is handled by firm IT from that point — no direct iManage API integration required. Part 3 — Plaud Note Routing and Archiving Objective: The principal attorney uses a Plaud AI note-taker on calls and meetings. Each evening, the agent pulls new Plaud summaries, routes them to the appropriate team members, archives a copy to Notion tagged to the relevant client matter, and deletes the underlying audio and transcript from Plaud's platform and the local device. Prerequisite — Plaud API Plaud API or webhook access is a prerequisite for this part. Candidates must investigate and confirm availability before scoping. If Plaud does not currently support programmatic access, this part will require a manual export step as a workaround — please address both scenarios in your proposal. Routing Logic: Similar in structure to Phase I inbox triage routing (MVC/HVP/Standard tiers with task-type overrides) but with distinct rules to be defined with the client during onboarding. Do not assume inbox triage rules apply directly. Archiving: One copy of each Plaud summary is saved to Notion as a note, tagged to the relevant client matter. Tagging logic to be defined during onboarding. Deletion: After successful routing and archiving, the agent deletes: (a) the audio and transcript from Plaud's platform via API, and (b) any local copies on the principal attorney's devices. Local deletion requires the same locally-running component described in Part 2. Candidates may propose a unified local agent that handles both Part 2 and Part 3 local operations.   What I'm Looking For Strong candidates will have: • Demonstrated experience building Claude-based automations or agents — not general AI experience • Hands-on experience with Gmail MCP, Google Calendar MCP, and Notion MCP (or equivalent API integrations) • Ability to build systems that non-technical users can maintain — editability and simplicity are as important as technical sophistication • Comfort with phased delivery — Phase I first, Phases II and III following sequentially based on performance • Experience with professional services clients (legal, financial, consulting) is a meaningful plus • Willingness to execute an NDA and work within a legally sensitive environment What to Include in Your Proposal Please address the following specifically. Proposals that do not engage with these questions will not be considered. • Your proposed technical architecture for Phase I — how you would connect Gmail, Claude, and Notion • Your answer to the Gmail MCP routing output question in Phase I (labeling vs. drafts vs. Notion logging) — what is actually supported and what do you recommend • Your assessment of Plaud API availability and your proposed approach for Phase III Part 3 • Your assessment of Monarch Money API feasibility for the Phase II financial summary section • Your proposed approach to local hard drive access for Phase III Parts 2 and 3 — daemon, sync folder, or other • A comparable project you have delivered — describe the client type, the stack, and what made it work • Your estimated timeline and rate for Phase I, and a rough order-of-magnitude estimate for Phases II and III • Confirmation that you are willing to execute an NDA prior to engagement I am looking for someone who has read this brief carefully and has a specific, informed point of view on how to build it. This is phase one of a longer automation roadmap and the right candidate will be a long-term partner, not a one-time contractor.

I am seeking a highly analytical AI Prompt Engineer & Knowledge Strategist to enhance our AI system's understanding of education civil rights. The role involves crafting precise prompts and developing knowledge strategies to ensure the AI's accuracy and relevance. An NDA is required due to the sensitive nature of the topic. The ideal candidate will have a strong background in AI engineering and a keen interest in education and civil rights.

We sell banknotes, coins, and postcards. We image thousands daily as we sell thousands daily. We currently write our own 40-60 word descriptions of these items using a CSV spreadsheet. We would like AI to write the descriptions! Looking for a widget/software/app/whatever that is PC based-has the ability for us to drag a folder of 1000 images into it and it spits out a spreadsheet of 2 columns. SKU (get into that later) and 40-60 word description. Example: 1952 France 5 Francs banknote or 1964 Kennedy Half Dollar by AI viewing the images of these items. A picture postcard of "visit Virginia" when viewed by AI will be Girl in white hat says visit Virginia" I'm told by a few folks it needs to have scripts to run it....that's not by forte. Yours is hopefully!

I'm an AI automation expert with a growing roster of clients, and I'm bringing on a skilled freelancer to help handle the smaller projects so I can keep up with demand. This isn't a new or one-off operation. I work with many clients already, and bring on new ones every week. I'm looking for someone reliable I can hand work to consistently, not just for a single project. You should be comfortable building AI automations independently and delivering clean, working solutions for client-facing work. To apply, please: - Send a short Loom introducing yourself - Share examples of your previous automation work I review every application personally, so a quick, genuine intro goes a long way. If we're a good fit, there's steady, ongoing work here.

# Upwork RFP: Internal AI Knowledge & Proposal Copilot for General Contracting Firm ## Company Overview Pizzano Contractors is a commercial general contractor based in Alexandria, Virginia, serving clients across the Mid-Atlantic region. Our work includes commercial interiors, law firms, associations and nonprofits, government-leased projects, secure spaces, building repositioning, Corporate Services/small projects, and related commercial construction services. We are seeking an experienced AI developer, consultant, or small team to help us create an internal AI-powered knowledge and proposal tool for firm-wide use. The goal is to help our employees quickly access approved company information, locate forms and templates, draft client responses, support onboarding, and help our marketing team generate first-draft RFP/proposal responses using approved Pizzano Contractors content. ## Project Name Pizzano AI Knowledge & Proposal Copilot ## Project Objective We want to build a secure internal AI tool that allows employees to ask natural-language questions and receive accurate, source-backed answers based on approved company documents. The tool should help our team: * Find approved forms, templates, checklists, policies, and internal documents. * Search approved SharePoint or network-drive content intelligently. * Draft client-facing responses using approved Pizzano language. * Help employees understand Pizzano Contractors, our services, divisions, history, market sectors, project experience, and internal processes. * Upload an RFP and generate a first-draft proposal package using approved content. * Help marketing prepare cover letters, bios, relevant case studies, boilerplate responses, safety language, quality-control language, risk-management language, schedule-management language, cost-control language, and other standard proposal sections. This tool should be designed as an internal assistant and drafting tool. It should not send client communications or final proposals without human review. ## Primary Use Cases ### 1. Internal Knowledge Search Employees should be able to ask questions such as: * Where is the latest approved client satisfaction survey process? * Where can I find the approved subcontractor form? * What is our current project closeout process? * What is the approved language for Pizzano’s safety program? * What is our Corporate Services Division? * What are our main market sectors? * What projects have we completed for law firms? * What experience do we have with secure spaces or government-leased work? The system should return a clear answer and cite or link back to the source document whenever possible. ### 2. Approved Forms and Templates Finder The system should help employees find the correct, most current version of internal documents, including but not limited to: * Forms * Templates * Checklists * Proposal templates * Cover letter templates * Safety documents * Quality control documents * Client survey materials * Project closeout documents * Estimating or handoff documents * Marketing and business development materials The tool should prioritize approved/current documents and avoid surfacing outdated or duplicate versions unless specifically requested. ### 3. RFP Upload and Proposal Drafting The tool should allow a user to upload an RFP, RFQ, or proposal request and then generate a first-draft response using approved Pizzano content. The draft proposal should be able to include: * Executive summary * Cover letter * Understanding of the project * Relevant project experience * Relevant case studies * Team bios * Company overview * Safety approach * Quality control approach * Risk management approach * Cost management approach * Schedule management approach * Similar client experience * Sector-specific experience * Boilerplate proposal responses * Clarification questions or missing information list The output should be editable and should be clearly labeled as a first draft for marketing/team review. ### 4. Client Response Drafting The tool should help draft professional responses to client questions using approved company language. Examples include responses related to: * Schedule concerns * Cost control * Change management * Quality control * Safety * Occupied renovations * Preconstruction * Budgeting * Closeout * Corporate Services/small projects * Secure or sensitive project environments The tool should be able to provide draft responses that are polished, professional, and consistent with Pizzano’s tone and brand. ### 5. Employee Onboarding and Company Knowledge The tool should help new and existing employees better understand the company, including: * Company history * Leadership * Divisions * Market sectors * Internal processes * Tools and systems * Standard operating procedures * Project types * Company values and brand language ## Desired System Capabilities The ideal solution should include: * Secure login/user access. * Permission-based document access. * Integration with SharePoint and/or Microsoft 365. * Ability to connect to approved folders or libraries. * Ability to exclude outdated, draft, duplicate, or unapproved documents. * Source citations or document links in AI answers. * Ability to upload an RFP and generate a structured proposal draft. * Ability to retrieve relevant case studies, bios, and boilerplate from approved content. * Admin controls for managing approved content sources. * Clear separation between approved content and general AI knowledge. * Human review workflow for proposal drafts. * Ability to export drafts to Word, Google Docs, or another editable format. * Strong data privacy and security practices. * Documentation and training for internal users. * Scalable architecture that can grow with the company. ## Preferred Technical Approach We are open to recommendations, but we expect the solution may involve: * Microsoft SharePoint / Microsoft 365 integration. * Retrieval-Augmented Generation, also known as RAG. * Vector search or semantic search across approved documents. * AI model integration through a secure API. * User authentication and permission control. * A web-based internal interface or Microsoft Teams-based interface. * Admin panel for managing knowledge sources. * Logging, feedback, and answer-quality review. We are not looking for a generic chatbot connected to all company files without controls. We need a secure, business-focused tool grounded in approved company content. ## Important Security and Governance Requirements The selected consultant should address: * How company data will be protected. * Whether our data will be used to train public AI models. * How user permissions will be handled. * How confidential documents will be protected. * How source documents will be cited. * How outdated documents will be excluded or flagged. * How hallucinations or unsupported answers will be minimized. * How administrators can approve, remove, or update content. * How proposal drafts will be reviewed before external use. ## Desired Deliverables Please include the following in your proposal: ### Phase 1: Discovery and Planning * Review our goals, document structure, SharePoint/network-drive structure, and current proposal process. * Recommend the best technical architecture. * Identify content governance needs. * Recommend how approved content should be organized. * Provide a project roadmap. ### Phase 2: Prototype / Pilot Build a working prototype that can: * Search a limited approved content library. * Answer employee questions with source references. * Locate forms and templates. * Upload a sample RFP. * Generate a sample proposal draft using approved source materials. ### Phase 3: Full Build Develop the broader internal tool with: * Secure user access. * SharePoint or Microsoft 365 integration. * Approved document indexing. * RFP upload and proposal generation. * Source citations. * Admin controls. * Exportable proposal drafts. * Feedback and improvement loop. ### Phase 4: Testing and Training * Test with a small internal team. * Improve answer quality. * Train marketing, business development, operations, and leadership users. * Provide documentation. * Provide admin training. ### Phase 5: Ongoing Support * Provide support options. * Provide maintenance recommendations. * Recommend future enhancements. ## Questions for Respondents Please answer the following in your response: 1. Have you built similar internal AI knowledge tools for companies before? 2. Have you built tools that integrate with SharePoint, Microsoft 365, Teams, OneDrive, or network drives? 3. Have you built RFP or proposal-generation tools before? 4. What AI models or platforms do you recommend for this use case and why? 5. How would you prevent the tool from using outdated or unapproved content? 6. How would you manage permissions and confidential documents? 7. How would you ensure answers include source citations? 8. How would you reduce hallucinations or unsupported answers? 9. Would our data be used to train any public AI model? 10. What would your recommended phased approach be? 11. What would you need from us to begin? 12. What is your estimated timeline for a prototype? 13. What is your estimated timeline for a full working version? 14. What is your estimated cost range for the prototype and full build? 15. What ongoing support would you recommend after launch? 16. Can you provide examples of similar work, screenshots, demos, or references? ## Proposal Response Format Please structure your response as follows: 1. Executive summary 2. Relevant experience 3. Recommended technical approach 4. Security and privacy approach 5. SharePoint/Microsoft 365 integration approach 6. RFP/proposal generation approach 7. Content governance recommendations 8. Project phases and timeline 9. Deliverables 10. Cost estimate 11. Ongoing support options 12. Relevant examples or portfolio 13. Key risks and how you would address them ## Selection Criteria We will evaluate responses based on: * Experience with internal AI knowledge tools. * Experience with SharePoint/Microsoft 365. * Experience with RFP/proposal automation. * Security and privacy approach. * Ability to create a practical business tool, not just a chatbot. * Understanding of document governance and approved content. * Quality of recommended approach. * Cost and timeline. * Communication style and ability to explain technical topics clearly. * Ability to provide ongoing support. ## Project Budget Please provide pricing options for: * Discovery and planning only. * Prototype/pilot build. * Full production build. * Monthly support and maintenance. We are open to phased pricing and would prefer a clear approach that allows us to test the concept before committing to a full build. ## Desired Outcome At the end of this project, Pizzano Contractors should have a secure, easy-to-use internal AI assistant that helps employees find approved information, understand company processes, draft better client responses, and help our marketing team produce stronger first-draft proposals more efficiently. The tool should make our company smarter, faster, better organized, and more consistent across the firm.

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