I need a advanced agentic system built with persistent memory and up to 6 agents that work together. I am building a franchised coffee shop business. there is so much data that can be pulled together and harvested from customer spending habits and also what is the highest grossing items that sell , vs the most profitable hours of the day. All that data needs to be meshed with the actual Quickbooks data and financials. All that then needs to be balanced with real world site selection for new coffee shop locations. Here is what I need: Agent 1. Pulls information Directly from clover POS automatically. Agent 2. takes Agent's 1 information and cross references with Margin Data from Quickbooks. Recommends New drinks that are both on trend AND High Margin. Agent 3 is the financial Agent. It works directly with Quickbooks. It monitors cash flow and alerts when labor percentage exceeds parameters. It also stress tests expansion and " what if" scenarios. Agent 4. the site selection agent. agent 4 monitors LoopNet, Costar, and parcel data for commercial land available. It cross references traffic count and demographics, it checks competitor coffee presence etc. Agent 5 is the capital strategist. when agent 4 finds a location, it consults with agent 3 which is connected to Quickbooks , it models out loan scenarios, cash flow impact. and helps run " what if " scenarios that it gets asked. Agent 6 is the main Orchestrator that runs everything that I would communicate soley with through Whatsapp. It connects all the agents and pulls data collectively and makes them all work together and stress tests ideas that one agent might find.

I am looking for an experienced AI engineer to fix one critical issue in an existing AI agent. The AI agent is already built and working correctly in most workflow sections. It is a document processing and structured data extraction agent. The agent receives uploaded documents, analyzes the content, extracts required fields, validates the extracted information, and generates a structured final output for the application. However, there is one critical issue in the validation and final-output generation section of the workflow. In some cases, the agent marks the process as completed and returns a final result even when required extracted fields are missing or the validation step has failed. This causes the application to treat incomplete or invalid document data as successfully processed. This is not a full rebuild. I only need an experienced engineer to investigate the existing workflow, identify the root cause, and fix this specific issue. Detailed technical documentation is attached. Please review it before applying. Timeline: Urgent / start as soon as possible Please apply only if you have experience AI agents, LLM workflows, tool/function calling, validation logic, and state management.

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.

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

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

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

I am a partner at a recruiting firm seeking a conversational AI tool to enhance our communication with candidates and clients. Our business is highly conversational, and we primarily use LinkedIn and Loxo. We need a tool that can efficiently manage and personalize our interactions, potentially integrating with our existing platforms.

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.

We are seeking a senior AI developer to build and enhance AI models for our business. The role involves developing, testing, and deploying AI solutions, as well as improving existing models to increase accuracy and performance. The ideal candidate should have strong experience in AI development and be able to work independently on complex projects.

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