We are an investment firm with a portfolio of healthcare companies. We are seeking to begin building our data capture systems across our business and layer AI to surface summarize and store insights. This is a process that is in parallel to our operations team SOP'ing our process in anticipation of expansion. It is our opinion that we have a relatively simple business process from end to end and lots of potential to capture useful data signals across each department/function. We have drafted a rough business process / data ontology diagram showing our preferred approach. We are seeking an expert to: 1 ) Create lightweight data systems to capture data signals from end to end across our business (Recruiting to Onboarding to Scheduling to Payroll to Finance to Legal to). This also includes organizing and categorizing our past / existing data in addition to capturing signals for future data. 2 ) Layer AI / agentic AI automations that can surface insights, categorize and aggregate info, populate knowledge databases, etc. Example Data Signals / Use Cases: Fireflies recorded meetings Tagging emails in inbox as Legal/Finance/Scheduling/Onboarding etc Job Board Postings Airtable (For building a lightweight scheduling/employee management system) (For storing a knowledge database and rolodex) To Apply: Please briefly present an instance of implementing a similar lightweight solution to capture data signals and convert the data into meaningful and actionable insight via AI

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

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 looking for an experienced ASR engineer to build a production-ready speech-to-text system for a low-resource language. I already have approximately 3,000 prepared audio segments, totaling about 10 hours of audio, with clean and consistent transcripts ready for immediate use. Data preparation and segmentation are already handled. The initial 10 hours of audio will serve as the first milestone. After that, the engineer will be expected to continue training and improving the model with additional data until the system reaches a target WER of 10% or below. Your responsibility will focus on: Fine-tuning a Whisper-based model for high transcription accuracy Optimizing word error rate (WER) over time Providing inline/embedded start timestamps per phrase Building an efficient inference pipeline for both real-time and batch transcription Structuring evaluation and improvement workflows Preparing the system for deployment and integration into a web platform Providing clear documentation and guidance so I can independently continue training and improving the model over time without ongoing engineer involvement The goal is to reach strong accuracy at launch, with a clear process for continued improvement as more data becomes available. Please describe your experience with Whisper fine-tuning or similar ASR model training in your proposal.

We are building a next-generation workflow automation platform that combines deterministic business rules, artificial intelligence, document intelligence, and human review workflows into a single operating system. This is not a traditional CRM project. Our vision is to develop a doctrine-driven platform where business rules serve as the system authority, AI serves as an analytical and drafting layer, and human reviewers serve as the final compliance checkpoint. We are seeking an experienced engineer or engineering partner who can help architect and build the platform from the ground up. Project Objectives The platform will: • Ingest and analyze large volumes of structured and unstructured documents • Extract data from reports, PDFs, and supporting documentation • Apply rule-based workflow logic • Generate AI-assisted recommendations and draft outputs • Maintain complete audit trails and workflow transparency • Route work through human review checkpoints • Support future deployment of local AI infrastructure for privacy and performance Core Architecture The system will be built around four primary layers: 1. Rules Engine * Deterministic business logic * Workflow orchestration * State management * Trigger and escalation logic * Audit tracking 2. AI Layer * Document analysis * Classification * Pattern detection * Summarization * Draft generation * Structured outputs 3. Local Processing Layer * OCR * Document parsing * Data extraction * Vector search * Local inference capabilities * Privacy-first processing 4. Human Review Layer * Quality assurance * Workflow approvals * Compliance review * Exception handling Initial Development Priorities Phase 1 • User authentication • Client record management • Document upload system • OCR and document extraction • Workflow engine • Rule-based status management • Review dashboard Phase 2 • AI-powered document analysis • Automated classification • Recommendation engine • Draft generation workflows • Response parsing Phase 3 • Local AI infrastructure • Vector database integration • Knowledge retrieval system • Multi-agent workflow orchestration • Advanced automation Desired Technical Experience Required • React / Next.js • Node.js, Python, or similar backend framework • PostgreSQL or equivalent relational database • REST APIs • Cloud infrastructure (AWS, Azure, or GCP) • Workflow automation systems • Document processing pipelines Preferred • OpenAI APIs • Anthropic APIs • Retrieval-Augmented Generation (RAG) • LangGraph, LangChain, or similar frameworks • Vector databases • OCR technologies • AI agent architectures • NVIDIA AI ecosystem • Local model deployment What We Are Looking For We are not looking for someone who simply builds forms and dashboards. We are looking for a builder who understands how to combine: • Rules engines • Artificial intelligence • Workflow automation • Human review systems • Scalable software architecture The ideal candidate enjoys solving complex business process problems and translating expert decision-making into software systems. Engagement Structure Open to: • Fractional CTO • Lead Architect • Senior Full-Stack Engineer • AI Systems Engineer • Development Agency • Long-term strategic technology partner To Apply Please provide: • Relevant project examples • Experience building workflow automation platforms • Experience with AI-powered applications • Technology stack recommendations • Estimated availability • Preferred engagement structure

Looking for an elite problem solver, either an ex-MBB consultant with deep technical fluency or a seasoned AI Product Manager, to act as the crucial bridge between business operations and technical AI implementation. You will be responsible for dissecting client operations, prototyping the AI logic, writing flawless engineering briefs, and driving the development team to the finish line. What You Will Do: Workflow Audits & Discovery: Lead deep-dive discovery sessions. Map out existing operational workflows, identify bottlenecks, and pinpoint high-ROI opportunities for AI automation. Prototyping & Technical Translation: Because you are an AI power user, you will prototype the initial AI prompts and logic. You will then translate your operational findings into crystal-clear engineering briefs (or PRDs) so developers know exactly what to build. Development Management: Act as the project lead during execution. Manage the engineering workstream, clear roadblocks, and ensure the final solution is delivered on time, within budget, and achieves the strategic goal. Stakeholder Alignment: Act as the primary liaison between non-technical business leaders and the technical development team. Requirements & Qualifications: Top-Tier Pedigree: 1+ years of experience at a top management consulting firm (MBB, Big 4, Tier 2) OR proven experience as a Technical/AI Product Manager. Advanced AI Fluency: You are an AI power user. You possess advanced prompt engineering skills (e.g., chain-of-thought, few-shot) and know how to force LLMs to output reliable, structured data. Elite Structured Thinking: You excel at turning highly ambiguous, messy business processes into clean, logical frameworks (using Lucidchart, Miro, etc.) and comprehensive technical requirements. Project Leadership: Proven track record of managing technical resources, tracking deliverables, managing budgets, and driving teams to a deadline.

Conversational AI / LLM Consultant We are looking for a Conversational AI and LLM specialist to support the strategy, design, development, testing, and improvement of AI-powered chatbot and voice automation solutions across multiple business groups. Responsibilities: Help identify, evaluate, and prioritize Conversational AI and LLM use cases across defined business units. Advise on best practices for Conversational AI strategy, LLM architecture, prompt design, orchestration, retrieval, integrations, and development. Recommend improvements across AWS services, Amazon Lex integrations, LLM workflows, and supporting AI infrastructure. Collaborate with the development team on chatbot, voice bot, Lex, and LLM-based implementations and configurations. Conduct QA testing to validate Conversational AI functionality, accuracy, performance, reliability, and user experience. Support the development of solution frameworks, automation workflows, dashboards, application management tools, and fulfillment processes. Assist in designing and extending multilingual Conversational AI solutions in English and Spanish. Support multiple lines of business, call flows, customer journeys, and AI-assisted workflows. Ideal Candidate: Experience with Conversational AI, LLMs, and chatbot or voice automation systems. Familiarity with Amazon Lex and AWS AI services is helpful, but broader LLM architecture experience is equally important. Strong understanding of prompt engineering, AI orchestration, integrations, QA testing, and production AI workflows. Ability to translate business requirements into practical AI-driven solutions. Experience with multilingual conversational design, especially English and Spanish, is a plus.

Title: Backend Developer — AI Data Pipeline, Vector DB & Real-Time Push API Post: We are building an automated backend system that continuously crawls public web sources, processes and indexes content using AI, and delivers updates via webhooks. Looking for someone who has built this type of system before and can move fast. NDA required before project details are shared. What you’ll build: • Web crawler network —. • AI processing pipeline — cleans, deduplicates, chunks, and embeds ingested content into a vector database using an LLM embedding model. Quality scoring and incremental updates required. • Push API — monitors for significant content changes and delivers updates via webhook endpoints automatically. Includes configurable push schedules per subscriber, REST query endpoint, API key authentication, and token usage tracking per key. Tech stack (flexible — use what you know best): • Python (FastAPI) or Node.js • Any vector DB — Pinecone, ChromaDB, Supabase • Any LLM API — Anthropic or OpenAI • Any scheduler — n8n, APScheduler, cron • Redis for queue management • Railway, Render, or AWS for deployment Requirements: • NDA signed before kickoff — non-negotiable • Must have built RAG pipelines or vector DB systems in production — not tutorials • Must have experience with web crawlers and scheduled job pipelines • Must have experience with webhook delivery systems • GitHub or portfolio showing relevant deployed work required • 95%+ Job Success Score preferred • Individual contractors only — no agencies To apply include: • Example of a similar system you’ve built — web crawler, RAG pipeline, or push notification API • Your preferred stack for this type of build • Brief technical approach in 3–5 sentences • Hourly rate and availability to start Budget: $50–$80/hr Timeline: 3 weeks — focused sprint with daily check-ins

We are looking for 2-3 AI engineers/developers to help us build/complete an AI go-to-market (GTM) tool that looks at both structured and unstructured data, runs it all through our AI engine, and provides insights and recommendations straight to sales people. The tool is able to handle large volumes of data and provide actionable insights for business decision-making. Our engine consists of a prioritization algorithm, pattern matching and sentiment analysis. We use our recommendation engine to deliver the output (insights) straight to our tool which is "Apple-simple", intuitive and gamified. No more sales time wasted on looking at dashboards and trying to agree on which insights are important and which should be acted on. What you'll do Own features end-to-end across theFastAPI backend (IEngine) and Next.js 15 / React 19 frontend— from Claude prompt design to UI polish. Extend the intelligence pipeline: meeting ingestion (Google Drive + Deepgram realtime), Claude-driven action card generation, Neo4j relationship graph, and Supabase-backed state. Build customer-facing dashboard surfaces — deals, gamification, coaching, trust graph — with TypeScript, Tailwind, shadcn/ui, and D3. Operate WebSocket transcription sessions and async job pipelines reliably under real meeting load. Instrument with Sentry, harden auth (NextAuth :left_right_arrow: JWT :left_right_arrow: FastAPI service-to-service), and keep deploy pipelines green. Build with simulators: when you can't test against live meetings, generate realistic synthetic transcripts through our simulator service. Stack you'll work in Backend: Python 3.11, FastAPI, Uvicorn, PyJWT, Anthropic SDK, Deepgram, Neo4j, Supabase (Postgres + realtime), WebSockets Frontend: TypeScript, Next.js 15 (App Router), React 19, NextAuth 5, Tailwind, shadcn/ui, D3, Stripe Infra: Fly.io, GitHub Actions, Sentry, Supabase AI: Claude (Opus/Sonnet) for transcript analysis, action card generation, and agentic dev workflows What we're looking for 4+ years building production web applications, ideally across Python and TypeScript. Comfort designing and shipping features against an LLM API — prompt iteration, structured outputs, evals, cost/latency tradeoffs. Real Claude or OpenAI production experience required. Experience with multi-tenant SaaS patterns, JWT auth, and one or more of: graph databases, realtime systems, audio/transcription pipelines. Comfort with AI-assisted development workflows (Claude Code, Cursor, etc.) — not just as a code-completion tool, but as a way to plan and ship features. Bias toward shipping. Small surface area, high ownership, no committee.