Forum Intelligence: Project Brief & Initial Rollout 1. Executive Summary & Objective Forum Intelligence is a beginning as a localized data retrieval, processing, and archiving system designed to scrape public municipal records and state legislative data for public oversight. The immediate objective is to build a functional, highly resilient prototype focused on the Tri-Cities region (Burbank, Glendale, and Pasadena, California). The system will autonomously ingest messy, unstructured municipal data (City Council meeting minutes, agendas, public notices, and legislative PDF text, recorded mp4), clean it, and make it fully searchable and queryable via a localized AI agentic framework. 2. Phase 1 Scope: The Tri-Cities Rollout Th engineer will be responsible for building two primary pillars: A. Resilient Scraper Bots • Target Ingestion: Monitor and pull data from Burbank, Glendale, and Pasadena municipal portals and California legislative feeds. • Data Types: Brittle HTML sites, heavily nested tables, public notices, legislative drafts, and massive unstructured PDF archives. • Requirements: The scraping architecture must be exceptionally robust, utilizing intelligent error handling, retry semantics, and pagination tracking to handle frequent municipal website layout changes without breaking the pipeline. B. Ingestion & Vector Pipeline • Parsing: Extracting clean text from poorly formatted documents and scanned PDFs. • Local RAG (Retrieval-Augmented Generation): Chunking and embedding the data locally into a vector database (e.g., pgvector, Chroma, or Milvus) to enable semantically accurate entity linking and contextual search. 3. Targeted Hardware Stack To ensure maximum data security, strict public oversight integrity, and predictable operational costs, Forum Intelligence is skipping commercial cloud APIs in favor of an on-premise, localized NVIDIA enterprise deployment. The production roadmap aligns precisely with the new computing patterns detailed in NVIDIA’s latest hardware roadmap: • Inference & Token Generation: Running local open-weight frontier models (e.g., Neotron 3 Ultra or Claude/Llama equivalents) optimized for reasoning and long-context tool use. • Compute & Orchestration: The backend infrastructure is architected around NVIDIA’s dedicated agentic architecture, utilizing high-instructions-per-clock (IPC) Vera CPUs paired with Vera Rubin GPUs. • Memory & Storage Processing: Utilizing NVIDIA’s unified memory fabric and data processing units (DPUs) for ultra-low latency context management, KV caching, and fast vector database retrieval. 4. Immediate Milestones for the Engineer 1. Architecture Design: Map out the database schema and local inference ingestion loop. 2. Tri-Cities Scraper Deployment: Write and deploy the initial automated bots for Burbank, Glendale, and Pasadena. 3. Local MVP Pipeline: Demonstrate a local RAG pipeline where a user can query the Tri-Cities scraped records and receive grounded answers with exact source attributions. The above was AI generated from months long conversations with Gemini. The goal is to prove the concept then roll out to LA County, state of CA, and then the country.

We're an early-stage AI startup building Hirey — an agent-to-agent marketplace that runs inside various AI tools via a plugin. Think "Upwork for AI agents": your agent finds, vets, and books the right human or agent on your behalf. We're looking for 5 AI Agent enthusiasts to install our plugin (openclaw, codex, Opus, Gemini), try a sample workflow, and give us honest feedback on a 30-minute Zoom. About 30 minutes of your time total. What you'll do 1. Install the Hirey plugin in Codex. It connects your agent to Hirey’s remote MCP server, so there’s no local server, Node setup, Claude Desktop, or JSON config edit required. Setup is usually: enable the plugin, restart the AI agent you installed on 2. Connect to Hirey and run one sample workflow we send you. 3. 30-min Zoom with the founding team. We'll ask what confused you, what worked, what you'd change. Camera on, recorded. Who we're looking for - Someone who has used AI tools in the past, especially for any coding or technical tasks - You use Claude Desktop, Cursor, Codex, or similar AI dev tools regularly. - Bonus: you've built or contributed to anything in the AI agent / MCP / LangChain / Claude Code ecosystem. What you get - $25 flat, released via Upwork on call completion. - Early access to the Hirey AI agent network if you want to keep using it. - A direct line to the founding team — we genuinely want your criticism. To apply, answer these in your proposal 1. Have you used an AI coding tool before? Which one(s)? 2. One sentence on a recent AI/agent project you've worked on or played with. 3. Your timezone and earliest availability this week. We'll respond within 24 hours and schedule calls within 2 business days. No long applications, no portfolio review. Optimizing for speed.

I am working through a design agency on an application for their end client. I think the agency will need you to contract with them directly, but I will manage the project for them. I have scoped out the project already, and our plan is to internally perform a design phase with the client to produce a prototype with Lovable. There may be minor changes to scope after that design phase. The purpose of the app is to create bespoke wedding gown concept images for potential customers of an online wedding dress store. I have provided the details below and attached as a PDF 1. Customer opens an AI dress/gown design experience from a link in their separate e-commerce site. - This can be presented in its own page, we don't want a chat window to be present on any other page - This will be a chat-based interface built into the content area of the page, instead of a popup - The design must be elegant, and match the theme of the e-commerce site - The top navbar and footer don't need to be exactly recreated in this subdomain site, but should look similar enough to create a seamless experience - There will be no integration with the e-commerce site, we need to keep these web apps completely separate 2. The customer must sign up for an account and purchase one credit to begin the AI session - We will need to set up the subdomain site with its own payment processing system and login system - The payment integrations are Stripe to facilitate credit card and Apple pay, and a basic Paypal integration 3. The customers should be able to use a magic link to sign into their accounts, instead of having to remember a password - The account should automatically remember the browser to reduce friction for future access to the app 4. When an AI session begins, we will ask the customer a series of questions programmatically to prime the AI agent so that it can deliver better results - The questions will need to use conditional logic, such that the first question which determines one of 3 main conditional tracks: What type of gown are you looking for? Wedding Gown, Evening Gown, Cocktail Dress - If Wedding Gown is selected, the AI should suggest for the customer to take go to a bridal store and pictures of themselves in different dresses they like and upload the pictures, and describe what they do and don’t like about each dress. It can ask this in the freeform chat, since it may make the most sense to let them fill out the entire questionnaire to stay engaged, and we should reduce the costs of development for the questionnaire by omitting any unnecessary UI that the freeform chat can provide. - It may be best to always just prompt for them to upload the inspirational image at the beginning of the freeform chat so we can omit unnecessary programmatic UI, but in the case of the Wedding Gown it will specifically ask the customer to peform the above task. - We may have other specific questions to add to the questionnaire depending on what conditional track the customer chooses, though only the 3 main branches of conditional logic based on dress type will be required. - Examples of general questions it will need to ask are as follows: -- silhouette -- neckline -- sleeves -- fabric -- embellishments -- color -- train -- length -- closure -- lining -- structure -- inspiration -- event type -- I didn’t get the exact list of questions yet from my client that we should ask in the initial questionnaire. Let me know if you will need this information to accurately provide a price for the development of this application 5. We should not display a concept image after the programmatic questionnaire, the customer will be taken directly into the freeform chat from the questionnaire. - The AI agent may start with an overview of the selected choices from the questionnaire, then can generate concept images at its discretion. 6. The AI should guide the customer through a freeform conversation - The conversation should begin with the AI asking the customer to subjectively describe their dream dress 7. The AI should also make a suggestion near the beginning of the conversation for the customer to upload at least one inspirational photo, but photo upload is optional - If the customer uploads an initial inspiration image, the AI agent should not attempt to figure out body type, measurements, or any other information that we can gather programmatically. - It should treat the inspirational image the same way it would treat any image the customer uploads during the freeform chat, to reduce the cost of development as much as possible. 8. Customer can proceed with a freeform conversation description - The customer should have the option to type in a chat and to upload images - The purpose of the conversation is for the customer to describe the desired dress or gown in an open-ended way 9. AI generates one or more concept images based on the conversation, as soon as it can once it has enough information - The AI model we select should be very good at generating these types of images, this is probably the most important quality the AI model needs to have - The concept images should have the same quality as the final image 10. It is acceptable to generate the gown on a mannequin or a real human model, however the dress must be photorealistic, not a sketch or cartoonish rendering. 11. The concept images that the AI generates and the final image should portray the body type and skin color which the customer specifies - It is very important for us to render the image of the garment on the correct body type - ex. Hourglass, pear-shaped, thin, plus sized, etc. -- Specific body measurements do not need to be factored into the rendering of the body type, it just generally needs to be able to render the garment on different body types. - It is also very important for us to render the garment on a human model or mannequin which has the same or similar skin color as the customer inquiring -- This is important for the customer to judge the garment color and fabric type that will look best on them -- This is also important to make the app inclusive for people of all racial backgrounds who might use the app -- It may be best not to display the face, or if human models are used, to use pictures of models with different racial backgrounds, to avoid bizarre mismatches between facial characteristics and skin tone - The AI agent should ideally prioritize pictures of garments from our client’s website to use as inspiration when it generates renderings in the freeform conversation, along with the customer’s description of what they want. However this is not a hard requirement, so it could be eliminated from the requirements if it greatly increases devlopment effort. - The requirement for the quality of the images that are generated will be somewhat subjective and so we will need to budget time for our client to request revisions to this based on their review of the system. - We need to build the image generation part upfront to ensure the quality is acceptable before we spend time on other parts of the application. 12. AI asks whether the generated concept is generally what the customer wants - Customer can revise the concept conversationally 13. AI can regenerate or refine images after customer feedback 14. The tone of the conversation the AI has with the customer is important. - We will want it to speak like a friendly expert seamstress. - This requirement will be somewhat subjective and so we will need to budget time for our client to request revisions to this tone based on their review of the system. 16. We ideally want the agent (both chat and image generation) to have deep expertise about fabrics and these types of garments in general, so it can guide the user through prompts, and render the chosen fabrics correctly - I think freeform chat will be necessary for the customer to explain which fabrics should be used where on the garment, instead of gathering this informaton in the programmatic questionnaire - The customer will likely revise the fabric selections after they see the initial renderings of the garment - We would like to avoid the costs of training an AI for this, so ideally we should use commercially available AI models which have been trained for this purpose, instead of having to train our own model. Prompting the AI with this information might be a cost-effective way to teach it this expertise 17. There will be certain restrictions on what types of colors or fabrics can be used in the dress designs - So, the agent should know these restrictions when it has the freeform conversation with the customer. - For example, the store owner will not be able to produce dresses with neon colors, tie dye colors, etc. - Our client will articulate a list of restrictions for us before we begin the project. 18. AI should never display links to other websites, or suggest for the customer to navigate to other websites 19. This AI might not need to be trained specifically for this industry, but we should at least use prompting to direct it to gather this kind of information, and to give it some background about what each of these things mean, so it can describe them to the customer. We basically need to make it as knowledgable as possble while keeping costs low. 20. The AI system the system should remember their active conversation - Since the customer will be required to have an account to use the AI system we can use that to automatically save the AI conversation - The saved conversation should preserve all the information that the customer input since the beginning of the AI session - A customer can only have one active AI session at a time - The customer cannot resume an AI session that has been completed - We don't need to provide a way for the customer to see the details of completed AI sessions 21. AI should have a fallback/human-help option if the customer gets stuck or the AI fails. - The fallback should collect enough information for an admin to follow up manually, so it should present a form in order to ensure that all the necessary information gets collected - A message should be displayed above the form, or somewhere on the page, to inform the customer that the entire conversation will be sent along with the form submission, so they know that they do not have to type all the details of the AI conversation - The app must present a button outside of the chat prompts after 3 - 5 chat messages have been sent, so the customer knows they have the option to terminate the AI conversation and manually ask for help. - That button would display the form - We don't want to display the button before any conversation has happened because we don't want customers to skip the chat altogether. -- One of the business goals of this app is to allow custom inquiries without overwhelming the support staff - Site admins must have the ability to adjust how many messages the button will display after, so they can control this threshold after they observe the results of real conversations - After the button initially displays, it should remain present in the view so the customer can easily access it at any point in the conversation 22. The freeform chat must be limited to something like 50 to 75 messages, in order to avoid excessive charges from the 3rd party AI services - This threshold should be adjustable from an admin portal - If this threshold is reached during the conversation, then we should force the customer to use the fallback form from requirement #21 to submit their inquiry 23. Customer can submit the completed design inquiry when satisfied. - During the submission process, the chat must ask for the following information, and present the following pre-written messages. This doesn't actually need to be executed by the AI model, but it can just be programmatically presented to the customer: - Ask for customer contact info, including name, email, and phone number. - Ask for requested event/date, while making clear the date is not guaranteed. - Ask for seamstress-relevant measurements, including bust, waist, hips, hollow-to-hem, shoulder width, bust point, underbust, waist-to-floor, arm length, bicep, wrist, back width, torso length, height, shoe height, and preferred fit. -- I still have to refine this list with the client, I am not sure if it needs to ask for all these things, or if there are some different things that I haven't listed here which it needs to ask for -- When it asks for this information it should display links under each measurement type to articles which describe how to produce each of the measurements. We can hardcode these links or allow the admin to specify each, they don't need to be generated by AI. - Prewritten disclaimer text should display. 24. The final submission should notify a list of email addresses set by a site admin. 25. The final submission will completely consume the credit used to purchase this AI session - The AI conversation cannot be resumed after the final submission - Another credit must be purchased to start a new AI conversation - New AI conversations will not have any memory of the previous conversations, any new AI conversations will start from a clean slate 26. Admins must have the ability to manually reset a credit, or assign a credit for free and cancel a current session, so the customer can start a new AI conversation. - This doesn't need to be very user friendly for the admin. If a session is reset this way, no knowledge of the previous conversation needs to be preserved. 27. Admins should be able to review partial, or completed conversations within a list in the admin portal - Each line item should display a status indicator to show if the conversation has been submitted yet, if an admin has began the review process, or if the item has been handled: Ex. In Progress, Submitted, In Review, Awaiting Payment, Handling, Ready To Ship, Closed - Admin should be able to see the answers to the programmatic questionnaire - Admin should be able to review the full conversation history - Admin should be able to review all uploaded photos/files - Admin should be able to review all AI-generated images, and the final one should be clear to them - Admin should be able to see the collected technical design details and measurements 28. Pricing of the garment remains manual and is handled by after review, the AI should not give any quote or present any pricing even if asked by the customer. 29. If the customer asks for pricing, the AI should display a prewritten script like this: "Pricing will be determined by the store owner after this conversation has been reviewed." 30. Invoices and payment will be handled manually through native WooCommerce custom order/invoice functionality which is already present in the e-commerce site, the AI system doesn't need to handle this at all. I mentioned this above on the requirements, but I want to reiterate since it is important and a hard requirement for how the development milestones must be structured: - The requirement for the quality of the images that are generated will be somewhat subjective and so we will need to budget time for our client to request revisions to this based on their review of the system. - We need to do the image generation part upfront to ensure the quality is acceptable before we spend time on other parts of the application As an optional add-on to the scope of this project, can you give a separate estimate to enhance the AI such that it understands which kinds of modifications will increase or decrease the cost of producing the gown, so it can guide the customer in case they are asking for very expensive things. - It shouldn’t give any specific price numbers, but should give the customer guidance if additions or alterations will significantly increase or decrease the cost of production. - This will be to prevent the customer from being surprised when the store owner manually follows up with them with the price of the garment they designed. This client did agree to adhere to a strict schedule to provide feedback after each round of development, given that we complete each round of development on the schedule we agreed to. - However, this client has deviated from agreed schedules multiple times in the past on other projects I did with them, so you should factor that into your timeline and cost estimations - We cannot increase the development cost mid-way through the project, however we can adjust the development timeline if the client deviates from the schedule In your proposal, please also include a quote or estimate for the cost of hosting and ongoing maintenance after the app has launched - Our client can pay for the hosting directly - We will need at least ongoing updates to patch security vulnerabilities and ensure uptime of the app and all its features which will be defined by the scope of this project - We don't need a 100% 24/7 uptime SLA, but basically just keeping everything up to date so it stays stable, and we'd need someone to respond to outages within 24 hours - Outage response can consist of simple rollbacks, if necessary, as long as all the chat session info is at least provided to the client as a CSV or similar, along with all graphic assets from any conversations, so they don't lose any data from an outage - I would set the expectation with my client that we would treat any future support or enhancement requests to be additionally charged for on an as-needed basis

We are a small software development team actively using Claude and GitHub Copilot for development on an ASP.NET and Azure technology stack. We’re looking for an experienced AI coach/consultant who can review how our team currently uses AI, identify opportunities for improvement, and help us work more effectively with Claude Code, agents, and AI-assisted development workflows. What we're looking for: Experience implementing and scaling AI-assisted development practices within agile software teams. Deep hands-on experience with Claude Code, agents, and agent-driven workflows. Experience designing and configuring agents for development, QA, testing, documentation, and other engineering processes. Ability to conduct team workshops, coaching sessions, and practical reviews of our current workflows. Strong understanding of software engineering best practices, not just AI tooling. Bonus skills: Software architecture and platform engineering experience. Experience setting up and optimizing MCPs (Model Context Protocol), tools, integrations, and custom Claude configurations. Knowledge of AI workflows beyond development, including QA, support, project management, and internal operations. The engagement would likely include a mix of: Reviewing our current processes and tooling One-on-one discussions with team members Team coaching and training sessions Recommendations for improving productivity, quality, and collaboration using AI Please include examples of teams you have coached, AI development workflows you have implemented, and your experience with Claude agents and MCP-based tooling

26ers is building Human + AI operating systems that help organizations improve decision quality, execution speed, and organizational leverage. We are seeking a customer-facing AI Architect who can work directly with executives, operational leaders, and technical teams to design practical AI solutions that solve real business problems. This role helps organizations identify high-value AI opportunities, redesign workflows, modernize operations, and implement Human + AI operating systems that improve execution, decision-making, and organizational effectiveness. The ideal candidate can move fluidly between customer conversations, workflow discovery, solution design, governance considerations, and implementation planning. Responsibilities • Participate in customer discovery and solution design conversations • Analyze current-state workflows and identify AI transformation opportunities • Design Human + AI operating models, agentic workflows, and operational systems that improve execution and decision-making • Create solution blueprints, implementation plans, and statements of work • Collaborate with implementation developers and technical delivery teams • Consider data governance, security, compliance, and operational requirements throughout solution design • Contribute to the development of reusable 26ers methodologies, frameworks, and institutional knowledge • Design systems that capture, structure, and operationalize organizational knowledge and institutional learning Ideal Experience • Experience designing AI-powered business workflows and operational systems • Strong understanding of OpenAI, Claude, and modern LLM-based solution design • Experience with workflow orchestration platforms, AI agents, automation systems, and API-based architectures • Strong understanding of data governance, information security, and enterprise AI deployment considerations • Experience translating business requirements into solution architectures, implementation plans, and statements of work • Customer-facing experience in consulting, solution engineering, professional services, digital transformation, or technical advisory roles • Experience conducting discovery workshops, workflow assessments, and current-state/future-state design exercises • Understanding of operating model design, workflow modernization, and organizational transformation • Strong written and verbal communication skills with executive stakeholders • Ability to leverage AI tools to rapidly produce architecture drafts, blueprints, requirements documents, implementation plans, training materials, and customer deliverables Nice to Have • Experience with Gemini, MCP, LangGraph, CrewAI, AutoGen, or similar orchestration frameworks • Experience with n8n, Make, Zapier, or workflow automation platforms • Experience with vector databases, RAG architectures, and organizational knowledge systems • Experience building or deploying multi-agent systems • Government, healthcare, financial services, or other regulated industry experience • Startup, founder, or early-stage company experience • Experience designing systems that capture institutional knowledge, operational learning, or organizational intelligence • Military, consulting, enterprise software, or transformation leadership experience Success in this role • Quickly understand a client's operating environment, workflows, and business objectives • Identify high-value opportunities for AI-enabled transformation and operational leverage • Translate customer goals into practical solution designs, implementation plans, and delivery roadmaps • Balance innovation, governance, security, and operational realities • Help organizations move from AI experimentation to operational execution This role may begin on a contract basis and expand into a longer-term strategic partnership as 26ers grows.

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

We are a growing construction law firm seeking an experienced AI strategist and solutions architect to advise our AI Council and help us identify, prioritize, and build practical AI solutions that improve productivity, reduce administrative burden, and enhance legal workflows. This is not a purely advisory role and it is not a traditional developer position. We are looking for someone who can help us evaluate opportunities, recommend technology solutions, perform build-versus-buy analyses, and then assist in building approved AI agents, workflows, and automations. Our initial focus is internal productivity and workflow improvement rather than client-facing applications. ABOUT US: We are a boutique construction law firm with attorneys and staff across multiple states. We have an active AI Council and are committed to thoughtful, practical adoption of generative AI. Current technology stack includes: * Microsoft 365 Copilot * Microsoft Teams * SharePoint * NetDocuments * Centerbase * Lexis Protégé Available: * Microsoft Power Platform * Power Automate * Copilot Studio * Power BI WHAT WE NEED: We are seeking a long-term advisor who can serve both in an advisory and developmental role: Strategic Advisory * Evaluate AI use cases submitted by our AI Council * Recommend practical implementation approaches * Perform build-versus-buy analyses * Evaluate AI vendors and emerging technologies * Advise on legal-industry AI trends and best practices * Help establish governance and implementation standards Solution Design & Development * Design AI agents and workflow automations * Build approved solutions within the Microsoft ecosystem * Create proof-of-concepts and pilot programs * Integrate solutions with existing firm systems where appropriate * Assist with testing, refinement, and deployment Initial Project Focus: Our first major initiative will likely involve evaluating whether we can replace or replicate portions of commercial contract review software through an internally developed solution. We are particularly interested in exploring: * Contract review workflows * Clause analysis * Risk identification * Contract summaries * Knowledge management integration IDEAL QUALIFICATIONS * Experience working with law firms, legal departments, or legal technology companies * Experience implementing generative AI solutions in professional services environments * Deep understanding of Microsoft Copilot ecosystem * Experience with Copilot Studio, Power Platform, and workflow automation * Ability to communicate effectively with attorneys and non-technical stakeholders REQUIRED * Experience designing and implementing AI agents or workflow automations * Ability to evaluate business requirements and recommend appropriate solutions * Strong understanding of current generative AI tools and trends * Ability to translate strategy into practical implementation Please provide a cover letter with your qualifications and answers to the following questions: 1. Describe a law firm or legal department AI project you personally led. 2. What is your experience with Microsoft Copilot, Copilot Studio, Power Platform, and Power Automate? 3. Describe a build-versus-buy recommendation you made and the factors considered. 4. If asked to replicate a significant portion of a contract review platform internally, how would you approach the project? 5. How do you measure ROI and success for AI implementations? We are looking for a practical advisor and builder who can help us make sound decisions, avoid unnecessary spending, and implement solutions that deliver measurable value.

Overview We’re looking for an experienced AI engineer or AI systems builder to help us design and build an internal intelligence layer that turns fragmented customer data into actionable growth opportunities. Right now, customer insights live across multiple disconnected systems — CRM notes, product usage data, emails, support tickets, and spreadsheets. While the data exists, it is not structured in a way that helps us proactively identify expansion opportunities, churn risks, or account-level next steps. We want to build an AI-driven system that continuously synthesizes this information and helps our team understand: * What is happening inside each account * Where expansion or upsell opportunities exist * Which accounts are at risk and why * What the next best action should be for each customer ⸻ What You’ll Build You will design and implement an AI system that can: * Ingest structured and unstructured data (CRM, emails, notes, product signals) * Build dynamic “account intelligence profiles” for each customer * Identify patterns across accounts (usage drops, feature gaps, expansion signals) * Generate clear, human-readable account summaries * Recommend next-best-actions for sales, customer success, or leadership * Surface expansion opportunities based on behavioral and contextual signals * Flag risk signals early with supporting reasoning ⸻ Ideal Output For each account, the system should be able to generate: * A concise account narrative (“what’s going on here”) * Key signals and anomalies * Expansion opportunities (with rationale) * Risk factors (churn or stagnation indicators) * Suggested actions for the team this week * Confidence level and supporting evidence ⸻ Why This Matters We are sitting on a large amount of customer data, but most of it is passive. The goal is to turn it into an active intelligence system that helps our team: * Prioritize the right accounts * Increase expansion revenue * Reduce churn risk * Spend time on the highest-impact opportunities This becomes a core internal system that directly impacts revenue efficiency and customer outcomes. ⸻ Ideal Candidate We’re looking for someone with experience in: * LLM-based systems and agentic workflows * Data pipelines and multi-source data ingestion * Prompt engineering + structured reasoning systems * CRM systems (Salesforce, HubSpot, etc.) * Customer analytics / product analytics * Building internal AI tools or copilots * Backend + API integration work Bonus if you’ve worked on: * RevOps tooling * Customer success platforms * Data enrichment or account intelligence systems * SaaS growth analytics ⸻ Deliverables * System architecture for AI customer intelligence layer * Data ingestion and normalization approach * Prompting / reasoning framework for account analysis * Prototype system (or working MVP) * Output format for account intelligence reports * Documentation for internal expansion and scaling * Recommendations for tooling (build vs buy decisions) ⸻ Engagement This starts as a project-based build, but could expand into a long-term role as we scale the system across our entire customer base and additional workflows. ⸻ To Apply Please include: * Examples of AI systems or agentic workflows you’ve built * Experience integrating LLMs with real business data * Your recommended architecture for a system like this * Any clarifying questions you’d want answered before starting

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.

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