Cloud-Based Jarvis Automation Suite

@augurstech

A Warm Hello! We are readily available to start working on this project! We are confident to provide you an internal AI operating system that understands your business, supports your team with context-aware intelligence, and evolves alongside your agency. This is exactly the type of AI platform our agency specializes in designing and delivering. We have extensive experience building secure AI-powered business platforms that combine LLMs, RAG (Retrieval-Augmented Generation), workflow automation, and scalable cloud infrastructure. Our focus is on creating AI systems that use your proprietary data to deliver reliable, business-specific insights while maintaining enterprise-grade security and ownership. We'd welcome the opportunity to discuss your roadmap, validate the architecture, and deliver an AI platform that your team fully owns and can confidently scale for years to come. Best regards, Ana

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re looking to build a cloud-based AI agent platform for your creator management agency. The goal seems to be creating an efficient automation suite that can streamline various processes and enhance user experience. I’ve worked on several similar platforms and understand the key technical challenges involved. I plan to leverage technologies like PHP and cloud services, particularly Azure and AWS, to develop a robust solution that meets your needs. I have a few questions to get a better understanding: Q1 – What specific user roles do you envision for the platform? Q2 – Are there any existing systems or APIs you want this suite to integrate with? Q3 – What type of data sources will the AI agent need to access for optimal performance? I’d be happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@kamran2012

Hi, I will deliver your two-agent chat platform (Acquisition + Finance) with streaming Claude responses, RAG over your agency docs using pgvector, and an admin panel where you edit prompts and refresh the knowledge base without redeploying. On a recent agency tool, chunking docs into ~500 token segments with overlap and pairing that with pgvector cosine search gave noticeably better recall than naive full-doc embedding. I will apply the same approach to your creator roster, financials, and pipeline data. Questions: 1) For the Acquisition agent, are prospect records currently in a spreadsheet, a CRM, or just scattered across inboxes? 2) For Finance, will you upload monthly figures as CSVs or do you want a structured input form inside the admin panel? Ready to start whenever you are. Kamran

@gutibs

Hi! Solid brief — well-scoped, sensible stack choices, and the milestone breakdown makes this straightforward to price. On your technical questions: for RAG we'd chunk by semantic boundary (not fixed tokens), embed with OpenAI's text-embedding-3-small, store in pgvector, and retrieve with a hybrid score — cosine similarity + a recency/metadata filter so the finance agent doesn't surface stale figures. Streaming to React is done via ReadableStream on the server piping Claude's SSE chunks straight to the client — no buffering, true token-by-token. Per-tenant API keys go encrypted at rest in Postgres (AES-256, server-side only), decrypted in-process per request, never serialised to the client. One honest note: you ask for a shipped LLM product with a link. We've built RAG pipelines and agent architectures for clients on private infrastructure — nothing we can link publicly without their sign-off. If that's a hard filter, fair enough. If you're open to a technical conversation to vet the approach instead, we're happy to go deep. For base scope we'd land in the £3,800–4,400 range across your four milestones. Voice and live platform integration we'd quote separately once we know which platforms (the API shape matters a lot there). Who does the work: small team of two on this kind of build, not outsourced. Happy to keep going on the details if the approach resonates. Gustavo & the DoTheCode team

@Hubready

Hello!, I am a US-based senior software engineer(frontend, backend, ecommerce, etc) with 15+ years of experience building AI automation platforms, SaaS systems, APIs, and cloud microservices. I read your Cloud-Based Jarvis Automation Suite brief carefully, and the goal is clear: not just a chatbot, but a dependable internal AI agent platform for HOTR that automates work and saves real team time. My approach: 1. Confirm the core workflows and data sources 2. Design the agent + API architecture 3. Build the automation layer with secure integrations 4. Test, harden, and deploy with logging and monitoring I’ve built similar systems using PHP, Python, Node.js, React, OpenAI, RAG, Salesforce, custom APIs, and cloud automation tools. Relevant examples include a creator ops dashboard, a sales automation hub, a support triage assistant, and a workflow engine for a subscription business. Could you please clarify the following questions to help me better understand the project? 1. Which tasks should Jarvis handle first, and what must be automated vs human-approved? 2. What systems need to connect on day one, especially Salesforce, email, storage, or internal tools? 3. Do you already have a preferred setup on Azure or AWS, or should I propose the best-fit architecture? If you want someone who pays attention to the details and builds this properly, I’m your guy. Happy to discuss the best MVP path and move fast. James Zappi

@krupnovdenis

Hello! We can build a private cloud-based AI assistant for your agency. 1. Are you looking for a fixed-price delivery for the base scope? 2. Do you already have the documents and figures ready for the knowledge base? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@vishal1145

Your Finance agent will fail under concurrent queries if you do not implement request queuing and token-budget management — Claude's API will throttle or return incomplete responses when multiple users ask complex questions simultaneously. This creates a poor UX exactly when your team needs fast answers most. Quick questions - are you planning to enforce per-user rate limits or a shared agency-wide token pool? And do you need audit logs showing which team member asked what financial question? Here is the architectural approach: - API DEVELOPMENT: Build a Node/TypeScript middleware layer that queues Claude streaming requests, manages token budgets per agent, and handles retry logic for rate-limit errors without exposing keys to the frontend. - MICROSERVICES: Deploy separate containerized services for RAG ingestion (chunking + OpenAI embeddings into pgvector), agent orchestration (prompt routing + context injection), and auth (Supabase RLS policies isolating tenant data). - CLOUD COMPUTING: Host on Railway with auto-scaling workers, encrypted environment variables for Anthropic/OpenAI keys, and Postgres backups to S3 — zero-downtime deploys via health-check endpoints. I have built 4 production AI agent platforms for SaaS clients processing 200K+ queries/month, including a creator-economy analytics tool with live Stripe/Patreon integrations. Let's schedule a 20-minute technical call to walk through your RAG strategy and multi-tenant security model before you commit budget.

@acesolution

Hi I read completely we can discuss in more detail if you allow me so we can complete your dedicated project Thankyou

@FizzaNadeemK

Hi I can build your private AI agent platform with a secure RAG architecture that understands HOTR’s internal operations, creator pipeline, and financial model. I’ll develop the chat system with streaming Claude responses, role-based access, editable agent prompts, conversation history, and a pgvector-powered knowledge base for accurate context retrieval. My approach for RAG will include document chunking, embeddings, semantic search, and controlled retrieval so agents respond using your real agency data instead of generic answers. I’ll deliver a scalable cloud deployment, admin controls, full source ownership, documentation, and a clean framework that allows future agents to be added easily. I’d be happy to discuss the milestone plan, timeline, and share relevant AI product experience before starting. Best Regards, Fizza Nadeem K

@kishanbwts

I built a custom AI assistant for a sales team that included lead tracking, follow-up nudges, and context-aware pipeline questions—similar to your Acquisition agent. For knowledge retrieval, I chunk documents by sections and embed them using vector search (pgvector fits well) for precise RAG results. Streaming LLM replies to React UI uses server-sent events, showing tokens as they arrive for fluid chat experience. I’d start with schema, auth, and a basic streaming chat with your first agent to rapidly validate the flow. Then I’d layer in knowledge base ingestion with chunked markdown docs, embedding, and semantic search tuned to your agency’s context. Per-tenant API keys can stay encrypted server-side with role-based access control so none leak client-side. For optional extras, I can offer separate quotes for revenue-platform integration and Slack alerts once the core is stable. I work solo and can deliver the base scope in about 6 weeks, full source and handover included. Could you share a sample of the knowledge base docs? Also, do you have existing Slack or CRM APIs to connect later? Ready to get started as soon as you say go.

@joshlui

<<<✔Consider it DONE✔>>> YO! I understand your project and I'm eager to help. As someone who has traversed the realms of both web development and AI integration, I’m uniquely qualified to create a bespoke solution for HOTR that marries your crucial need for an AI assistant with my proficiency in WordPress, AI-powered web tools and backend/frontend development. Not limited to providing generic website enhancements, my main practice lies in producing practical results and augmenting your existing business operations – exactly what you’re aiming for with this AI Agent platform. Looking forward to being part of your project! You will surely be impressed by my work! Not sure what the next step is? I offer free and professional consultation -- I'm just a text away. All the very best, Josh

@Aaleen110

I understand you need a private, cloud-hosted AI agent platform for HOTR, specifically a web-based chatbot with two specialist agents. My experience building custom AI workflows for content agencies has resulted in a 20% reduction in administrative overhead for client onboarding. For v1, I will develop a React frontend connecting to a Python Flask API, deployed on AWS. The Acquisition Agent will use LangChain and OpenAI's GPT-4 to manage your creator-signing pipeline, storing prospect data in a PostgreSQL database and sending automated follow-up prompts based on defined stages. What are the specific data points you'd like the Acquisition Agent to track for each prospect beyond their stage? Ready to start as soon as you confirm scope.

@vladang

The biggest failure point here isn't the chat interface—it's making sure both agents consistently answer from your agency's actual data instead of falling back to model assumptions. I'd build the RAG layer first with document chunking, embeddings stored in pgvector, and retrieval injected into every request so the Acquisition and Finance agents always work from the latest uploaded knowledge and figures. For streaming, I'd send Claude's token stream to the React UI over Server-Sent Events so responses appear naturally as they're generated, while conversation history is persisted alongside each agent. I'd also keep prompts editable in the admin panel, since prompt tuning shouldn't require deployments. I work personally, not through a team. One thing I can't honestly provide is a shipped LLM product link, since I won't fabricate experience. If that's a strict requirement, I'd rather be upfront than claim work I haven't done. The rest of the architecture, deployment, and handover are straightforward to deliver on your infrastructure with your accounts and API keys.

@revival786

Greetings! I can build your private AI agent platform with an Acquisition agent and a Finance agent, using a React frontend and Node backend with PostgreSQL and pgvector for RAG. I will implement streaming replies, editable system prompts, knowledge base uploads with chunking and semantic retrieval, conversation history, multi-user auth with roles, and secure cloud hosting. I have shipped LLM-powered products and can share examples. I will provide full source code, deployment, and handover documentation. Let me know your preferred hosting platform and I will share a milestone breakdown. Thanks, Revival

@TayyabNawaz1

Drawing from over 8 years of comprehensive web and software development experience, I'm confident in my ability to construct the specific cloud-based Jarvis Automation Suite that your creator management agency demands. In addition to my skillset, which includes specialization in AI Chatbot Development and PHP, I understand the nuances that come into play when building an automated system. From your need for an acquisition agent to manage your creator-signing pipeline all the way to a finance agent for detailed finance inquiries, I’ll build these two specialist AI agents tailored exactly to what HOTR requires. The core requirements you've specified align perfectly with my modus operandi; from a web chat UI that is not only protected but also accessible on multiple devices to persistent and searchable conversation histories, I assure you no stone will be left unturned. Furthermore, my experience with Node/TypeScript makes me well-suited to tackle the backend requirements, and I’ve used similar front-end technologies like React in dynamic projects that have required streaming replies similar to what you expect in this project.

@ahmaddar2970

This innovative project to develop a cloud-based AI assistant for HOTR mixes cutting-edge tech with strategic customization, ensuring tailored, effective solutions for managing creator pipelines and finances. With my expertise in cloud-hosted AI solutions, I will craft a secure, scalable, and user-friendly platform, incorporating state-of-the-art retrieval-augmented generation (RAG) for knowledge management and stream. I will use React for the frontend and Node/TypeScript for the backend, leveraging PostgreSQL with pgvector, and secure API integrations. I propose a phased approach over 45 days, ensuring quality and seamless deployment, including full source code ownership, admin tools, and comprehensive documentation. Optional add-ons such as platform integrations and speech features can be provided at additional costs. I am committed to delivering a robust, secure, and functional AI suite that exceeds expectations. The bid reflects the project complexity, technical demands, and the need for a tailored, scalable solution.

@KasjanovaEste

Hi, The key focus of this project is to develop a cloud-based Jarvis Automation Suite for HOTR, a creator management agency. I will create a web-based chat bot with two specialized AI agents to streamline your creator-signing pipeline and provide insights into agency finances. With over 10 years of experience in developing AI-driven solutions, I am confident in delivering a solution that meets your specific requirements. If my approach aligns with your vision, I would love to discuss this project further. Best Regards. Ps. https://www.freelancer.com/u/KasjanovaEste , this is my profile and you can check my previous projects.

@bcosley

Hi, You're building a private, cloud-hosted AI assistant for HOTR with two specialist agents and a live knowledge base. Data ownership, streaming replies, and admin-editable prompts need to be baked in from day one. I've delivered AI chat workflows with per-tenant data isolation and end-to-end validation for agencies. I'd start by validating current data sources and a single end-to-end chat flow, then add the knowledge base ingest and RAG loop. I'd keep prompts editable via admin UI and verify streaming in the browser. The tricky part is keeping per-tenant secrets and prompts isolated while delivering streaming responses without leakage. What should happen if a KB document is updated mid-conversation, should the agent use the latest content immediately or batch updates? How should we handle multi-user edits to prompts, concurrent edits and role-based approvals? If we're aligned, I can outline the implementation phases before kickoff. Best regards, Brandon

@giovannif87

Hi — Giovanni here. Building your Cloud-Based Jarvis Automation Suite needs more than just a functional chatbot — the key is ensuring your AI agents deliver precise, context-aware insights while maintaining seamless multi-user access and easy admin control. Your project reminded me of a recent AI assistant I developed for a SaaS client managing sales pipelines and finance queries, where I integrated RAG over custom knowledge bases with streaming LLM responses in a React frontend. The challenge was balancing real-time data accuracy, secure multi-tenant API key management, and editable system prompts without redeployment. For your project, I would first map out your main workflows and data ingestion processes, then implement a robust RAG pipeline using markdown chunking and pgvector embeddings for semantic retrieval. Streaming responses will be handled via WebSockets to React with token-by-token updates. API keys and secrets will be encrypted server-side with strict role-based access control. A few questions before estimating the final scope: Q1 - Do you prefer OpenAI or Gemini embeddings for your knowledge base, or are you open to recommendations based on cost/performance? Q2 - Will you require integration with live revenue platforms immediately, or is that a later phase? I’m excited to help build a reliable, scalable AI assistant tailored to your agency’s unique needs. Let’s discuss next steps and timeline! Looking forward to your reply.