Full-Stack Developer — AI Automation SaaS Platform | Python + Chrome Extension + LLM Integration | 4-Week Sprint

@vasudharao9

Hi there, I’m a Computer Science graduate from UC Berkeley, specializing in Artificial Intelligence. I have over a decade of experience working in the AI and ML fields. I’d be happy to help you with this project. Please let me know if you’d like to discuss it further. Thanks!

@MQamar123

With over 7 years of extensive experience as a full-stack developer, I undeniably tick all the boxes for your AI Automation SaaS Platform project. My profound Python backend expertise, especially with FastAPI and async patterns, perfectly aligns with your tech stack requirements. Furthermore, I have hands-on experience in deploying APIs on various cloud platforms which is essential for your project's buildout. solid understanding of payments integration esp. with Stripe, will ensure a secure and flawless financial module for your platform. I place paramount importance on client satisfaction; meaning not only will I navigate this 4-week sprint to meet your alloted timeline but also make sure you're continuously updated throughout the developmental process. My adaptive nature to emerging technologies combined with a steadfast work ethic make me the perfect fit for your project. Let's collaborate and craft an AI-driven automation masterpiece that exceeds expectations!

@KarthikTechCon

Hello, I’m Karthik, a Full-Stack Architect with 15+ years of experience building SaaS platforms, AI automation systems, and browser extensions. I’ve delivered production systems using Python (FastAPI), PostgreSQL/pgvector, Redis, LLM integrations, and workflow automation. Relevant AI Project: Built an AI operations assistant that monitored emails, CRM, and team tools, generated summaries, and triggered automated workflows using FastAPI, vector memory, LLM routing, and Playwright automation. Key challenge solved was cost-aware LLM routing with failover and shared memory layers. Chrome Extension: Strong experience with Chrome Manifest V3 extensions including sidebar UI, service workers, secure API communication, and SaaS integrations. LangGraph / mem0 / Graphiti: Experience with LangGraph and vector memory architectures. Graphiti ramp-up would take ~1–2 days due to prior Neo4j knowledge graph work. Availability: Ready to start within a few days and fully available for the 4-week sprint. LLM Adapter Rule: I implement a single gateway module for all LLM calls to manage routing, guardrails, retries, and logging. Happy to review the detailed brief and begin. Best regards, Karthik Full-Stack & AI Systems Architect | 15+ Years Experience

@mayankinnovative

Hi, As per my understanding: You are building a production-ready B2B AI automation platform for SMB teams. The system will monitor email and calendars, provide AI-generated summaries and actions, automate GUI workflows across web apps, and offer an AI assistant through a Chrome Manifest V3 sidebar extension. The platform includes a Python FastAPI backend, multi-LLM routing with cost controls, vector and graph memory layers, workflow automation using Playwright and LangGraph, voice input, document indexing, and integrations with multiple SaaS tools. It must also include an admin console, partner console, approval gates for critical actions, and be deployed for real customers within a 4-week sprint. Implementation approach: I will implement the system using the predefined stack: FastAPI backend with PostgreSQL + pgvector and Redis, and a Chrome MV3 extension as the primary interface. AI requests will pass through a unified adapter layer that routes between Claude, Gemini, and Qwen with fallback and cost guardrails. Memory will combine ChromaDB, pgvector, mem0, and a Neo4j knowledge graph. Workflow automation will be orchestrated with LangGraph and executed via Playwright using accessibility-tree automation with AI self-healing. Integrations will use OAuth via Nango, while CI/CD and deployment will run on Railway and Cloudflare services. A few quick questions: Is the Chrome extension expected to support both Chrome and Chromium-based browsers like Edge?

@chiragardeshna

Hi there, I understand you are building a production AI automation platform that combines a FastAPI backend, a Chrome Manifest V3 extension, LLM routing, workflow automation, and multi layer memory systems for SMB productivity automation. The main challenge in a system like this is coordinating multiple AI services, browser automation, and real time integrations while keeping the architecture stable, secure, and cost controlled. My name is Chirag Ardeshna, and I am a full stack developer. I have experience building AI powered web platforms that integrate APIs, automation workflows, and scalable backend systems. I typically work with Python based backends, structured API layers, and browser based interfaces while integrating LLM services and automation pipelines. My approach is to structure the backend service layer first, implement the LLM adapter routing architecture, integrate the Chrome extension interface, and then connect automation workflows and plugin integrations in modular stages. I am available for a focused development sprint and can begin reviewing the architecture and milestone plan immediately. Regards Chirag

@vanhuy19892011

Hi, Client. I am very interested in your project and confident that my core skills and extensive experience align perfectly with your requirements. After carefully reviewing the project details, I am certain that I can deliver high-quality results within a short timeframe. I am readily available to begin work immediately and will maintain clear, consistent communication throughout the process. I look forward to the opportunity to collaborate with you. Best regards, Huy

@rakam26

Hello, Just read your post and it seems you are looking for a senior Python/FastAPI engineer who can deliver a production-ready B2B AI automation platform end-to-end, including a Chrome Manifest V3 sidebar extension, LangGraph-based orchestration, multi-provider LLM routing with guardrails, memory layers (ChromaDB/pgvector/mem0/Graphiti+Neo4j), Playwright accessibility-tree automation with approval gates, and Railway deployment. With my years of extensive experience and exceptional expertise in FastAPI async systems, Chrome MV3 extensions, agentic LLM pipelines, Playwright automation, and cost/observability-driven production engineering, I am 100% confident that I can bring your vision to life in the shortest possible time within your fixed stack and milestone gates. Let’s connect and see how great value I can add to your business. Best Regards, Raka

@ahmetTC

Hope you are doing well! This is a true production build with tight constraints. Key risks: async complexity across FastAPI + LangGraph, state sync between extension and backend, OAuth token refresh edge cases (via Nango), GUI automation fragility when DOM changes, and LLM cost blowouts without strict routing + guardrails. The 4-layer memory (Chroma + pgvector + mem0 + Neo4j) must avoid duplication and latency spikes, and the single-adapter LLM rule must be enforced structurally. Most complex AI system I built: a multi-tenant AI ops assistant using FastAPI, pgvector, Redis, LangGraph-style orchestration, and multi-LLM routing (Claude + Gemini). Hardest part was preventing runaway token costs and race conditions in async tool calls; solved via centralized adapter, budget middleware, and task queues with strict timeouts. Manifest V3: built two production extensions with service workers, message passing, content script isolation, and sidebar UIs. Resolved MV3 background suspension issues using event-driven wake patterns and storage sync guards. LangGraph: yes. mem0/Graphiti: not directly; ramp-up 3–4 days given prior vector + Neo4j experience. Availability: can start within 5 days; fully available 4 weeks, no blackout dates. Single-LLM rule: enforce via one adapter module + dependency injection; block direct SDK imports with lint rule. I am ready for you and waiting here. Thank you.

@jorginhoo1

Dear Client, I have carefully read your brief and understand this is a full production build of a B2B AI automation platform with strict milestones, fixed stack, and no discovery phase. The scope spans FastAPI backend, Chrome Manifest V3 extension, LLM routing, memory layers, GUI automation, approvals, integrations, billing, and admin tooling. I have strong Python and FastAPI experience, building async APIs, cost-aware AI pipelines, OAuth flows, and production systems deployed on Railway and cloud platforms. My most complex LLM system handled real-time sports analytics, routing models by task complexity, managing latency, failures, and memory consistency under live conditions. I have built Chrome Manifest V3 extensions with sidebar UIs, background workers, message passing, and secure auth tied to backend APIs. I have hands-on experience with workflow automation using Playwright, Zapier, Make, n8n, and can ramp up on LangGraph, mem0, and Graphiti within days. All LLM calls will route through a single adapter file with guardrails, fallbacks, and provider abstraction enforced across the codebase. Thank you for hiring me. Greeting.

@Microlent

Hello, We are Microlent Systems, experienced in building AI SaaS platforms and automation systems using Python, FastAPI, and modern cloud stacks. We previously built an AI workflow automation platform that used multiple LLMs, vector databases (pgvector/Chroma), and Playwright-based browser automation. The biggest challenge was designing LLM routing with cost control and fallback logic, which we solved through a centralized AI adapter and async orchestration. We have experience building Chrome Manifest V3 extensions with sidebar UI, service workers, OAuth, and backend API communication. LLM Adapter Approach: All model calls go through a single adapter layer that manages routing, cost tracking, and fallbacks with no direct provider imports elsewhere. Best regards, Jenifer Microlent Systems

@MuhammadAsad108

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Document / Email / Calendar Events ⟶⟶ FastAPI Backend (Auth, AI Routing, Cost Guardrails) ⟶⟶ Memory Layer (ChromaDB → pgvector → mem0 → Graphiti) ⟶⟶ LLM Adapter Layer ⟶⟶ Chrome Extension Sidebar / GUI Automation / Voice Input ⟶⟶ Plugin Integrations (Gmail, Slack, Xero…) ⟶⟶ Admin / Partner Consoles Key Highlights ✔ Full Production-Ready Build — not a prototype; ready for paying SMB customers at delivery. ✔ Python Backend (FastAPI) — async patterns, authentication, AI routing, workflow scheduling, and cost management. ✔ Chrome Manifest V3 Extension — sidebar interface with AI assistant access, workflow execution, and on-device voice input. ✔ Continuous Email & Calendar Monitoring — Gmail, Outlook, Google Calendar via IMAP IDLE and CalDAV polling. ✔ Agentic LLM Pipelines — multi-layer memory, cost guardrails, single-adapter file routing to Anthropic Claude, Qwen, and Google Gemini. ✔ GUI Automation & Scheduling — record once, replay workflows across web apps using Playwright and accessibility tree with AI self-healing. ✔ Document Handling & Recall — index uploaded files for semantic queries via the assistant. ✔ Plugin Integrations — 18 services including Gmail, Slack, Xero, Salesforce (read-only), Notion, WhatsApp Business, and webhooks. Best Regards, Asad Python & AI Engineer | Chrome Extension Developer | LLM / Workflow Automation Specialist

@Visharaa2024

Hello, your project description is clear and I understand this is a production-ready B2B AI automation platform, not a prototype. The stack you outlined—FastAPI, Chrome Manifest V3 extension, multi-LLM routing, LangGraph orchestration, and Playwright automation—aligns closely with systems I have built before. I previously developed an LLM-driven workflow automation platform that monitored emails and documents, retrieved context from vector databases, and triggered automated browser workflows across SaaS tools using Python, FastAPI, PostgreSQL (vector embeddings), Redis, and Playwright. A key challenge was designing a reliable LLM routing layer to balance cost, latency, and capability while enforcing safe execution controls. I also have experience building Chrome Manifest V3 extensions with sidebar UI, service workers, and secure API communication for real-time interaction with backend AI services. I work with agent orchestration and vector memory systems like LangGraph and mem0, and can ramp up on Graphiti within 1–2 days. To enforce the single-LLM adapter rule, I would implement a centralized adapter managing ( Claude, Gemini, and Qwen ) with routing logic, token tracking, cost guardrails, and provider fallback.

@drtaimoor

Hi, I’m interested in this project because it combines LLM orchestration, browser automation, and production SaaS architecture. One of the more complex AI systems I built was a business workflow automation assistant using Python (FastAPI), PostgreSQL + pgvector, Redis, and LLM APIs. It processed emails, summarized documents, and triggered automated actions. The hardest part was implementing reliable task orchestration and cost-controlled LLM routing with vector-based memory while keeping latency and token costs predictable. I have experience building Chrome extensions using Manifest V3, including sidebar UI, background service workers, and secure API communication with backend services. I’ve also worked with Playwright for browser automation to replicate user workflows across web apps. Regarding LangGraph, mem0, and Graphiti — I’ve worked with similar agent orchestration and vector memory systems. While Graphiti itself would be new, I estimate 1–2 days ramp-up to integrate it properly. Availability: I can start within a few days and commit fully to the 4-week sprint, with no blackout days. Single-LLM-adapter rule: All LLM calls should pass through a single adapter module handling routing, retries, fallback providers, and cost tracking so the rest of the codebase never calls models directly. Looking forward to reviewing the detailed brief.

@ibrahiman58

Hi, there. Timeline : 14 Days, Budget: 2500USD I am interested your project. Because your project is my major, I believe I am a right person for your project I recently built a multi-tenant AI operations assistant using FastAPI, PostgreSQL with pgvector, Redis, and agent orchestration via LangGraph; the hardest part was cost-aware routing across multiple LLMs with graceful fallback and structured tool execution. I have hands-on experience building Chrome Manifest V3 extensions with service workers, side panel UI, secure message passing, and strict CSP compliance for production deployments. I’ve worked with LangGraph directly; mem0 and Graphiti/Neo4j I have not used in production yet, but given my background with vector stores and knowledge graphs, ramp-up would take approximately 3–5 focused days. I am available to start within one week of agreement and can commit fully for the 2-week sprint with no planned blackout days. My approach to the single-LLM-adapter-file rule is strict abstraction: one centralized provider interface handling routing, retries, logging, and guardrails — zero direct model calls elsewhere in the codebase. I hope to hear from you. Thank you

@Hemantt841

I understand you require a full-stack developer to deliver a production-ready AI automation SaaS platform within a strict 4-week sprint, integrating a Python FastAPI backend, a Chrome Manifest V3 extension, and complex LLM routing with cost guardrails. Your need for continuous email and calendar monitoring, combined with GUI automation and a layered memory system, shows this is a highly sophisticated project demanding precise execution. With over 15 years of experience and more than 200 projects completed, I specialize in Python backend development, PostgreSQL database design, and AI-driven automation solutions. My background includes building AI applications that leverage LLM APIs with agentic pipelines and creating Chrome extensions specifically using Manifest V3, along with workflow orchestration and containerized deployments. For your project, I will focus on integrating the Python backend with the Chrome extension sidebar, ensuring seamless LLM routing through a single adapter file, and implementing the GUI automation using Playwright as specified. I will maintain daily communication and adhere strictly to the four milestone gates, delivering all features including voice input and plugin integrations within the 28-day timeline. I’m ready to start within your required timeframe and would welcome the opportunity to discuss your project in more detail.

@ziadhosaaam

Hello, As a seasoned full-stack developer with a strong focus on Python, I believe I am the ideal candidate to bring your B2B AI automation platform to life. Having honed my skills over multiple projects, my scope is not limited to proficient coding, but rather translating your vision into a functional product that resonates with your audience. From inception to project delivery, I prioritize proper planning and structures that foster seamless maintenance, scalability, and extensibility. In alignment with the exacting details of your project description, I bring specific expertise in FastAPI and async patterns as well as database migrations. This proficiency will ensure smooth deployment for your Python backend API, handling authentication, AI routing, cost management and all business logic seamlessly in the cloud. ConnectionState being an achiever is heavily predicated on timeliness and meticulousness, something this 4-week sprint project demands, I am committed to delivering quality results within set timelines. With a proactive approach to communication and an unwavering commitment to reliable delivery, I offer more than a mere implementation service. I bring creative solutions to the table by virtue of my ability to effectively marry technicality with product functionality; such as your need for clean architecture in tandem with scalable builds. My consistent work ethic over years in the industry has always been to turn ideas int Thanks!

@nevenac888

Hi there, I’ve built an agentic support-ops platform that monitored Gmail and Slack, summarized threads, suggested actions, and executed Playwright workflows with an approval gate. Stack: FastAPI, Postgres, Redis, LangGraph style orchestration, and multi provider LLM routing. Hardest part was reliable tool execution with retries, cost guardrails, and safe fallbacks. I have shipped Chrome Manifest V3 extensions with a sidebar UI, OAuth flows, background service worker messaging, and strict permission scoping. I have used LangGraph. I have not used mem0 or Graphiti yet; I can ramp up in two to three days while implementing with your docs. I can start within one week, full time for four weeks, with no blackout days. I also work via Upwork. Single LLM adapter rule: I will enforce a single llm_client module and inject it everywhere, with code checks to prevent direct provider imports. I can start immediately. Thanks, Yutzu

@murtuza90

After carefully reviewing your project brief, we understand you want to build a production-ready B2B AI automation platform with a Python FastAPI backend, Chrome Manifest V3 extension, AI routing across LLMs, workflow automation using Playwright, memory systems (vector DB + knowledge graph), and integrations with tools like Gmail, Slack, and Salesforce. You need a developer who can implement the full architecture, ensure scalability, automation reliability, and cost-controlled AI routing. Key challenges include stable GUI automation, multi-LLM routing, real-time monitoring, and scalable memory architecture. With 10+ years of experience, our team has built AI SaaS and automation platforms. We’ve prepared a technical blueprint and milestone plan to deliver this efficiently.

@mindmagic1

Timeline: 3 weeks | Cost: $2000 USD Hello, I fully understand your needs and can deliver the full production-ready AI automation platform, including the Python FastAPI backend, Chrome Manifest V3 extension, multi-layer memory, GUI automation, workflow scheduler, and all integrations, within the specified 4-week sprint. Based on my past experience, the most importance is ensuring robust integration of LLM pipelines, browser automation, and AI-assisted workflows while maintaining system reliability, security, and adherence to the single-LLM-adapter rule for maintainable, testable architecture. I will proceed with the project in the following manner: ✔ Implement the Python backend with FastAPI, PostgreSQL with pgvector, Redis caching, LLM routing, and workflow orchestration via LangGraph ✔ Build the Chrome Manifest V3 extension with sidebar AI assistant, voice input, and all GUI automation hooks, ensuring seamless integration with the backend and plugin APIs ✔ Integrate multi-layer memory (ChromaDB, pgvector, mem0, Graphiti) and AI-powered self-healing GUI automation using Playwright and Claude fallback ✔ Configure admin console, partner sub-console, payment workflows via Stripe, and continuous email/calendar monitoring with IMAP/CalDAV ✔ Conduct testing, debugging, and deployment on Railway and Cloudflare infrastructure, ensuring all milestones pass weekly exit criteria Looking forward to discussing more in detail on chat! ✅ Best Regards