Open-Source Email Extraction and Automation Tool Development

@Herculesz

Hi, this reads like a service-business automation project where the real value is in turning ad hoc AI behavior into a modular workflow with clear control points. The main engineering risk is orchestration drift: once intake, decision logic, and follow-up are coupled too tightly, the system becomes hard to debug and unreliable in production. I've built several production AI systems in this category, especially workflow-driven tools that need structured automation instead of a single prompt chain. When a business says “modular,” I usually interpret that as separable stages, predictable state transitions, and clean failure handling. The closest match is AI-Driven Marketing Suite Development -- 2, where I built a staged AI workflow for a local service business with approval gates and optimization loops. Custom Feature Development & Integration is also relevant because it involved reviewing an existing product and shaping maintainable enhancements around business operations. I typically design these systems by separating intake, orchestration, and execution layers so each module can be changed without destabilizing the rest. That makes it easier to add rules, approvals, and reporting as the workflow evolves. For reliability, I recommend explicit validation checkpoints, confidence thresholds for automated actions, and auditability around why a step advanced or failed. That is what keeps AI workflows usable after initial launch. Thanks, Hercules

@rbtech1984

Hi There!!! ★★★★ (Modular open-source AI workflow system using Qwen/DeepSeek for phone, email, and automation agents with phased delivery approach) ★★★★ Project understanding: You need a modular AI automation system for a service business starting with small phased tools like a unified phone intake agent, email parsing workflow, and automated reminders. The focus is on open-source models (Qwen-Agent, DeepSeek), full ownership, and a system that can grow step-by-step without overengineering. ⚜ Design modular AI architecture for phased workflow expansion ⚜ Build Unified Phone Agent for intake, pricing rules, and structured logging ⚜ Develop email parsing workflow into structured customer data extraction ⚜ Automate confirmations and reminder sequences (email/SMS logging included) ⚜ Outbound issue/damage intake agent with structured reporting flow ⚜ Open-source stack setup using Qwen-Agent / DeepSeek / Python tooling ⚜ Coaching + documentation so non-technical team can extend workflows I have worked on AI workflow automation systems and agent-based pipelines where modular design and extensibility were the main focus. I prefer building small working phases first, then improving based on real usage instead of complex overbuilds. My approach is simple: Phase 1 phone agent → test → refine → then expand email and automation layers step by step. Let’s connect and I can help you design a clean, practical system that stays fully under your control. Warm Regards, Farhin B.

@omard211

So you need modular open source AI tools built in phases, starting with a phone agent, all self owned and easy to extend. The smart part of your plan is testing one tool first. Prove value, then expand. I would build Phase 1 on Qwen Agent with an open source voice stack so you keep full ownership. The phone agent greets callers, collects intake, and answers pricing using only your approved rules. It never quotes outside those rules, and logs every call as structured data for review. The design keeps prompts, rules, and pricing in editable files so you add more without rebuilding. I specialise in Qwen and DeepSeek workflows, and coaching non technical teams through each step. Phase 1 timeline and pricing confirmed once I see your call volume and pricing sheet.

@haseebsidd07

Hi There, I understand that you are seeking an open-source, fully customizable tool utilizing Qwen or DeepSeek frameworks for efficient email data extraction and automated responses. Your project aligns perfectly with my expertise, as I have extensive experience in developing modular and scalable solutions tailored to various client needs. I am Abdul Haseeb Siddiqui, and I bring over 6 years of experience in Open Source and AI Model Development. I am committed to delivering high-quality, user-friendly solutions that meet your specifications. Here are some relevant portfolio links showcasing my previous work: - https://www.freelancer.com/u/haseebsidd07 I look forward to discussing how I can help you achieve your goals and provide a solution that ensures full ownership and adaptability for your team. Thank you, Regards, Abdul Haseeb Siddiqui

@johnedwardl

Hi, I will develop your modular AI workflow tools, starting with the Unified Phone Agent. My deep experience with Qwen-Agent and DeepSeek enables me to create a flexible system that meets your requirements without unnecessary complexity. I specialize in building practical, open-source solutions tailored for small businesses, ensuring you retain full ownership and control. For the tech stack, I recommend leveraging Qwen models alongside proven open-source components to ensure scalability and maintainability. The design will prioritize modularity, allowing for easy integration of new rules and workflows as your needs evolve. I’ve successfully implemented similar voice agents and email parsing solutions, which have streamlined operations and improved data collection. For Phase 1, I estimate a timeline of 4-6 weeks, with pricing structured to reflect the incremental nature of your project. Let’s discuss how we can kick off this first phase effectively. I’m ready to help you build these tools and ensure their success. Thank you!

@marcosfermin

Howdy! Your project is a strong fit for how I work. I build modular, open source AI systems in small deliverable phases, and I coach non technical clients through every step so you always understand what you own and how to extend it. I have built AI operations copilots and RAG based intake systems that parse unstructured text from tickets, emails, and logs into clean structured records, which maps directly to your email extraction workflow. I have also built outbound and inbound call handling agents using open source voice stacks, and I have wired LLM workflows to rule based guardrails so the agent never goes off script or makes promises outside approved boundaries. For your phone agent specifically, I would use a Qwen2.5 or DeepSeek V2 base model served locally or on a low cost VPS, paired with Qwen Agent for tool calling and workflow orchestration, Whisper for speech to text, Coqui TTS or Piper for voice output, and Twilio or Asterisk for call routing. Thank you. Marcos.

@mayr81

You’re looking for a modular, open-source, low-risk phased rollout, not a single monolithic rebuild. I can build a Unified AI Phone Agent that stays fully under your control by separating components into: (1) channel layer (telephony + SMS/email routing), (2) policy/rules layer (pre-approved pricing, availability, greetings), (3) agent layer (Qwen-Agent/DeepSeek-style tool-calling), and (4) data layer (structured call logs + JSON intake objects). Phase 1 (Phone Agent) will: greet callers, explain the owner is unavailable, collect name/callback/service address/job details/urgency, answer only from your pricing sheets/rules, route to callback/email when appropriate, and never generate custom quotes outside policy. Every interaction will be persisted as structured data for later review/analytics. Design for extension: rules/prompts stored versioned (Git/DB), tools registered per capability, and workflow graphs composed so you can add new data, rules, prompts, or entire workflows without refactoring the core intake pipeline.

@cliftonc20

Hello, this reads like an AI workflow architecture problem more than a single feature build, and that’s usually where modular boundaries matter most. The real engineering risk is not model output alone; it’s orchestration drift between intake, decision logic, and downstream actions once the workflow starts handling real exceptions. I’ve built production AI systems where the hard part was separating ingestion, processing, retrieval, and operator control so the workflow stays maintainable as rules change. The closest match is DocIntel AI — Document Intelligence & Event Extraction Platform, where I designed an end-to-end automated pipeline with distinct processing stages, background jobs, and traceable outputs. Enterprise ProxyTool Client App is also relevant architecturally because the system was intentionally split into independently scalable modules. I usually structure these systems with clear boundaries between trigger intake, task execution, state tracking, and human-review paths. That keeps changes localized and avoids brittle prompt chains turning into business logic. For reliability, I recommend explicit validation gates, retry rules, confidence thresholds, and auditability around every automated action. If useful, I can sketch the workflow architecture first and map the failure points before implementation. Clifton

@carlosd178

Hello, As a result of a detailed review of your project requirements, I fully understand the scope and expectations. I have experience handling similar types of AI workflow automation, agent-based tools, and modular business process systems and I'm available to start your project right now. I bring strong expertise in Open Source AI, Qwen/DeepSeek workflows, AI Agents, voice automation, email parsing, structured data extraction, workflow orchestration, API integration, and modular system design. My approach would be to start with Phase 1: a unified phone intake agent that follows approved scripts only, collects caller details, answers pricing/availability from your rules, avoids custom promises, and logs structured data for review. I would recommend an open-source-friendly stack using Qwen or DeepSeek for reasoning/extraction, a voice layer for calls, a simple database for logs, and modular rule/prompt files so future email parsing, reminders, and outbound intake can be added without rebuilding. I have one quick question. • Do you already have a phone provider such as Twilio, Telnyx, or another VoIP system, or should I recommend the best option for Phase 1? I would be glad to discuss the first phase, timeline, and pricing. Looking forward to hearing from you. Best regards, Carlos.

@assadfarid

Greetings, It looks like you're aiming to build practical, modular AI tools to streamline your service business operations, focusing on low-risk phases and open-source solutions. I can help you create a Unified AI Phone Agent that efficiently handles calls, gathers essential customer information, and maintains a clear structure for logging interactions. My approach will ensure that the system is flexible and allows for easy updates without starting from scratch. With experience in Qwen-Agent and DeepSeek, I specialize in developing modular frameworks that keep your systems fully under your control. I prioritize simplicity, using proven components to avoid unnecessary complexity. My focus is on coaching non-technical clients, helping you understand and manage the tools effectively. I'm excited about the opportunity to work together and contribute to your business's growth. Best regards, Assad Farid

@artemb35

Hi there! I’m genuinely excited about the chance to collaborate on this modular AI workflow project. It sounds like a fantastic opportunity to create something impactful for your service business. I recently worked on a modular intake system for a small clinic that involved a voice agent capable of handling calls and gathering information without making any promises—just like your Unified AI Phone Agent. It was a hit! One distinctive feature I implemented was a smart routing system that adjusted responses based on caller urgency. This not only streamlined interactions but also increased customer satisfaction. I believe a similar approach could enhance your workflows. As for my experience, I've built several agents using Qwen and DeepSeek, focusing on modular frameworks to ensure scalability and easy updates. I specialize in creating simple, effective solutions without unnecessary complexity. I’d love to know more about your preferred metrics for success in the first phase. Can we hop on a quick Zoom this week to discuss how we can kick this off? Looking forward to hearing from you! Artem

@ammarimt05

quick one before i quote, are you hosting this locally or cloud? the open source requirement changes the whole setup, n8n self-hosted vs cloud means different paths for the phone and email modules. either way i can build each piece as a standalone trigger. phone intake, email follow-up, appointment booking, all wired through n8n so you can switch any module on or off without breaking the others. first module live in hours, not days. what does your current phone setup look like? twilio, something else?

@nimran54

Hey , I am a US based Freelancer, I just finished reading the job description and I see you are looking for someone experienced in AI Model Development and Open Source. This is something I can do. I will design the first phase as a modular, self-owned workflow using Qwen-Agent with open-source voice and text components, so your team can start small and expand safely. The Unified AI Phone Agent will be built to greet callers, capture intake details, apply your approved rules for pricing and availability, and save structured logs for review without making off-script promises. For the email workflow, I will parse threads into clean fields like name, phone, address, and service request, then flag missing data for follow-up. To keep everything flexible, I would structure the system around separate prompt, rules, and workflow modules so you can add new services, scripts, and automations later without rebuilding the core. I can also guide you through the setup in plain language so you stay fully in control of the stack. I am ready to start by reviewing your pricing sheets, intake rules, sample calls, and example emails. Drop me a message before placing an order price can be discussed after discussion.- Hey , Write a line like, I am a US based Freelancer, I just finished reading the job description and I see you are looking for someone experienced in AI Model Development and Open Source. This is something I can do. Read the job description carefully and tell how you will solve

@stanleyo46

Hi, there. I’ve built modular AI automation workflows before with voice intake, email parsing, structured data extraction, rule-based responses, logging, reminders, and human-review safeguards. Your phased approach makes sense. I would not build this as one large fragile AI system. I’d start with a focused Unified Phone Agent that can greet callers, explain the owner is unavailable, collect name, phone, address, job details, urgency, and preferred contact method, then answer only from approved pricing and availability rules. For the stack, I’d keep it open-source friendly: Qwen or DeepSeek through Ollama/vLLM where possible, FastAPI, SQLite or PostgreSQL, Docker, n8n for workflow routing, and editable config files for prompts, rules, pricing sheets, and escalation logic. Twilio can be used only if fast phone integration matters more than full open-source ownership. The key is that rules and prompts should stay editable outside the code, so you can add new workflows later without rebuilding. Do you already have pricing rules and call scripts, or should I help convert rough notes into safe agent rules?

@johnc150

Hi, How are you? I can help you design a clean, modular AI workflow system built around open-source tools like Qwen-based models, DeepSeek workflows, and lightweight agent frameworks so you keep full ownership and control. My approach would focus on building each component as an independent module (phone intake agent, email parsing agent, confirmation/reminder automation, and outbound issue handling), so you can test value quickly and expand without rebuilding the system. For Phase 1 (Unified AI Phone Agent), I would design a structured voice-to-intake pipeline that captures caller details, applies strict rule-based responses for pricing/availability, logs everything into a structured database, and integrates with email/SMS for follow-ups when needed. The architecture would prioritize simplicity, extensibility, and open-source compatibility so you can continuously add new prompts, rules, and workflows without vendor lock-in. Best Regard, John

@extreamcode

Hi, Your brief is clear: you want small, controllable AI phases, not a risky all-in rebuild. That mindset is exactly right for a phone-and-email workflow system that must stay modular, auditable, and fully owned by you. I build practical AI/web automation systems with Python, APIs, structured logging, and clean backend workflows. I’d approach Phase 1 as a narrow, reliable intake agent: capture caller details, apply your rules for pricing/availability, log every interaction, and keep the stack open-source friendly so new prompts, rules, and workflows can be added without rework. I’ve shared an initial estimate based on your description, and once we go over a few technical or functional details, I’ll confirm the exact cost and delivery schedule. I can also help you choose a lean stack around Qwen-Agent or DeepSeek, with orchestration and data storage designed for long-term expansion. Would you like Phase 1 to prioritize live call intake only, or include voicemail and callback routing from day one? Questions I’d like to explore: which telephony provider are you leaning toward, do you already have pricing sheets/rules in a structured format, and should Phase 1 support voicemail fallback or callback-only handling? Looking forward to your reply so we can finalize the exact plan. Best regards, Asad

@SherozKhalid9

Hi, I'm Khalid, an AI workflow specialist who's helped small service businesses like yours with practical tools. For example, I built a Qwen-Agent voice intake system for a home repair company that greets callers, collects job details and pricing questions using their rules only, logs calls, and suggests callbacks. I also created a DeepSeek email parser that extracts name, address, service needs from threads and flags missing info reliably. I specialize in modular Qwen/DeepSeek agents, phone voice flows, email automation, and guiding non-tech teams in simple phases. My suggested stack is Qwen-Agent + open-source LiveKit or Asterisk for calls, DeepSeek for logic, and SQLite for data. It's fully open-source, self-owned, lightweight, and easy to extend without complexity or lock-in. Phase 1 Unified Phone Agent: 2-3 weeks for MVP with your rules and testing. First-phase pricing: $1,200 with milestone payments. I'll design it modular with separate prompt files, rule configs, and plugin-style workflows so you can easily add new rules, data fields, or agents later while keeping full control and ownership. Project Overview: Start small with the inbound phone handler using your pricing sheets, review results, then expand. Ready to begin the first phase when you are. Best regards, Khalid

@bondoc0526

༺❖༻ Dear Client ༺❖༻ Thanks for posting about my specialist job area. Your requirements perfectly match my experience and work style. I have experience building modular AI automation systems using open-source LLM stacks, including Qwen-compatible and DeepSeek-based workflows, with a strong focus on phased delivery and fully owned architectures. I can design your Phase 1 Unified AI Phone Agent as a structured, rule-based intake system that captures caller details, service requests, urgency, and callback preferences while strictly following your pricing rules. The system will log all interactions in structured format for easy review and future automation. The architecture will be fully modular, allowing you to add new workflows like email parsing, reminders, and outbound call agents without changing the core system. All prompts, rules, and workflows will be externally configurable for full control. Given the opportunity I am confident I can deliver a clean, scalable Phase 1 system with a strong foundation for future AI expansion. Best regards, Glenn Bondoc

@eshmumkhan

Hi there, I have built modular AI workflows for service businesses using Qwen-Agent and DeepSeek, and I agree with your phased approach. For your Unified Phone Agent, I will build a call handler using open-source voice frameworks with a Qwen model that collects name, number, address, job details, urgency, and answers pricing only from your approved rules. For email extraction, I will use DeepSeek to pull name, address, service requested, timing, and summary from messy threads, flagging missing info. For confirmations and reminders, I will build a scheduler for email and text at 3, 2, and 1 day before. For damage intake, an outbound agent that gathers date, location, damage details, and requests photos without making promises. My stack is Qwen-Agent, DeepSeek, open-source SIP or Twilio, PostgreSQL, and FastAPI – all under your control and easy to extend later. I can start immediately. Let me know if you want a quick call. Kind regards