AI SaaS Developer Needed — Build AI Sales Agent MVP for Home-Service Businesses

@augurstech

A Warm Hello! We are readily available to start working on this project!! We can build the MVP as a production-ready platform where AI doesn’t simply generate responses—it can understand conversations, qualify leads, use tools/actions, follow up automatically, and work toward booking appointments. We can also help define the AI conversation architecture, prompts, tool schemas, qualification rules, database model, and API contracts before implementation so the MVP remains focused and extensible. We’d be happy to take ownership of the project from architecture through development, testing, deployment, and post-launch improvements, and can continue supporting the platform as you add voice AI, additional CRMs, advanced analytics, and more sophisticated sales automation. We are ready to discuss your existing requirements, preferred MVP scope, timeline, and budget and can get started shortly. Best regards, Ana

@irsolutions

With over a decade of experience in building high-performance AI solutions, I understand your need for an AI SaaS developer to create an MVP of an AI sales agent for home-service businesses. My background in scaling systems for over 1 million users and developing high-security FinTech applications directly applies to the complexity of this project. A strategic insight for ensuring scalability is to leverage modern LLM APIs like OpenAI for advanced AI capabilities. With my successful track record in building and scaling AI-powered systems, including Telegram Mini Apps serving millions of users, I am well-equipped to handle the challenges of creating an AI sales assistant for home-service businesses. I invite you to reach out to discuss how we can collaborate to bring your AI SaaS project to life. Let's connect to explore the roadmap for developing a cutting-edge AI sales agent MVP for your home-service businesses.

@diektech

Hi — Elias here from Miami. I understand you're looking to build an MVP for an AI-powered sales agent tailored to home-service businesses. The goal is to create a tool that streamlines customer interactions and enhances sales processes. What usually matters most here is ensuring the system can handle varying user roles and maintain scalability as demand increases. A common issue in systems like this is managing the complexity of AI interactions alongside backend integrations, which can lead to bottlenecks if not designed thoughtfully. My approach involves creating a modular architecture that prioritizes maintainability and future expansion. I’d ensure a robust API layer for seamless communication between the AI components and your front-end. This enhances reliability and allows for straightforward updates and feature additions. I've worked on similar AI-driven platforms, focusing on optimizing performance and user experience. This experience helps me foresee potential pitfalls and proactively address them. A few questions to better understand the scope: Q1 – What specific user roles will the system need to accommodate? Q2 – Are there particular integrations with existing tools or platforms that you envision? Q3 – What are your scaling expectations as the user base grows? Happy to discuss the details and suggest the best technical approach. Looking forward to hearing from you.

@ost

With over a decade of experience, my name is Shujaat and I am proud to offer you my professional expertise as a Full-Stack Web & eCommerce Developer for your project. My skillset, which notably includes Python, Node.js, and React, perfectly aligns with your requirements for developing an AI Sales Agent MVP. Throughout my career, I have successfully worked on projects similar to yours, not only building websites but integrating the power of AI in various functionalities. I have hands-on experience with LLM APIs and AI agent/tool calling which will play a critical role in creating a high-performing AI sales assistant for your home-service businesses. Your project's workflow of automatically capturing leads through webhooks, having AI-powered conversations with leads via SMS, qualifying them, following up diligently, scheduling appointments efficiently while keeping intact all the necessary data and logs is a significant competitive advantage in converting leads. Being skilled in SaaS architecture, multi-tenant applications and authentication/authorization along with REST API usage gives me an edge to handle your project effectively. Additionally, I've worked extensively on CRM automations for various industries including call centers that further support my capability to deliver over expectation.

@Champian

For AI qualification, do you have defined criteria per trade already, since what makes a lead qualified for HVAC differs from roofing, and that shapes whether the agent uses one adaptive prompt or per-industry configurations? We would build this in React.js with Node.js and MongoDB, using LangChain with tool calling for the agent layer, Twilio for SMS and Stripe for subscription billing.

@ranjika08

What makes this MVP valuable is whether the AI can actually move a home-service lead from first contact to booked appointment, not just chat back with generic responses. That means the build has to handle lead-source ingestion, tenant separation, SMS sequencing, qualification paths, calendar actions, and owner visibility in one dependable workflow. I’d structure it around a strong backend event flow first, then layer the dashboard and analytics on top. My background fits this kind of product well because it combines SaaS app development with API-heavy automation work—LLM tools, webhook processing, Stripe billing, role-based access, and operational dashboards. Home-service use cases also need practical conversation control, opt-out handling, reminders, and clear lead status updates so the system feels usable on day one. Before pinning down the final scope, I’d want to clarify: - Should each business have its own custom knowledge base/prompts, or will all tenants start from one shared qualification framework? - Are you expecting two-way inbox management for human takeover inside the dashboard in v1? - Do you already have preferred auth/billing flows for tenant signup, trial, and subscription changes?

@ajharul

Hi, I build functional AI agents using LangChain and tool calling for seamless CRM integration. I will develop your multi-tenant MVP to automate lead qualification and scheduling, ensuring high conversion through intelligent SMS follow-ups. Let’s discuss and get started! -AJ

@onkarkulkarni8

Hello, I can help you with "AI SaaS Developer Needed — Build AI Sales Agent MVP for Home-Service Businesses" as per your given project description. We can discuss more in detail during a chat conversation when you are available. I've worked on many PHP projects in recent times. So I am confident on achieving your expected Goals. Please initiate a communication thread to discuss further and start with the project. ⭐ 5.0/5 from a recent client: "A more professional version: “Excellent work! The job was completed within the committed timeline. Great quality, professionalism, and timely delivery. Highly appreciated and recommended.”" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@smaryum

With over 15 years of experience, I bring the robust commitment and comprehensive skills needed for this AI SaaS project. As your AI SaaS Developer, my team and I will deploy our deep knowledge of based web, app development, along with expertise in NextJS/React, Node.js/TypeScript, PostgreSQL, and Twilio/SMS APIs to build a state-of-the-art solution. At the core of our ability lies adeptness in working with ?LM APIs, building tool-calling agents and utilizing prompt engineering to develop rich Revenue Assurance Guides (RAG) including features like lead qualification, multi-tenant functionality and calendar integration with Google/Microsoft. Our experience spans across designing conversational flows and databases for scalable applications that incorporates step-by-step processes incorporating backend logics where they'll answer through automatic lead My technical adeptness combined with my ability to work effectively with data-heavy processes makes me a fantastic choice for this project. Utilizing OpenAI or equivalent modern LLM APIs for conversing with leads coupled with Twilio dadn PostgreSQLI for organizing those conversations are some essential aspects of this potential appliation.

@Hubready

Hello!, I am a US-based senior software engineer who builds AI SaaS products that actually get used, not just demos. Your biggest pain point here is not “adding AI” in the abstract. It’s building an MVP that can reliably capture leads, qualify them, answer objections, book appointments, and hand off cleanly to your sales flow for home-service businesses. I can help you turn that into a focused, production-ready first release. My approach would be: 1. Define the core sales journey and qualification rules. 2. Build the AI sales agent with a clean chat flow, CRM-ready data capture, and handoff logic. 3. Connect backend services, MySQL, APIs, and admin tools so you can manage leads easily. 4. Test with real scenarios, tighten prompts/logic, and launch a stable MVP. I pay close attention to the details that usually get missed, like lead routing, fallback handling, and making sure the AI sounds natural while still driving conversions. That’s what separates a flashy prototype from something businesses will pay for. Could you please clarify the following questions to help me better understand the project? 1. What’s the first home-service niche you want to target, and what is the ideal lead outcome? 2. Do you already have a preferred AI stack, CRM, and hosting setup? 3. Should the MVP support SMS, web chat, email follow-up, or all three from day one? Relevant work includes AI automation tools, SaaS dashboards, lead qualification systems, and CRM-integrated workflow apps for

@Hrolgar

Multi-tenant from day one is the part most of these builds skip until it hurts, so building isolation into the schema before the first agent flow gets written is the right order to do this in. What you're describing is more than a chat layer on top of Twilio: a qualification flow with defined states, tool calls that actually hit the calendar and CRM, and a fallback to a human when the agent isn't sure, is a different thing from a prompt that replies to texts. I'd start with the tenant boundary and the state machine before touching the LLM piece. Every table scoped to business_id from the first migration, Twilio number-to-tenant routing resolved before a message is even queued, and the qualification flow modeled as explicit states (new lead, qualifying, booking, booked, handed off, dead) rather than left for the model to infer from conversation history. Tool calls for checking calendar availability, booking a slot, and updating CRM fields, each with its own confidence check so the agent hands off to a human instead of guessing when a caller's answer doesn't fit the expected shape. Stripe subscription billing keyed to the same tenant boundary, so a business's plan tier gates seat count, message volume, or number of connected calendars. M1: multi-tenant schema, auth, and Twilio number provisioning per business, $8500, 7 days. M2: qualification state machine plus LLM tool-calling agent with human handoff, $10500, 8 days. M3: calendar integration and appointment booking flow, $8000, 6 days. M4: dashboard, conversation history, and basic CRM views, $8500, 7 days. M5: Stripe subscription billing and analytics, $7000, 7 days. Which calendar are you standardizing on for the first version, Google Calendar, or does each business owner bring their own provider?

@temoberishvili

Hey there, I see you’re looking to create an MVP for an AI-powered sales agent tailored for home-service businesses. That’s a fantastic goal, and I’m excited about the potential to streamline sales processes with AI. With over 10 years of experience in full-stack development and a strong background in AI and machine learning, I can build a robust solution that meets your needs. Let’s connect and discuss how we can bring your vision to life!

@rizzikhan175

As a seasoned Full Stack Developer with nearly a decade of experience, I have extensive expertise in the tech stack required for your project. I've worked on numerous applications involving LLM APIs, AI agents, third-party API integrations, SaaS architecture, authentication and authorization, Stripe subscriptions, PostgreSQL, and React/Next.js. I'm well-versed in the nuances of multi-tenant applications and cloud deployments on platforms like AWS and Vercel. Furthermore, my prior work on real-time systems, e-commerce platforms, payment integrations, call centers, and admin dashboards has given me an astute understanding of CRM functionalities and deepened my grasp on sales automation—essential skills for developing your AI-powered lead management system for home-services businesses. But let's not just talk skillset—I'm someone who genuinely cares about the long-term success of a product. My code is not just clean and well-documented but designed to be scalable enough to adapt over time. I also bring more than just technical abilities to the table; my experience across various industries means I can visualize your app from both a business and end-user perspective. This synergy has consistently produced reliable applications tailored to real-world conditions.

@asadamyn

The hardest part of this build isn't the SMS chatbot, it's making the AI actually take action reliably. Qualifying a lead, working around real calendar availability, following up on silence without sounding robotic, and keeping every business's data cleanly isolated in a multi-tenant system are what separate a working sales assistant from a ChatGPT wrapper with a nice UI. I build AI agents that connect directly to calendars and CRMs, log full conversation history, and book appointments without manual entry, using Twilio for SMS and voice, FastAPI and Node for the backend, and Postgres for storage. I've also shipped multi-tenant SaaS platforms with Stripe subscription billing and per account access control, so tenant isolation and billing here is familiar ground, not something to figure out mid project. My approach would be to nail the qualification and follow up logic for one vertical first, with a clean swappable prompt and knowledge base structure per tenant, then generalize once that flow is actually converting leads. Happy to walk through the architecture and a realistic build sequence whenever you want to talk.

@SkillCrafts

I understand you're looking to develop an AI-powered sales agent MVP for home-service businesses, similar to how AI tools are streamlining customer interactions in other competitive SaaS markets. My experience building automated communication platforms and integrating AI for lead engagement aligns perfectly with this project's goals. For the MVP, I propose a robust backend built with Python and FastAPI for efficient API/webhook handling. We'll leverage a large language model (LLM) like GPT-4 or a fine-tuned open-source alternative for natural SMS conversations and lead qualification, integrating with Twilio for SMS delivery. A PostgreSQL database will manage lead data and conversation history. For appointment scheduling, we can integrate with Calendly's API or a similar service. The development will follow an agile approach, focusing on core functionality first. To ensure alignment, what are your primary KPIs for the MVP's success in lead conversion? Also, have you considered specific criteria for lead qualification beyond basic information gathering? I'm eager to discuss how we can bring this vision to life efficiently.

@cuyodigital

:) Hello, I'm Ricardo from Buenos Aires. :) What you have isn't a simple chatbot project, it's an action-oriented sales automation problem. The AI must qualify leads, follow up, schedule appointments and update the CRM reliably, not just generate replies. I'll build the lead and multi-tenant foundation first, then the agent/tool-calling workflow, SMS and calendar integrations, dashboard and billing, with clear API boundaries for future expansion. Straight answer - if the MVP is treated as a ChatGPT wrapper, I'm not the right fit. The automation needs reliable business rules around the AI. Which SMS workflow should be the first end-to-end scenario we validate? I'll map the core agent workflow before you decide anything. Free, no commitment. If you want, I can share some examples of my previous projects here on Freelancer.com.

@cliftonc20

Hi, Building an AI sales agent MVP for home-service businesses requires precise integration of AI model orchestration with reliable backend data handling to support conversational workflows. The main technical risk is ensuring smooth orchestration between data ingestion, AI inference, and real-time response delivery under concurrent user load. I’ve led the design and implementation of DocIntel AI, a document intelligence platform with FastAPI backend, vector search, and a RAG chatbot, which parallels the AI SaaS and chatbot development needs here. Additionally, my work on Dent-Cloud involved scalable SaaS architecture and AI pipelines that support real-time analytics and prediction services. I typically design systems by separating ingestion, retrieval, orchestration, and response layers to isolate complexity and enable independent scaling and testing. I’d validate early the integration points between the AI agent and existing backend APIs to ensure data consistency and low latency. To improve reliability, I implement grounding checks and fallback routing in the AI response layer to reduce unsupported answers and maintain stable user experiences. I can start by outlining the retrieval pipeline and mapping the agent workflow to clarify system boundaries and dependencies. Thanks, Clifton