AI-Driven Pre-sales Automation Platform

@ZohaibRoy

⭐⭐⭐⭐⭐ Build an AI-Powered Quotation Engine with Python and React ❇️ Hi My Friend, I hope you're doing well. I’ve reviewed your project needs and see you are looking for a developer to create an AI-powered pre-sales automation platform. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects focused on automation and data processing. I will build a robust web application, ensuring efficient file handling, AI integration, and user-friendly design. ➡️ Why Me? I can easily build your quotation engine as I have 5 years of experience in Python FastAPI and React.js development. My skills include PDF and Excel parsing, API integration, and database management. Additionally, I have a strong grip on PostgreSQL and cloud deployment, ensuring a seamless project execution. ➡️ Let's have a quick chat to discuss your project in detail. I’d love to show you samples of my previous work and how I can add value to your project. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ Python FastAPI ✅ React.js ✅ Tailwind CSS ✅ PostgreSQL ✅ PDF Processing ✅ Excel Generation ✅ API Integration ✅ Cloud Deployment ✅ GitHub CI/CD ✅ Data Validation ✅ User Authentication ✅ Project Management Waiting for your response! Best Regards, Zohaib

@diektech

Hi — Elias here from Miami. I see you’re developing an AI-driven pre-sales automation platform to streamline manual processes. This is a significant step towards enhancing efficiency. The real challenge often lies in ensuring the system is scalable while managing complex workflows and integrations. What usually matters most here is balancing automation with human oversight, especially in pre-sales activities. The tricky part is usually integrating various data sources to ensure the AI can learn effectively. My approach would involve creating a modular architecture, focusing on clear separation of concerns. This enhances maintainability and allows for easier updates or feature additions in the future. I'd prioritize building a robust backend with PostgreSQL while ensuring the frontend in React.js offers a seamless user experience. I've worked on similar AI-driven projects, successfully implementing automation features that improved operational efficiency without compromising user control. A few questions to better understand the scope: Q1 – What specific manual tasks are you looking to automate first? Q2 – How do you envision the AI's learning process? Will it require human validation? Q3 – What are your expectations regarding user roles and access permissions? Happy to discuss the details and suggest the best technical approach. Looking forward to hearing from you.

@opencollar

Hi, I understand you need to automate the creation of detailed quotations quickly, eliminating manual effort, and ensuring accuracy. I saw your focus on parsing PDFs and Excel sheets for device info, which reminds me how important it is to extract data correctly for seamless downstream processing. For your project, I can develop a robust backend that handles file uploads, extracts data from PDFs and Excel files, and uses AI to match parts accurately. I will also build the frontend with React and Tailwind to make the interface simple for engineers; they can upload files, review AI suggestions, and override details before generating the final quote. The database will store all parameters and project info, making future edits easier. Clear communication and quality are my priorities to save your time and support your growth. After delivery, I’ll stay available for support and adjustments if needed. Let’s discuss further to plan a powerful system that can grow with your needs and deliver big results. Regards, Nick.

@skalogir

Hi, I understand you need Phase 1 Quotation Engine: FastAPI backend, React/Tailwind frontend, Supabase Auth/PostgreSQL/Storage, Claude-powered BOQ mapping/validation, pricing logic, and exact-format Excel quotation generation. I have experience building document-processing web apps with PDF/Excel parsing, structured output generation, AI API integration, PostgreSQL workflows, CI/CD, and Vercel/Railway-style deployments. I can start within 1 week, follow your 12-week roadmap, share relevant PDF/Excel automation samples privately, and deliver clean code, testing against pilot projects, deployment, and handover documentation. Q1: Is your Excel quotation template already finalized with formulas and styling? Q2: Should PDF BOQs support scanned files, or text-based PDFs only? Q3: What fixed-price budget range have you allocated for Phase 1? Best regards.

@Mahihassi

With a strong background in AI development and machine learning, I am confident in my ability to spearhead your pre-sales automation platform project. From your precise project requirements to the need to integrate the specific tools and technologies, I'm not only well-versed but also experienced in them all. For instance, I have created scalable backend systems using Python FastAPI, often integrating with cloud services such as Supabase. Moreover, I understand that this platform should be capable of extracting and processing vast quantities of data from diverse file types (Excel and PDF) - an area where I excel. I am adept with libraries like pdfplumber, openpyxl, and python-docx which will come in handy for parsing and generating files within the platform.I also have experience with robust database systems like PostgreSQL and cloud deployment platforms such as Vercel and Railway. Beyond technical versatility, I am a proactive communicator who is accustomed to regular check-ins and prompt decision-making; guaranteeing a smooth collaboration even though we may not be physically close. Given my track record in delivering reliable products on time, your goal of building this pre-sales automation platform can be achieved efficiently and on schedule.

@Herculesz

Hi I can build the Phase 1 Quotation Engine using Python FastAPI, React, Tailwind CSS, Supabase PostgreSQL/Auth/Storage, Claude API, pdfplumber, openpyxl, and a clean GitHub CI/CD workflow. The main technical challenge is converting inconsistent RFQ Excel/PDF BOQs into accurate mapped part numbers, validated quantities, pricing calculations, and a company-formatted Excel output without requiring heavy manual correction. My solution would be to create a structured pipeline for upload, parsing, AI-assisted device mapping, pricing formula execution, engineer review, validation flags, and final workbook generation. I have experience with document processing, AI extraction, structured data mapping, Excel automation, and data-heavy web applications where accuracy and traceability are critical. The backend would keep parser logic, Claude mapping, pricing rules, validation, and Excel generation separated so each part can be tested and improved independently. The frontend would give engineers a clear project form, upload flow, editable BOQ preview, AI flags panel, project history, and Generate & Download workflow. I would also keep the system deployment-ready on Vercel, Railway, Supabase, and GitHub with clear handover documentation. Thanks, Hercules

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@mahig47

Hello! As per your project post, you are looking to build an AI driven pre sales automation platform phase 1 focused on transforming client RFQs and BOQs into fully priced and validated quotations. The goal is to automate the engineering quotation workflow by extracting data from Excel and PDF files, intelligently mapping devices to part numbers, validating scope completeness, applying pricing rules, and generating company formatted quotation workbooks within a streamlined web application. My focus will be on developing a robust quotation engine consisting of a FastAPI backend, React.js frontend, Supabase powered infrastructure, and AI integrations. The solution will support BOQ and specification parsing, Claude powered device mapping and validation, configurable pricing calculations, engineer review workflows, Excel quotation generation with multiple sheets, project history management. I specialize in AI powered business platforms, workflow automation systems, document processing solutions, FastAPI, React, PostgreSQL, and cloud native architectures. My approach focuses on building reliable processing pipelines, transparent AI assisted workflows, maintainable codebases, and enterprise ready infrastructure. Let’s connect to discuss your quotation formats, catalog structure, AI mapping expectations, and future phase roadmap to ensure Phase 1 establishes a strong foundation for the complete platform. Best regards, Nikita Gupta.

@corpmember29

Hello, I have carefully reviewed the Phase 1 requirements for your AI-powered Quotation Engine and understand the complete workflow from RFQ/BOQ upload to AI-assisted device mapping, pricing calculations, validation, and automated Excel quotation generation. With 10+ years of experience in Python, FastAPI, React.js, PostgreSQL, Supabase, AI integrations, and document processing systems, I can build a scalable production-ready solution aligned with your 12-week roadmap. My approach: • FastAPI backend with modular architecture for BOQ parsing, AI mapping, pricing engine, validation, and Excel generation • React + Tailwind frontend with engineer-friendly review and override workflows • Claude API integration for intelligent device mapping and BOQ validation • Supabase Auth, PostgreSQL, and Storage for secure project management • Openpyxl-based Excel generation matching your exact quotation format • CI/CD via GitHub, deployment on Railway & Vercel, and complete documentation I have experience building AI-powered document processing, PDF/Excel automation, quotation systems, and enterprise workflow applications involving OpenAI/Claude integrations and structured output generation. I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT, COMPLETE SOURCE CODE, FULL DOCUMENTATION, AND ASSISTANCE FROM DEVELOPMENT THROUGH DEPLOYMENT AND HANDOVER. Thanks

@ShaikhAneesa

Hi there, I understand you are building an AI-powered pre-sales automation platform to eliminate manual engineering effort by converting client RFQs (Excel/PDF BOQs) into fully priced, validated, and formatted quotations across 15 ELV disciplines, with Phase 1 focused on delivering a complete quotation engine. My approach will be to build a full-stack system where engineers upload BOQ files through a React + Tailwind interface, and a FastAPI backend processes Excel and PDF inputs using openpyxl and pdfplumber. I will implement an AI mapping layer using Anthropic Claude to match BOQ items to your internal part-number catalog with consistent standardisation. A pricing engine will calculate costs using database-driven rules (list price, multipliers, exchange rates, margins), ensuring all logic is centralised and editable. A validation layer will use Claude to flag missing items, quantity issues, and scope gaps before final output. The system will then generate a structured 3-sheet Excel file (Front Page, Material BOQ, Analysis Sheet) in your exact format. The platform will use Supabase (PostgreSQL, Auth, Storage) for users, projects, and pricing data, with deployment on Vercel (frontend) and Railway (backend) via GitHub CI/CD. Do you already have your part-number catalog and pricing rules structured, or should I design the database schema from scratch? I’m ready to start immediately. Warm Regards, Aneesa.

@sunainawbst

✅ Lovable AI Expert | AI Development | Game Development ✅ Hi, Thank you for considering this opportunity! I bring extensive experience in implementing custom solutions powered by LLMs, conversational AI, and intelligent automation. Recently I have been working on Lovable AI for developing a gaming platform using it, complete with chat-based agent logic, expressive front-ends, and backend integrations. In other project, implemented a fully automated AI agent system for intelligent meeting creation using ElevenLabs Conversational AI and Gemini (via a custom agent brain). The flow integrates voice interaction, natural language processing, location precision, and frontend. Whether you're building an internal assistant, a public-facing voice agent, or an integrated AI productivity tool, I can help bring your vision to life with robust, scalable architecture and a human-like user experience. I would love to connect and explore how we can contribute to your AI initiative. Regards Ashraf

@devendrathakur12

Hello, I built many large scale AI document processing and quotation automation platforms before and I would love if I get the chance to work on your project. I can deliver this using Python FastAPI, React.js, Tailwind CSS, Supabase, PostgreSQL, openpyxl, pdfplumber and Anthropic Claude API with a clean and maintainable architecture that supports future phases. One important question is how should conflicting values between the client BOQ and specification PDF be resolved before pricing when both sources disagree? Can we connect over a chat to discuss more about the project? Best regards, Dev Singh

@codefusion53

Hi I can build Phase 1 of your AI-powered quotation engine using Python FastAPI, React, Tailwind, Supabase, PostgreSQL, Claude API, pdfplumber, openpyxl, Vercel, Railway, and GitHub CI/CD. This is exactly the type of data-heavy document automation platform I work well with. I can handle the full workflow: Excel/PDF BOQ upload, BOQ parsing, specification extraction, Claude-powered device-to-part-number mapping, pricing formula logic, BOQ validation, engineer review/override screen, and final 3-sheet Excel quotation generation in your company format. I have experience with FastAPI backends, React frontends, Supabase Auth/Storage/PostgreSQL, AI API integrations using Claude/OpenAI, Excel automation with openpyxl, PDF parsing, structured data extraction, pricing logic, and deployment pipelines. I would follow your 12-week schedule, starting with schema and infrastructure, then backend services, frontend review screens, AI validation, Excel output generation, pilot testing, deployment, and documentation. I can start within 1 week and commit to the full 12-week Phase 1 duration. I can share relevant document-processing and AI automation examples here: [portfolio/project link]. For fixed price, I would confirm the final bid after reviewing the charter, Excel template, sample BOQs, and five pilot projects so the scope is priced accurately. Best, Justin

@taimoorkhan311

I have thoroughly reviewed the project requirements and am confident in my ability to deliver a high-quality solution. With expertise in Python, Excel, PDF processing, Machine Learning, PostgreSQL, React.js, and web development, I am well-equipped to handle the complexities of this AI-driven pre-sales automation platform. I am keen to discuss the project further in chat to understand your specific needs and provide a tailored approach. Looking forward to the opportunity to collaborate on this exciting project. Regards, Taimoor from Pixels Soft.

@topcreator100

Hello I'm excited to apply for this AI-driven pre-sales project and I've got hands-on Python and ML experience to back it up. I will design a robust backend with Python FastAPI and a responsive frontend using React.js, while ensuring the solution supports PDF/Excel parsing and automated quoting. I’ve worked on similar data heavy docs and document processing, weaving Python, JavaScript, and CSS to deliver a polished, user friendly interface. Next steps: I can start within a week, outline milestones in 72 hours, and lock in a fixed price bid after a quick 30 minutes clarification call;

@extreamcode

Hi, This is exactly the kind of workflow where hidden friction kills engineer time: the BOQ arrives messy, the mapping is manual, and the quote needs to be consistent every time. I can build the Phase 1 quotation engine so your team uploads Excel/PDF RFQs, reviews AI-mapped items, overrides quantities if needed, and generates a branded 3-sheet Excel output with the right formulas and analysis. I’ve built web apps around Python FastAPI, React.js, Tailwind CSS, PostgreSQL/Supabase, and document automation using openpyxl and pdfplumber. I’ll structure the FastAPI backend for parsing, Claude-based device mapping, pricing calculations, validation, and secure file storage, while keeping the React UI clean for fast engineer review. 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 start within 1 week and would deliver this in milestones across the 12-week scope so Phase 1 stays controlled and testable. Would you like the first milestone to prioritize BOQ parsing accuracy, or the exact Excel output format and formulas? Best regards, Asad

@sabaehsan

Greetings, I'm excited about your project to create an AI-driven pre-sales automation platform. It sounds like you're aiming to streamline the quotation process for your engineering team by automating the workload associated with RFQs. I can help build the backend using Python FastAPI for file uploads, parsing, and pricing calculations, while ensuring seamless integration with the Claude API for device mapping and validation. My experience includes working with similar technologies, particularly in developing web applications that involve PDF and Excel processing. I understand the importance of accurate data extraction and structured output, and I'm confident in delivering a solution tailored to your needs. I’m ready to commit to the full 12-week timeline and collaborate closely to achieve your project goals. Best regards, Saba Ehsan

@quan9824

Hi, I can build the Phase 1 Quotation Engine as a complete FastAPI + React application using your required stack and 12-week roadmap. I have experience with Python FastAPI, React/Tailwind, Supabase, PostgreSQL, file upload workflows, PDF/Excel parsing, openpyxl-based Excel generation, and AI API integrations using Claude/OpenAI. I can develop the BOQ parser, specification parser, Claude-powered device mapper, pricing engine, BOQ validator, and final 3-sheet Excel quotation generator based on your exact company template. My approach would be to start with database schema, Supabase Auth/Storage, GitHub repo, CI/CD, and deployment environments, then build the backend modules in a clean, testable structure. In parallel, I would implement the React frontend for project creation, uploads, mapped BOQ preview, editable pricing/quantity overrides, AI flags, project history, and final download. I can also load your product catalog and pricing parameters into Supabase, test against the 5 pilot projects, and refine parsing/mapping accuracy before final handover. I’m available for the full 12-week Phase 1 schedule and can start within one week. I’d be happy to review your project charter, Excel template, pilot BOQs, and pricing data to provide a fixed-price bid and confirm milestones.

@vishal1145

Your BOQ parser will fail if the client uploads a scanned PDF instead of a digital one — pdfplumber can't extract text from images. This happens in 30% of RFQs in the ELV industry. You'll need OCR fallback (Tesseract or AWS Textract) or the system will return blank data and your engineers will lose trust in the tool. Before architecting the solution, I need clarity on two things: What's your expected Claude API token usage per BOQ? A 50-line Excel file might use 10K tokens, but a 500-line PDF specification could hit 100K+ tokens per request, which affects cost modeling and rate limit handling. Second, do your pricing formulas include conditional logic (e.g., discount tiers based on quantity brackets, or vendor-specific markup rules)? If yes, we need a rules engine, not just static multipliers in the database. Here's the architectural approach: - PYTHON FASTAPI + CELERY: Implement async task queue for BOQ processing so engineers don't wait on a loading screen — they upload, get a task ID, and receive a notification when the quotation is ready (handles 2-hour processing window without timeout). - PDFPLUMBER + PYTESSERACT: Build a hybrid parser that detects if PDF is text-based or scanned, then routes to OCR pipeline automatically — prevents silent failures on image-based RFQs. - ANTHROPIC CLAUDE API: Structure prompts with few-shot examples of your part number catalog to improve mapping accuracy, implement exponential backoff for rate limits, and log all API calls with input/output for debugging when engineers flag incorrect mappings. - POSTGRESQL + SUPABASE: Design schema with versioned price lists (effective_date column) so historical quotations remain accurate even after price updates, and use row-level security policies to isolate projects by user role. - OPENPYXL + TEMPLATE ENGINE: Pre-build Excel template with formulas as strings, then inject data into specific cells — this preserves your company formatting and prevents formula corruption during generation. - REACT + TAILWIND: Build the BOQ preview table with inline editing (react-table library) so engineers can override AI mappings before final generation, reducing post-generation rework by 60%. I've built 3 document automation platforms for construction and procurement teams, including one that processes 200+ RFQs per month with Claude API integration. I don't take on projects where the AI accuracy expectations are unrealistic — let's discuss your acceptable error rate and manual override workflow before committing to the 12-week timeline.