Parametric CAD/drafting portal with an AI assistant

@rizwange4

Hi, I can build this AI technical-report drafting engine as an extensible MVP, with Python + FastAPI + LLM API + RAG/vector database architecture. The MVP can include: Raw data + natural-language prompt → structured technical report Engineering/scientific writing style enforcement Reusable report templates Internal guidelines via RAG Sections for introduction, methodology, results and conclusions Paragraph-level accept/reject/edit feedback RESTful JSON APIs with clear documentation Secure model/API configuration Modular architecture for future GPT/Claude/open-source model support Demo showing data in → publication-ready report out Setup/deployment documentation I’d start with an LLM API for the MVP to achieve the fastest reliable results, while keeping the architecture flexible enough for a fine-tuned/open-source model later.

@sahildogra222

Hey, You are looking for Parametric CAD/drafting portal with an AI assistant "Lets Gets Started, I know you have several tempting proposals here, but I guarantee you to be impressed by my work. I have various skills in design, Illustration, Photoshop, Graphic Design, Logo Design and Illustrator. If you give me this chance you will be impressed, because I guarantee that I will meet your expectations. I invite you to get a look at my portfolio You Can see it from here : https://www.freelancer.com/u/sahildogra222 If you have any questions or queries, do not hesitate to contact me. I hope to start working with you. With regards! SAHIL

@starttechno

This MVP will turn structured raw data and short prompts into coherent engineering reports with introduction, methodology, results, and conclusions, while applying your internal scientific/engineering style guidelines. I’ll build the orchestration in Python with FastAPI, using GPT-4o for drafting, PostgreSQL with pgvector for guideline retrieval, and Jinja-based versioned templates that can be swapped without code changes. The REST layer will provide clean JSON input/output, OpenAPI documentation, validation, and endpoints for generating reports and managing templates. Each paragraph will carry its own accept, reject, and edit action, with feedback stored for prompt improvement and future fine-tuning or model evaluation. I’ll support common inputs such as JSON/CSV, add safeguards against unsupported claims, and structure the services so multilingual output, citation management, and data-lake connectors can be added later. Delivery includes a working “data in, report out” demo, Docker setup, concise local/cloud instructions, sample data, and an implementation ready for portal integration. I can begin immediately and deliver the functional MVP in four days. Are your internal style guidelines already available as PDF, Word, or plain-text files? Muhammad Saad

@sabaehsan

Greetings, I've read your project details, and it sounds like you're looking to create an AI-powered system that can generate technical reports from raw data and prompts automatically. My approach would focus on leveraging natural language generation techniques to ensure the reports meet engineering and scientific writing standards. I’d utilize Python for orchestration and integrate NLP libraries like spaCy and Hugging Face for enhanced text generation and processing. The feedback loop feature is crucial, as it will allow users to refine the output while improving the model over time. I also understand the importance of clean API endpoints for easy integration into your existing web portal. I’m excited about the potential for follow-on features, and I’ll keep extensibility in mind during development. Best regards, Saba Ehsan

@ajharul

Hi, Ajhar here, ▪ What specific technical formats or schemas do your raw data inputs currently follow? ▪ Should the feedback loop store edits in a vector database for immediate RAG refinement? I will build your MVP using Python, LangChain, and GPT-4 to generate reports via RESTful endpoints. This modular engine ensures seamless integration, template flexibility, and iterative improvement for your drafting needs while prioritizing extensible architecture for future scale. Let’s discuss and get started! -Ajhar

@asifalimzd

Hi, I can build your AI-driven technical report drafting MVP using Python, LLM APIs, and a scalable RAG architecture where needed. The system can transform raw data and prompts into structured, publication-ready sections while following internal style guidelines, supporting flexible templates, paragraph-level feedback, and clean REST APIs with documented JSON input/output. I’ll focus on getting a functional “data in, report out” MVP delivered quickly while keeping the architecture extensible for future multilingual support, citations, and data-lake integration. Do you already have internal style guidelines and sample technical reports available to use as reference material? Kindly send me a message to discuss more or directly award me. Thank you!

@seefattechnologi

Certainly-------------Checked reference website-I believe I will satisfied you by completing this task with high Quality and Accuracy I am highly proficient to work on this project . I am an PYTHON/Full stack developer having rich experience with all the latest technologies with so many successful Tasks. I have some queries to give you accurate time and price Please ping me to get started and provide you great results. Thanks!!!

@ShaikhAneesa

Hi there, I understand you need an AI-driven drafting engine that converts raw data and short prompts into structured, publication-ready technical reports, with reusable templates, internal style guidance and paragraph-level feedback built into the workflow. I’m confident I can build the MVP as a modular foundation that can later support citations, multilingual output and internal data sources. My approach is to first define the report schema, engineering/scientific writing rules, template structure and input/output requirements. Next, I’ll build the Python orchestration layer with an LLM, structured prompting and RAG/vector retrieval where needed to ground outputs in your internal guidelines. Then, I’ll implement template management and the accept/reject/edit feedback loop so paragraph-level revisions can be captured for future improvement. Finally, I’ll expose the drafting engine through clean REST endpoints, document the JSON contracts and demonstrate the complete data → draft → report workflow. I’ll keep the architecture extensible so the MVP can evolve without rebuilding the core generation pipeline. For the first release, do you already have internal style guides/templates and sample technical reports that should be used as the grounding material? I’m ready to start immediately. Warm Regards, Aneesa.

@mohjib092

I am python ninja my price to match your description and project base , I’ll build a scalable AI drafting engine that turns raw data into publication-ready technical reports using LLMs, RAG, templates, and a feedback-driven refinement loop. With a Python REST API, clean JSON documentation, and a fast data in report out MVP demo, I’ll deliver a production-ready foundation designed for future integrations and multilingual expansion.

@denisSolution

Hi, I'm Denis, a developer with experience in building systems where structured content generation and API-driven automation were key requirements. You need a drafting engine that transforms raw data and prompts into consistent, publication-ready technical reports automatically. The system must enforce style guidelines, generate coherent sections, manage templates, and allow user feedback to refine outputs. It should also expose clean REST endpoints for integration into your portal. I’d design a modular pipeline: an input handler, a style enforcer using retrieval-augmented generation with your guidelines as context, and a template renderer. A feedback interface would capture user edits to improve the model over time. The API would provide endpoints for submitting inputs and retrieving reports in JSON, with clear documentation. For the technical backbone, I’d use Python with spaCy, LangChain, and a vector database like Chroma or Pinecone for storing style guidelines and examples. The LLM choice would depend on cost and quality trade-offs; GPT-4 for the MVP could provide reliable outputs quickly, while a fine-tuned open-source model might be more cost-effective for scaling. Two risks stand out: unclear style guidelines could lead to inconsistent outputs, requiring a review phase. Also, LLM variability might need fallback logic or human-in-the-loop validation. I can start working right away. Let’s connect to discuss the details. Thanks, Denis

@ImranAli01

Hello, I got that you are looking for an AI-driven drafting engine to automatically generate well-structured technical reports from raw data and prompts. This is what I can help you with, let's chat. My approach is to utilize large-language-model APIs like GPT-4, integrated with Python for orchestration. Leveraging NLP libraries such as spaCy and Hugging Face, I will ensure natural-language generation adheres to engineering and scientific writing standards. The system will feature template management for easy layout changes, a feedback loop for user edits, and RESTful endpoints for seamless integration into your web portal. As final deliverables, you will receive a fully functional MVP with a demo showcasing the "data in, report out" process, along with a setup guide for easy replication. The system will be built with extensibility in mind for future milestones like multi-language support and advanced citation management. One thing I'd like to confirm before we start: Are there any specific domain data sources you would like the system to be pre-trained on? Looking forward to discussing this project further. Best Regards, Imran

@seospider

Hello, Your MVP can turn raw data into structured technical reports using RAG + style templates, with paragraph-level feedback saved to improve future drafts. I’ve built AI SaaS platforms using Python/FastAPI, LLM APIs, vector search, multi-tenant knowledge systems, and configurable AI workflows. I’d structure this with clean REST APIs, reusable report templates, retrieval from internal guidelines, and traceable feedback history so later citation/data-lake features remain easy to add. Do you already have sample reports/style guides? Should user edits improve only that client’s future reports or the global model behavior? Best Regards Hasan

@hirenksanghvi

Hello, Best regards from Syndell! Your AI-driven drafting engine that transforms raw data and prompts into structured, publication-ready technical reports is exactly what we build. We'd architect this with modular LLM orchestration, robust template management, and a feedback loop that learns from user edits. Our team has deep experience integrating LLM APIs with REST services and enforcing domain-specific writing standards in generated content. Would the feedback mechanism retrain a custom model or refine prompts dynamically within a hosted LLM service? We are 5-star reviewed on Clutch and GoodFirms, and among the top-rated agencies for our services on Freelancer.com. We would be happy to answer any questions, and once we are engaged we will quickly introduce you to our extensive portfolio. Looking forward to collaborating. Thanks! PS: The final time and cost may be subject to change based on our discussion.

@krupnovdenis

Hello! We can build a solution for this task. 1. Do you already have the style guidelines and sample inputs for the first release? 2. Which part should we prioritize first: generation, templates, or review workflow? — 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!

@Zaiyan

I can develop the AI-driven drafting engine to generate structured technical reports from raw data and prompts. My first step will be to outline the architecture, confirming the choice of LLM API and necessary NLP libraries for the initial build. Based in Toronto, I deliver promptly and stay responsive throughout the project.

@naminghn

Hi there, I understand the urgency of creating an AI-driven drafting engine for technical reports. Leveraging NLP libraries like spaCy and supported by Python, I can build a system that transforms user inputs into coherent sections as specified, while incorporating a feedback loop for future improvements. Furthermore, I'll ensure the RESTful API endpoints are well-documented for easy integration into your existing portal, allowing for smooth data transitions. Your satisfaction is my priority and I guarantee that I will deliver you a high-quality result. Regards, Ali

@topcreator100

Hello, As a highly-seasoned full-stack developer with extensive experience in Python and Natural Language Processing (NLP), I fully understand the intricate demands of your project. Adapting to your preference, I can use Python as an orchestrating tool while also leveraging the power of renowned libraries such as spaCy, LangChain and Hugging Face to bring your AI-driven drafting engine to life. I am also well-versed in working with RESTful APIs and JSON for seamless integration into your existing web portal interface. In addition to that, I am comfortable implementing vector databases for retrieval-augmented generation - a skill paramount to ensure your AI model is fully equipped for effective data conversion. Be it using large-language model APIs or open-source fine-tuned LLMs, I will transparently walk you through the stack. Given my extensive knowledge in writing robust, scalable code and my ability to deliver on-time, I guarantee that you will have an MVP in record time. Moreover, my industry insight motivated by a background in Computer Science will ensure subsequents milestones like multi-language support, advanced citation management, etc., are positively realized for optimum extensibility. With me onboard, take confidence knowing that high-speed and impeccable UX are my watchwords. So let's collaborate on propelling your vision to fruition! Thanks!

@temoberishvili

Hey! I see you're looking to create an AI-driven drafting engine that can transform raw data into polished technical reports. That’s a fantastic project, and I’m excited about the potential it has to streamline the drafting process. With over 10 years of experience in both backend development and AI integration, I have the skills in Python and Natural Language Processing to bring your vision to life. I’ve worked on similar projects that required building robust engines to handle complex data and generate coherent outputs. Let’s connect and discuss how we can make this happen. Looking forward to it!

@FizzaNadeemK

Hi, The important part here is making the report engine grounded in your internal guidelines and templates, rather than just generating generic LLM text. I’d use Python + FastAPI with an LLM API, structured templates, and RAG via pgvector so each section is generated against the relevant engineering standards and source material. For the MVP: 2 days for generation/RAG + template management, 2 days for paragraph-level feedback and REST endpoints, and 1–2 days for testing, documentation, deployment and the “data in → report out” demo. Estimated total: 5–7 days. I’d keep the feedback data structured from day one so accepted, rejected and edited paragraphs can later support model improvement without rebuilding the system. The API and data layer would also be designed for the planned multilingual, citation and data-lake milestones. Do you already have the internal style guides, report templates, and sample reports ready for the initial RAG setup?