Enterprise Email Classifier API

@nrajuu07

I'm an NLP engineer experienced in text classification, transformer fine-tuning, and production ML deployment. I'll build an end-to-end email routing system — data preparation with PII stripping and consistent labelling for HR, Finance, and IT Support, baseline TF-IDF + SVM models, fine-tuned BERT/RoBERTa via Hugging Face with rigorous evaluation (precision, recall, F1 per class), and comprehensive error analysis identifying common misclassifications. The best model gets packaged behind a FastAPI service, containerised with Docker for any Linux environment, and delivers predictions under 500ms per email on CPU. Deliverables include cleaned/labelled dataset, reproducible Jupyter notebooks for both baselines and transformers, evaluation report with metrics, production-ready Dockerfile, and a clear README enabling fresh setup and retraining in under 30 minutes — all in a private Git repo. Target F1 ≥ 0.90 across all three classes on held-out test split. Four-week timeline. Ready to share relevant NLP and FastAPI projects and start immediately.

@vishal1145

Your acceptance threshold of 0.90 F1 across three classes is aggressive - most enterprise email classifiers plateau around 0.85 because HR/Finance overlap heavily on payroll queries. If your training set has fewer than 2,000 labeled examples per class, you'll hit a ceiling before reaching 0.90. I've seen this exact pattern in 4 production deployments. Before architecting the pipeline, two questions: What's your current label distribution across HR/Finance/IT? If one class represents 60% of emails, we'll need stratified sampling and class weighting. Second - are you planning to retrain weekly or monthly? That determines whether we build a full MLOps pipeline with DVC versioning or keep it lightweight with shell scripts. Here's the technical approach: - NLP PREPROCESSING: Build a spaCy pipeline that strips PII (emails, phone numbers, employee IDs) using regex + named entity recognition, then tokenize with subword handling for domain-specific jargon like "PTO request" or "VPN access." - BASELINE + TRANSFORMER: Train scikit-learn TF-IDF + LinearSVC as the speed benchmark, then fine-tune distilbert-base-uncased (40% faster than BERT, 95% of the accuracy). I'll log experiments in MLflow so you can compare precision/recall curves across 8+ model variants. - FASTAPI + DOCKER: Serve the model with ONNX Runtime for 3x inference speedup, implement request batching to hit your 500ms target on CPU, and add Prometheus metrics for tracking prediction confidence drift over time. - RETRAINING PIPELINE: Package the entire workflow - data validation, model training, evaluation - into a single Python script with argparse flags so your team can retrain without touching Jupyter notebooks. I've built 3 similar text classifiers for fintech compliance teams, including one that routes 40K support tickets daily with 0.88 F1. The deployment used the exact stack you specified - FastAPI, Docker, Hugging Face transformers. Let's schedule a 15-minute call to review your label quality before I commit to the 0.90 threshold.

@extreamcode

Hi, Your brief shows the real risk isn’t just classification accuracy , it’s getting clean, repeatable routing that survives new mail and deployment. I’ve built text-classification pipelines with TF-IDF/SVM baselines, Hugging Face transformers, FastAPI services, and Docker delivery, so I can take this from anonymised data through evaluation and a production-ready /predict endpoint. I’ll structure the work so the dataset is cleaned and relabelled consistently, then compare the baseline against BERT/RoBERTa with precision, recall, F1, and error analysis for each department. 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. If the data is already reasonably balanced, I can keep the first pass tight and use the results to guide retraining and deployment. Looking forward to your reply so we can finalize the exact plan. Do you already have a labelled training set for HR, Finance, and IT Support, or should I build the initial labelling pipeline from scratch? Best regards, Asad

@zeeshanmcp12

I understand you need an Enterprise Email Classifier API developed, starting from data preparation to deploying a Docker-ised REST service, using TF-IDF + SVM and transformer models like BERT or RoBERTa via Hugging Face for classification. I will ensure a precise and efficient workflow, delivering a production-ready FastAPI service within the specified criteria. Thank you.

@lalitdeveloper7

Hi I can build the complete email classification pipeline from data preparation through deployment, including PII-safe preprocessing, TF-IDF + SVM baselines, transformer-based models (BERT/RoBERTa), model comparison, and detailed error analysis. The final solution will include a Dockerized FastAPI service, reproducible training notebooks, evaluation reports, and clear documentation for retraining and deployment, with a focus on achieving the required F1-score and low-latency inference. Please let me know further. Thanks.

@sjohn117

Hi, I'm Sean, an AI & Full-Stack Developer with over 10 years of experience in building scalable systems, including advanced NLP solutions. Your need for an Enterprise Email Classifier API aligns perfectly with my expertise in developing and deploying intelligent applications. I've successfully delivered similar projects where I leveraged Docker, FastAPI, and NLP techniques, ensuring robust and efficient processing. I will prepare the dataset, model the data using TF-IDF and transformer models like BERT or RoBERTa, and document the entire pipeline for ease of use. My approach guarantees clean, maintainable code while emphasizing thorough documentation and performance metrics. I can deliver the first milestone in just one week, ensuring we stay on track for your four-week timeline. Could you share your preferred method for evaluating model performance? Thanks, Sean

@KrisuTechkul

Hi, Krishna here from Delhi - we are a team of 20+ engineers and have completed more than 300 projects with 4.7 rating. We have gone through your project requirements and would like to schedule a discussion call please. With over a decade of experience in crafting advanced, tailor-made AI solutions that tackle complex business challenges, my team at Krishna Kant can exceed your expectations with this enterprise email classification project. We have a proven track record in delivering efficient machine learning solutions, which includes leveraging transformer models like BERT and RoBERTa (via Hugging Face) similar to your project's scope. Stripping off Personally Identifiable Information (PII), applying consistent labeling, and creating a pipeline for fresh mail processing are all familiar tasks that we inculcate into our ML workflows. Our expertise doesn't just include successful modeling but extends to robust deployment as well. Transforming the best-performing models into a Docker-ised REST service with incredible speed, under CPU load is something we've mastered over time. We are skilled users of FastAPI and proficient in handling NLP-based classification tasks; one of our recent projects involved building an efficient sentiment analysis framework using similar technologies.

@manoval09

Hi, I notice your brief cuts off at "routed automatically to the correct"—I'll need clarity on target mailboxes/categories and your anonymised dataset size, but the technical path is straightforward. Email classification at scale needs both accuracy and speed; I've built this pattern before and know where budget constraints typically bite. My approach: scikit-learn or a fine-tuned transformer (depending on your email volume and accuracy threshold) for the classifier, PostgreSQL for metadata/routing rules, FastAPI for the REST layer, and deployment to AWS Lambda or GCP Cloud Functions to keep infrastructure costs minimal. I'll focus on getting the classification logic right first, then optimise for throughput. First 24 hours: I'll parse your dataset, analyse feature distributions across categories, and share a classification report showing baseline accuracy. This tells you whether the job is a two-week sprint or requires a bigger model. What's your target accuracy level, and roughly how many emails per day need routing? Best regards, Val

@andreww344

As an experienced Full-Stack Shopify Developer and skilled engineer in FastAPI, Python, and Java, I have the requisite expertise to successfully deliver your enterprise email classifier API. Although my primary focus has been on e-commerce solutions, my proficient understanding of databases (including PostgreSQL and MongoDB), API integrations (such as Shopify's REST and GraphQL endpoints), utilizing custom Shopify Apps, and frameworks like Laravel, Express, and FastAPI makes me uniquely qualified for this project. Moreover, I'm well-equipped to provide you with a comprehensive documentation package detailing the complete pipeline - from environment setup and model training to deployment and retraining- to make sure it can be rerun independently when fresh mail arrives. My commitment to efficient project management ensures that your set acceptance criteria are achieved within your desired timeline. Besides meeting your technical needs with proficiency, I'll bring a deep-rooted work ethic and excellent communication skills to guarantee smooth collaboration throughout the process.

@rishis0193

I am an experienced Full Stack Developer with 8+ years of professional experience in designing, developing, and maintaining scalable web and mobile applications. My technical expertise includes: ✅ Backend Development: PHP, Laravel, Python, Node.js ✅ Frontend Development: React.js, JavaScript, HTML5, CSS3, Bootstrap ✅ Mobile App Development: Flutter (Android & iOS) ✅ Databases: MySQL, MongoDB ✅ API Development & Integration: REST APIs, Third-Party APIs, Payment Gateways ✅ Cloud & Deployment: VPS, Linux Servers, AWS, DigitalOcean, cPanel Why choose me? * 8+ years of hands-on development experience * Clean, maintainable, and scalable code * Strong problem-solving and debugging skills * Regular communication and project updates * On-time delivery with high-quality standards * Long-term support after project completion

@Ingjuanguzmane

Hello, I can help with Enterprise Email Classifier API. I specialize in automation, API integrations, AI-assisted workflows, backend services, and production-ready web systems. My approach is to first confirm the exact requirements, then deliver a working milestone quickly, with clear progress updates and testable results. For this bid I propose 5 day(s) and a budget of 2000.0. Relevant areas I identified: Java, Python, Linux, Machine Learning (ML), Hadoop, Docker, FastAPI, Natural Language Processing. I can start by reviewing your current assets/specification and sending a concrete implementation plan before the first milestone.

@rakhshanz3

With nearly two decades in AI and ML development, I am confident in delivering results that hit all your criteria. As a former lecturer, I can strip PII competently and implement consistent labelling for HR, Finance, and IT Support departments. For modelling the classifier, I will begin with a TF-IDF + SVM model followed by fine-tuning transformer models like BERT or RoBERTa via Hugging Face to ensure the best accuracy and efficiency. FastAPI being my go-to framework of choice combined with Docker for deployment assures an API backend that is both robust and scalable while yielding predictions on par with your expectations within 500ms per email. My development firm was actively involved in large-scale software and AI solutions where we developed NLP systems similar to yours utilizing FastAPI for a streamlined service. Moreover, my custom AI tools experience can offer you an effective error analysis showcasing common misclassifications for more informed decision-making. Finally, documentation will include a concise setup guide as well as a retraining cheatsheet outlining the steps to refresh labels, retrain and redeploy to ensure your project can be sustained easily throughout time. Overall, my goal is to provide a production-ready solution within four weeks without compromising quality or speed; enabling you to efficiently classify mails while keeping your team’s administrative tasks at minimum.

@sanddep5

As an experienced Full Stack Developer with a knack for transforming innovative ideas into scalable digital products, I am eager to take on your Enterprise Email Classifier API project. My specialization in building applications using MERN Stack, PHP Laravel, Java Spring Boot, and now FastAPI positions me ideally for leveraging those skills in creating your highly performant, fast AI model deployment. In a similar vein, my experience designing and implementing secure SaaS solutions could be of value as we streamline operations with automated email routing. Having worked with templated cluster-architectures, I understand the need for clean, precise labelling -- minimizing all residual PII -- which I'll diligently document for easy future maintenance. Notably, I have previous experience with Hugging Face models and FastAPI that aligns closely with this project's requirements. By building production-ready AI systems including LLM-powered applications and multi-model orchestration systems, I've gained a deep understanding of Python-based NLP technologies often used during text classification tasks - such as the BERT or RoBERTa models erroneously mentioned. Let me build the future-fit solution you're after by delivering clean data sets, comprehensive error analyses, high-F1 models and the fast-serving Docker APIs you seek!

@ayushis151

Hello, I am an AI/ML Engineer with experience in machine learning, NLP, Python, and model deployment. Your project aligns well with my skills, particularly in text classification, transformer-based models, and API development. For this project, I will: • Build a reproducible data preparation pipeline, including PII removal, text preprocessing, and label validation. • Develop and evaluate a TF-IDF + SVM baseline before fine-tuning transformer models such as BERT and RoBERTa using Hugging Face. • Compare models using precision, recall, and F1-score, and provide detailed error analysis highlighting common misclassifications. • Deploy the selected model through a FastAPI REST service and containerize it using Docker for easy Linux deployment. • Deliver complete documentation covering setup, inference, retraining, and redeployment procedures. I follow clean coding practices, maintain well-documented notebooks and scripts, and focus on creating solutions that are both production-ready and easy to maintain. I can begin immediately and work toward meeting the four-week timeline. I would be happy to discuss the dataset size, infrastructure requirements, and any specific deployment preferences before getting started. Looking forward to hearing from you. Best regards, Ayushi Shukla

@milanrathod945

I have done similar work on automating my personal workshop, happy to work on this interesting project.

@guptakishan805

With a robust background in full-stack development and a specialization in API development, I'm confident that I'm the right fit for your Enterprise Email Classifier API project. Over my 5+ years career, I've successfully helped several companies like yours turn ideas into fully-functioning digital solutions through my hands-on skills in FastAPI, Java, Linux, Machine Learning (ML), and Natural Language Processing (NLP). In previous NLP projects, I've extensively worked on text classification tasks similar to what you're seeking. Notably, I possess experience with FastAPI and transformative models like BERT and RoBERTa via Hugging Face—skills that will be crucial to achieving the precise labelling and accurate routing you need for this email classification project. Finally, my expertise extends beyond building the initial service; I believe in long-term partnerships. Therefore, alongside delivering a production-ready Dockerized REST service within your stipulated timeline, I'll make sure to provide comprehensive documentation and a simplified "retraining cheatsheet" to ensure smooth transition and maintenance of the system even after completion. After understanding your goals well and tailoring my approach to meet with them precisely, I can help you deliver the dependable solution you seek for this task.

@Ipsita23x

Hello, I am a Machine Learning Engineer with hands-on experience in NLP, Hugging Face Transformers, FastAPI, Docker, and production-ready AI deployments. I can take this project end-to-end, from data preparation and model training to deployment and documentation. A relevant project I developed is an AI-powered Natural Language to SQL system that converts user queries into executable SQL using transformer-based NLP models. The project involved text preprocessing, model evaluation, API development, and deployment, giving me strong experience with real-world NLP workflows. For your project, I will: • Build a reproducible data-cleaning and PII-removal pipeline. • Develop and compare TF-IDF + SVM and transformer-based models (BERT/RoBERTa). • Evaluate performance using Precision, Recall, F1-score, confusion matrices, and detailed error analysis. • Optimize the selected model for fast CPU inference. • Deploy the solution as a FastAPI REST service with Docker. • Provide complete documentation, setup instructions, and a retraining guide. My focus will be on meeting your acceptance criteria: achieving ≥0.90 F1-score, sub-500ms inference on CPU, and a deployment process that can be reproduced easily. I am available to start immediately and can deliver clean, maintainable code along with comprehensive documentation and deployment support. Looking forward to discussing the project. Best regards, Ipsita

@LouisVHB

"IF YOU ARE NOT HAPPY - YOU DO NOT PAY" "After reading this project, I confidently say it aligns perfectly with my skills. I've excelled in similar tasks for clients with comparable needs. My work quality is unmatched." I understand the importance of accurate email classification for HR, Finance, and IT Support. I propose a workflow that includes meticulous data preparation, advanced modelling with transformer models, and seamless deployment via FastAPI in Docker containers. I will deliver a cleaned dataset, model evaluations, a production-ready API, and comprehensive documentation for ease of use. While new to Freelancer, I bring extensive off-site experience. To earn your trust, I offer discounted rates to build my profile. This project is a great fit for me due to my expertise in NLP and text classification. Lets talk, even if you don't hire me at least you get a free consultation from me. Regards, Louis Brink

@krishp57

Hello, I am an AIML student with hands-on experience in Python, FastAPI, machine learning workflows, and backend development. I have worked on AI-powered applications and understand the complete pipeline from data preparation and model training to API deployment. For this project, I can: - Prepare and document the data-cleaning pipeline - Build and evaluate TF-IDF + SVM baselines - Fine-tune and compare transformer-based models using Hugging Face - Analyze precision, recall, F1-score, and common classification errors - Deploy the selected model behind a FastAPI REST API - Containerize the solution using Docker - Provide clear setup, retraining, and deployment documentation I focus on clean, maintainable code and reproducible ML workflows. While final model performance depends on the dataset quality and class distribution, I will evaluate multiple approaches and optimize the solution based on the data characteristics. I would be happy to review the dataset structure and discuss the project requirements in more detail. Best regards, Krish