AI agent Customer Insights MVP Build

@Microlent

Hello, I can deliver a clean, reliable MVP of AnanthaAI’s Customer Insights & Growth Opportunity Agent exactly as scoped. I’ll build a full Python pipeline for ingestion, cleaning, and automated analytics (RFM, cohorts, segmentation), then layer churn/CLV/next-best-action models using scikit-learn or XGBoost. A polished Streamlit UI will let users explore segments, view insights in plain English, and export action-ready CSVs. I’ll generate large synthetic datasets, package everything with reproducible requirements, and provide a structured repo, lightweight architecture diagrams, and a short walkthrough video. I’ve built ML dashboards and data-to-recommendation flows before, and can deliver a dependable MVP quickly for advisor/client demos. Let’s define milestones and get started. Best, Jenifer

@snbhanja

Hi, I have worked on customer Analytics, segment action projects before using ML algos like clustering, xgboost etc. I can generate the five different synthetic datasets with the streamlit ui detailing customer segments and other insights as you need. I will package everything in docker for easy deployment. Let me know, let’s connect.

@mayankinnovative

Hello, With 15+ years of experience in data science and machine learning, I'm excited to build AnanthaAI's Customer Insights & Growth Opportunity Agent MVP. *Project Understanding* You'll provide synthetic datasets imitating CRM, e-commerce, and in-store tables. I'll develop a pipeline for data ingestion, analytics, predictive models, and a Streamlit UI for user interaction. *Step-by-Step Approach* 1. *Data Ingestion & Cleaning* - Create synthetic datasets (5 datasets, 10,000 rows each) - Develop data ingestion and cleaning pipeline using Python and pandas 2. *Analytics & Predictive Models* - Implement RFM scoring, behavioural segmentation, and cohort analysis - Develop predictive models for churn risk, lifetime value, and next-best action using scikit-learn or XGBoost 3. *Streamlit UI & Visualization* - Design a user-friendly Streamlit UI for segment selection, metric viewing, and CSV export - Implement data visualization for key metrics and recommendations *Key Highlights* - Code repo with clear structure and concise README - Working Streamlit app deployable locally - Synthetic datasets and generation scripts - Short demo video or GIF walking through the UI I'm confident in delivering a high-quality MVP that meets your requirements. Let's discuss further. Your AnanthaAI MVP, ready – let's get started!

@Rahulsingh5xz

Hello, I’m Rahul Singh from Team Velora. With 3 years of experience building data-driven MVPs and ML-powered analytics dashboards, we can deliver a polished Streamlit app for AnanthaAI that ingests customer data, runs predictive analytics, and produces action-ready segments. Please come into a private chat to see examples of similar Python/ML dashboards we’ve developed. We’ll provide clean datasets, automated RFM/cohort/CLV analytics, predictive recommendations, and a user-friendly UI ready for client demos.

@hamzakhattabi

Hey there, I am an AI/ML Engineer with over 5 years of experience building data pipelines, customer analytics engines, and Streamlit-based MVPs. I can deliver your full workflow—synthetic data generation, RFM/cohort analytics, ML models, recommendation engine, and a polished Streamlit UI. My expertise includes Python/pandas, scikit-learn/XGBoost, and Streamlit + containerized deployments. With my experience, I’m sure I can complete this quickly and reliably. Regards,

@hieupham97

Hello, I am an AI engineer with 5+ years of experience. I can provide the service you seek. We can discuss for more details via DM.

@oleksandrf3

Hi there, your goal for AnanthaAI’s MVP is clear: an end-to-end pipeline from ingesting rich synthetic customer data to actionable segment exports, all wrapped in a polished Streamlit UI ready for clients. My background lines up perfectly since I’ve built customer analytics dashboards and ML-driven apps using pandas, scikit-learn/XGBoost, and Streamlit for real-world e-commerce and SaaS companies—handling everything from dataset generation and RFM scoring to churn forecasting, segmentation, and clean UI deployments. You’ll get a modular, well-documented repo, reproducible datasets, and a tidy app that’s client demo–ready, plus lightweight diagrams and a walkthrough to make sign-off seamless. Is there a preferred CRM or specific customer types you want the synthetic data to reflect most closely for the MVP?

@habebaftooh

I hold a Bachelor’s degree in Artificial Intelligence and I’m currently completing my Master’s in AI in Germany, and I’ve delivered multiple data-driven systems that focus on customer analytics, behavioural modeling, churn/CLV prediction, and growth-oriented recommendation engines. The workflow you’re targeting is something I’ve already built variations of in past projects, so I’m very familiar with the structure and expectations. My approach is straightforward: establish a clean data foundation, build reliable insight layers, and package everything into a polished MVP that clearly demonstrates value to early clients. I’ll generate realistic synthetic datasets, implement robust preprocessing, design the customer-insight logic (RFM, segments, cohorts), and develop prediction models for churn risk, lifetime value, and next-best-action. All components will be engineered for clarity, speed, and easy extension. On the product side, I’ll build a smooth Streamlit interface where users can explore segments, view metrics, understand recommendations, and export action-ready CSVs. I’ve built similar dashboards before simple, intuitive, and demo-ready so achieving a professional, reliable UI will not be an issue.

@tamannarajput18

As a seasoned Full Stack Developer with six years of industry experience, I am well-equipped to tackle the immense frontend and backend challenges your project entails. My data analysis prowess and Python skills, in particular, facilitate optimal handling of complex data pipelines such as data ingestion and cleaning, which are critical components of your project. Furthermore, I have an expansive background in creating intelligently-designed UI experiences that prioritize ease-of-use and understandability. By employing Streamlit, I can not only ensure a robust user interface but also deliver actionable insights presented in a human-readable format. My innovative approach to streamlining analytics dashboards using relevant libraries like `pandas` for data manipulation will ensure an efficient RFM scoring, cohort analysis, and behavioral segmentation on your dataset. In addition to my technical skills, my knack for effective project management is something I know will serve us well on this venture. Conformity with project timelines is one of my key priorities and I am fully committed to delivering the desired MVP functionalities such as predictive models for churn risk, lifetime value, and next-best action exactly on time.

@UsmanSaleem813

Hi, A Full Stack & Flutter Developer with experience in AI integrations, cloud deployment, and CRM solutions. delivering high-quality web/mobile apps, smart automation, and scalable cloud-based systems. Let’s connect, PRICE negotiable. ➡️ Fast delivery | 100% satisfaction

@ivand587

Hello, I am pleased to submit a proposal for delivering the AnanthaAI Customer Insights & Growth Opportunity Agent MVP. ⭐ Availability in your time zone and ready to start immediately. ⭐ Proven experience building end-to-end data analytics pipelines and ML-driven recommendation engines. ⭐ Technical expertise in Python, pandas, scikit-learn, XGBoost, and Streamlit. ⭐ Portfolio evidence available for dashboards, segmentation, and predictive analytics projects. ⭐ Client-focused approach: every solution designed to drive actionable insights and measurable business outcomes. Proposed Deliverables: ✅ Data ingestion & cleaning pipeline with synthetic dataset generation ✅ Automated analytics: RFM scoring, cohort analysis, churn risk, and CLV modeling ✅ Recommendation engine highlighting growth opportunities ✅ Action-ready CSV/Excel outputs for customer segments ✅ Interactive Streamlit UI with plain-English insights ✅ Documentation and lightweight architecture diagrams ✅ Demo video or GIF walkthrough Looking forward to delivering a polished, reliable MVP that demonstrates clear business value. Best regards, Ivan.

@nidhip091

Hi , I’m Nidhi Pandey, a Senior Solutions Architect with 11+ years of experience in data automation, CRM systems, Power Platform, and cloud-driven analytics. I specialize in building end-to-end pipelines, clean data structures, ML-driven insights, and interactive dashboards. I can deliver your MVP with a polished, reliable workflow covering: Data ingestion & cleaning (Python + pandas) Automated analytics (RFM, cohorts, segmentation) Predictive models (churn, CLV, next-best action using scikit-learn/XGBoost) Action-ready outputs (CSV/Excel segments) Streamlit UI with insights + recommendations Containerized or requirements-based deployment Complete documentation + light architecture diagrams Data Layer I will generate all 5 synthetic datasets (10K+ rows each): Customers, Transactions, Visits, Feedback/NPS, and Marketing Campaigns—fully realistic and ready for testing. Before we start, I’d like to confirm: Preferred modeling depth (baseline models or more tuned ML)? Any specific UI branding or layout preferences for Streamlit? Expected timeline for MVP review/demo? Once confirmed, I’ll share a clear roadmap and delivery schedule. Looking forward to building this high-quality MVP for your early clients and advisors. Best regards, Nidhi

@kunalpt161

Hi, I’m interested in building this MVP for you. I work extensively with Python, pandas, scikit-learn, and Streamlit, and I’ve built end-to-end analytics pipelines before—from generating synthetic data to modelling churn, CLV and customer segments, and wrapping everything in a simple UI. I can create the five synthetic datasets, build a clean ingestion and feature pipeline, automate RFM/cohorts/churn insights, train lightweight predictive models, and deliver a polished Streamlit app where users can explore segments and download action-ready CSVs. I’ll also provide clear documentation and a walkthrough so the build is easy to present and extend. This aligns well with the work I already do, and I’d be happy to deliver a reliable, well-structured MVP.

@karthikresonite

Hello, I can build a polished MVP for AnanthaAI’s Customer Insights Agent that ingests sample customer datasets, generates actionable analytics, and provides growth recommendations via a Streamlit UI. Approach: Data Layer: Generate synthetic datasets (customers, transactions, visits, feedback, campaigns; ≥10k rows each). Pipeline: Clean & merge data using Python/pandas. Analytics: Automated RFM scoring, cohort analysis, churn & CLV predictions using scikit-learn/XGBoost. Recommendations: Next-best-action insights surfaced in plain English, ready to export as CSV/Excel. UI: Interactive Streamlit dashboard with segment selection, metrics, insights, and CSV export. Deliverables: Code repo with structured modules, README, lightweight architecture diagram, and demo video/GIF. Deployment: Containerised or one-shot requirements setup for easy local execution. I have experience in ML-powered dashboards and predictive analytics for early-stage client demos. Timeline: ~14–16 days to deliver a fully functional MVP. Best regards Resonite Technologies