I plan to streamline several internal workflows with machine-learning powered automations and, in parallel, launch a tailored chatbot that reflects my brand voice. The engagement is split into two connected tracks: 1. Smart automation • Map current data flows, then design and deploy Python-based pipelines that ingest data from my CRM and analytics dashboards. • Train and tune models (TensorFlow / scikit-learn preferred) to surface predictive insights—think churn risk and high-value lead scoring—served through a lightweight API so the business team can consume results in real time. 2. Custom chatbot • Build a conversational agent (Rasa, Dialogflow, or a comparable LLM stack) able to switch context between customer support queries and basic sales questions. • Integrate with existing knowledge bases and Zendesk so the bot can escalate tickets or hand over to a human when confidence is low. • Implement continuous learning loops: conversation logs feed back into the model for refinement. Deliverables • Architecture diagram and tech stack decision notes • Clean, documented code in a private Git repository • Deployment scripts (Docker / Kubernetes friendly) • A short video walkthrough and live demo link • Handoff guide covering retraining, monitoring, and fallback procedures Acceptance criteria • Automation jobs run on schedule and return predictions with ≥90 % accuracy on the validation set I will supply. • Chatbot passes a 20-conversation test set with an average CSAT of 4/5 while handing off unresolved issues correctly. • All third-party services authenticated through my existing cloud accounts without exposing credentials in code. This scope should be achievable in discrete milestones, and I’m happy to adjust as we iterate once you’ve assessed the current data landscape.