End-to-End AI Process Automation
Budget: $250 – $750 AUD
I’m ready to overhaul three core areas of our operation—customer service, inventory management, and data analysis—by introducing intelligent, self-optimising workflows. The immediate priority is to connect and interpret our sales data, then use those insights to trigger automated actions that reduce manual touchpoints and improve response speed.
Here’s the vision:
• Customer service: deploy conversational AI (Dialogflow, GPT-based bots) that plugs into our help-desk platform, triages tickets, and resolves common queries without human intervention while still escalating edge cases seamlessly.
• Inventory management: integrate real-time stock monitoring with predictive algorithms (Python, TensorFlow or similar) so purchase orders generate automatically when thresholds are met.
• Data analysis: build a pipeline that cleans, enriches, and visualises sales data, then feeds key metrics back into both the bot and inventory models for continuous improvement.
Acceptance criteria
• A working prototype for each workflow running in a cloud environment (AWS, GCP, or Azure is fine).
• Clear documentation of architecture, API connections, and retraining procedures.
• A hand-off session where I can trigger, monitor, and adjust the automations myself.
My timeline is flexible; quality and sustainability matter more than speed. If we can validate the concept quickly, we’ll expand the scope into deeper AI automation across the organisation.
Here’s the vision:
• Customer service: deploy conversational AI (Dialogflow, GPT-based bots) that plugs into our help-desk platform, triages tickets, and resolves common queries without human intervention while still escalating edge cases seamlessly.
• Inventory management: integrate real-time stock monitoring with predictive algorithms (Python, TensorFlow or similar) so purchase orders generate automatically when thresholds are met.
• Data analysis: build a pipeline that cleans, enriches, and visualises sales data, then feeds key metrics back into both the bot and inventory models for continuous improvement.
Acceptance criteria
• A working prototype for each workflow running in a cloud environment (AWS, GCP, or Azure is fine).
• Clear documentation of architecture, API connections, and retraining procedures.
• A hand-off session where I can trigger, monitor, and adjust the automations myself.
My timeline is flexible; quality and sustainability matter more than speed. If we can validate the concept quickly, we’ll expand the scope into deeper AI automation across the organisation.