I want to implement an AI-driven business process automation that measurably raises our data accuracy and eliminates the manual double-checking that is slowing teams down today. The precise workflow to automate is still open for discussion—I’ll share live process recordings and sample data once we start—so I need someone comfortable analysing an existing operation, spotting error-prone steps and then designing, training and deploying a streamlined AI solution around them. The heart of the brief is accuracy: whatever you build must show clear improvement over our current error rate, with easy-to-read metrics that prove it. Expect to work with structured data, APIs and possibly an RPA layer; Python, TensorFlow/PyTorch, and cloud services such as AWS or Azure are all on the table if they help reach the goal. Later on we may expand the same framework into marketing or customer-service flows, so keep the architecture modular. Deliverables I will use for sign-off: • A brief discovery report summarising the chosen workflow and baseline error statistics • A working automation (script, model and orchestration code) deployed in our environment • A simple dashboard or log output that tracks accuracy gains in real time • Hand-off documentation so my team can maintain and extend the solution If you have a record of squeezing every last percentage point of accuracy out of business processes with AI, I’d love to see how you’d tackle this.