AI Email Data Entry Automation
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
I need an end-to-end AI workflow that removes the manual data-entry burden created by incoming email. Each time a message lands in our inbox the system should:
• Parse the email, identify and validate all required fields (names, dates, order numbers, any custom tags we define)
• Post those fields directly into our Google Sheet or database with zero duplication
• Draft a clear, on-brand reply when the message is a routine inquiry so my team only has to skim and hit Send
• Keep a timestamped log we can audit later for accuracy and compliance
I’m open to your choice of stack—Python, Zapier, Make, OpenAI, RPA tools, or a mix—so long as the result is reliable, explainable, and easy to extend. Integration happens exclusively through email for now; if we decide to add live chat or social channels later I’ll treat that as a follow-on milestone.
Acceptance criteria
1. Data captured from a test batch of 100 emails shows ≥98 % field-level accuracy.
2. Suggested replies reflect our brand voice and require no more than light editing in 90 % of cases.
3. Deployment instructions and commented code/docker files allow us to run the pipeline on our own server.
If this scope is clear, let’s talk timing and next steps so you can get started right away.
• Parse the email, identify and validate all required fields (names, dates, order numbers, any custom tags we define)
• Post those fields directly into our Google Sheet or database with zero duplication
• Draft a clear, on-brand reply when the message is a routine inquiry so my team only has to skim and hit Send
• Keep a timestamped log we can audit later for accuracy and compliance
I’m open to your choice of stack—Python, Zapier, Make, OpenAI, RPA tools, or a mix—so long as the result is reliable, explainable, and easy to extend. Integration happens exclusively through email for now; if we decide to add live chat or social channels later I’ll treat that as a follow-on milestone.
Acceptance criteria
1. Data captured from a test batch of 100 emails shows ≥98 % field-level accuracy.
2. Suggested replies reflect our brand voice and require no more than light editing in 90 % of cases.
3. Deployment instructions and commented code/docker files allow us to run the pipeline on our own server.
If this scope is clear, let’s talk timing and next steps so you can get started right away.
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