AI Automation: What Should Businesses Actually Automate?

01 // The Technical Problem

Companies spend hundreds of hours training AI models to write marketing blog posts that still require extensive editing. Meanwhile, operations staff spend hours daily manually transcribing details from PDFs, invoices, and emails into ERP databases. This manual data entry is slow, prone to errors, and delays invoicing and fulfillment loops.

02 // Background & Engineering Constraints

The return on investment (ROI) of automation is highest on repetitive, structured, and rules-based tasks with high volumes. For example, qualifying incoming web forms, extracting details from billing PDF files, and syncing them across software services yields immediate hours saved and minimizes processing delays.

03 // Architecture Solution & Implementation

We configure API-driven automation loops. We link file upload systems to LLM document parsing engines. When an invoice PDF arrives, the system runs OCR to extract the text, utilizes structured LLM tools to compile standard JSON data (e.g. line items, tax totals), and pipes this structured data directly into the accounting database.

We write serverless script triggers using Node.js or Python. We link incoming email events to a parser script. The script downloads attachments, runs them through an LLM document extraction API, verifies calculations, and sends a request to the HubSpot CRM API to update client records.

Implementation Snippet (python)

from pydantic import BaseModel, Field

class InvoiceSchema(BaseModel):
    vendor: str = Field(description="Name of the vendor")
    total_amount: float = Field(description="Total invoice cost in USD")
    invoice_date: str = Field(description="Date formatted as YYYY-MM-DD")

Frequently Asked Technical Questions