AI Automation
Use cloud AI (OpenAI, Claude, Gemini) to handle unstructured data like documents, emails, and images that require interpretation.
What is AI Automation?
AI automation uses language models (OpenAI GPT, Claude, Google Gemini) to process unstructured data. Unlike process automation, which follows fixed rules, AI automation can interpret documents, understand context, extract meaning, and make probabilistic decisions.
This is perfect when your data doesn't follow predictable patterns. Emails with different formats, invoices from various vendors, customer inquiries that need interpretation. AI automation handles the complexity that rule-based systems can't.
Common Use Cases
What we automate
These are the most common AI automation workflows we build for SMBs.
Email Classification
Automatically categorize incoming emails, extract key information, and route them to the right team or trigger appropriate workflows.
Document Data Extraction
Extract structured data from PDFs, scanned documents, invoices, contracts, and forms. Handle variations in format automatically.
Customer Inquiry Response
Generate intelligent responses to common customer questions. Pull from your knowledge base and provide accurate answers that feel human-written.
Content Analysis
Analyze documents, social media posts, customer reviews, or support tickets for sentiment, key themes, compliance issues, or action items.
Quality Review
Automatically review content for accuracy, completeness, tone, and compliance. Flag issues before they reach customers.
Data Enrichment
Enhance your records by extracting additional information from text. Parse addresses, identify company names, extract job titles intelligently.
Our Stack
Technology we use
AI Models
- →OpenAI (GPT-4, GPT-4o) for general intelligence and reasoning
- →Anthropic Claude for nuanced understanding and long documents
- →Google Gemini when multimodal capabilities are needed
- →OCR tools (Tesseract, Google Vision) for document processing
Integration Layer
- →n8n for workflow orchestration
- →Custom Python scripts for complex processing
- →Vector databases (Pinecone, ChromaDB) when semantic search is needed
Quality Assurance
AI systems require different testing than rule-based automation. We use deterministic QA with ground truth datasets, targeting 95%+ accuracy before production. We test edge cases extensively and build in human review loops when appropriate.
Is This Right For You?
Ideal client profile
AI automation is the right fit if you:
- →Process 100+ documents per week with varying formats
- →Handle unstructured data that doesn't follow fixed patterns
- →Need to interpret meaning, not just move data
- →Are comfortable with cloud-based AI (no strict compliance restrictions)
- →Want to eliminate manual review and categorization work
- →Need to scale without sacrificing quality or accuracy
Pricing Structure
AI automation projects are priced based on complexity and volume. Simple email classification starts around $5,000. Complex document processing with multiple data points ranges from $10,000-$25,000. Most projects include ongoing AI API costs of $100-500/month depending on volume.
We offer optional managed services for monitoring, retraining, and optimization to ensure your AI automation continues to perform as your data evolves.
Ready to automate unstructured data?
Book a discovery call. We'll analyze your documents and workflows to show you exactly what AI can automate and what accuracy to expect.