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 growing businesses.
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
- →Python for workflow orchestration and custom processing
- →Pydantic for structured output validation and type-safe extraction
- →LangChain when multi-step reasoning or document chaining is needed
- →Vector databases (ChromaDB, Qdrant) for semantic search and retrieval
- →Open-source models available for data-sensitive workloads
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.
Side by Side
AI Processing vs Manual Review
| Feature | AI Automation (Syntora) | Manual Human Review |
|---|---|---|
| Throughput | Hundreds of documents per hour. Scales linearly | 5-15 documents per hour per person |
| Consistency | Same logic every time. No fatigue errors | Varies by reviewer, time of day, workload |
| Format Handling | Interprets varying formats and layouts automatically | Handles variation but needs retraining |
| Cost at Volume | $100-500/month API costs at scale | Linear headcount growth per 500 docs/week |
| Edge Cases | Confidence scoring flags uncertain results. 95%+ on trained types | Better human judgment on novel cases |
| Audit Trail | Every extraction logged with confidence scores and timestamps | Depends on individual discipline |
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.
Read about our security practices or see how AI automation transforms commercial real estate workflows.