Smart, Private AI Workflows for Small Firms to Save Costs and Protect Data
🧠Understanding the Core AI Model Types Before building a smart AI workflow, it’s essential to understand the three main categories of models: Model Type Examples Best For Encoder-only BERT, DistilBERT Classification, entity recognition Decoder-only GPT-4.5, GPT-4o Text generation, summarization Encoder-Decoder BART, T5 Format conversion (e.g., text ↔ JSON) Use the right model for the right job—don’t overuse LLMs where smaller models will do. 🧾 Why Traditional Approaches Often Fall Short ❌ LLM-Only (e.g., GPT-4.5 for everything) Expensive: GPT-4.5 API usage can cost $5–$15 per 1,000 tokens depending on tier. Resource-heavy for local deployment (requires GPUs). High risk if sending sensitive financial data to cloud APIs. Overkill for parsing emails or extracting numbers. ❌ SaaS Automation Tools (e.g., QuickBooks AI, Dext) Limited transparency: You can’t fine-tune or inspect the logic. Lack of custom workflow integration. Privacy concerns: Client data stored on external servers. Recurring subscription costs grow with team size. Often feature-rich but rigid—one-size-fits-all solutions. ✅ A Better Path: Modular, Privacy-First AI Workflow Using a combination of open-source models and selective LLM use, small firms can achieve automation that is cost-effective, privacy-preserving, and fully controllable. ...