As Towards reports, most small and midsize companies don't need to build their own neural networks or new artificial intelligence software from scratch. They already have applications that support their day-to-day operations: custom CRM systems, warehouse databases, invoicing portals, and legacy customer support tools. The article explains why businesses typically choose to add AI capabilities to existing systems rather than replace them, and describes a practical production-ready integration approach—using an asynchronous queuing pattern instead of blocking HTTP requests. You need to store the raw data first, then enrich it in the background using the LLM API with rigid structured output, validate the returned JSON, and update the database while keeping the interface responsive. The following demonstrates the structure of implementing ticket sorting (e.g., returning urgency, department, resume, and flag for human review) and discusses key reliability issues: limiting the use of tokens by sanitizing/truncating input data, managing AI errors using confidence checks
ity and human redundancy, and overcoming API downtime with retries and dead letter queues. It concludes by listing common mistakes (misusing chatbots when buttons are sufficient, credential disclosure, over-complexity with heavy frameworks), offering advice on when to recommend AI integrations (high volumes of unstructured text, repetitive employee data entry, clear relational schemas), when to avoid them (tasks requiring deterministic precision or lack of available APIs), and offering guidance on selling work in terms of workflow automation and measurability. operational value. The material was prepared by a neural network based on the original publication. AI may make mistakes in details (numbers, names, dates, quotes) - for important information, check with the original source. NVIDIA Unveils SoL-Pi: Engines to Optimize Encoding Agents AI Infrastructure May Be More Valuable Than AI Applications SpeakON Unveils Magnetic Button with Voice Input for iPhone. Snorkel AI raised $350 million and increased its valuation to $3.5 billion. Rabbit announced OS3 -
a new software platform for AI agents.
