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Generates candidate business names using a language model (likely GPT-3.5/4 or similar) with prompt engineering to produce creative, brandable alternatives. The system likely uses temperature/sampling parameters and constrained decoding to avoid repetitive outputs across multiple generation passes. Names are produced in batches (typically 50+) with semantic filtering to reduce linguistic overlap and improve perceived originality.

Validates domain availability by querying domain registrar APIs (likely GoDaddy, Namecheap, or WHOIS protocol) for each generated name in real-time or near-real-time. The system batches availability checks to reduce API call overhead and caches results to avoid redundant lookups. Returns availability status (available, taken, premium) and optionally pricing for premium domains.

Allows users to control generation behavior through parameters like creativity level (temperature), name style (modern, classic, playful), industry focus, and target audience. The system uses these parameters to adjust LLM sampling behavior and apply post-generation filtering rules (e.g., exclude names longer than 3 syllables, exclude names with numbers). Generates names in configurable batch sizes (typically 10-100 per request).

Maintains a session-based history of all generated names and their metadata (generation parameters, domain availability, timestamp). Allows users to compare names across multiple batches, filter by availability status, and export results. Likely uses browser-side storage (localStorage/IndexedDB) for session persistence and backend storage for logged-in users.

Extends domain checking beyond .com to include popular alternative TLDs (.io, .co, .net, .app, .dev, etc.). For each name, queries availability and pricing across multiple TLDs simultaneously, aggregating results into a single view. Pricing data is fetched from registrar APIs and cached to reduce latency. Users can filter results by TLD or price range.