AI Integration Services

Adding LLM-powered features to an existing product requires: API client setup (OpenAI SDK or Anthropic SDK with TypeScript types, retry logic with exponential backoff, timeout configuration), streaming response implementation (Server-Sent Events from backend to frontend users see tokens appear as they are generated, not a blank screen for 10 seconds), prompt engineering (system prompts that define model behaviour precisely, few-shot examples for consistent output formatting, chain-of-thought instructions for reasoning-intensive tasks), structured output (JSON mode with Pydantic/Zod schema LLM responses validated against a type definition before they reach the application layer), and model fallback (primary model + fallback model automatically switch if primary is rate-limited or unavailable).

Cost management requires four mechanisms: token counting and budget limits (count tokens before each API call reject or truncate requests that would exceed a per-user or per-request budget), response caching (cache responses to repeated or semantically similar queries a user asking "what is your refund policy?" should not trigger a new LLM call every time), model tiering (route requests to cheaper, faster models GPT-4o mini at $0.15/1M tokens vs GPT-4o at $2.50/1M tokens based on task complexity), and per-user rate limiting (cap the number of AI requests per user per day prevents any single user or abuse pattern from exhausting your API budget). ClickMasters implements all four mechanisms and sets up a cost monitoring dashboard (usage per model, per user, per feature with budget alert thresholds) as standard.