Raindrop has launched a purpose‑built observability platform for production generative AI applications to tackle silent failures and the “black box” problem. The new platform combines large‑language‑model summarization with bespoke classifiers while offering SOC‑2 grade encryption. The Need for Raindrop AI Observability According to a McKinsey survey, only 27% of enterprises that deploy generative AI evaluate all AI outputs before release. This output result lacks visibility into silent errors that traditional monitoring misses. Raindrop has come with an ingenious solution which continuously analyzes user interactions to identify anomalies in real time. How Raindrop Works The platform uses large‑language models to distill user‑AI exchanges into concise summaries, quickly surfacing error patterns and unusual behaviors. Lightweight, high‑performance classifiers then categorize those summaries into failure types to scale analysis across millions of daily events. These failure types include hallucinations, task failures, and policy refusals. Raindrop ingests both explicit feedback signals (thumbs down) and implicit cues (repeated corrections) to refine its detection algorithms and reduce false positives. Deployment Options and Data Privacy Raindrop’s cloud offering features SOC‑2 compliant encryption for data at rest and in transit. While the on‑premises variant performs client‑side redaction and server‑side semantic filtering, ensuring no persistent data leaves the customer’s infrastructure. Early Adopters and Partnerships Initial customers include AI code‑generation firms, customer support platforms, and creative assistance services. Key use cases span: Customer Support Bots: Real‑time alerts when chatbots return incorrect or unhelpful responses. AI Code Assistants: Detection of hallucinated code snippets to maintain developer trust. Content Generation: Monitoring tone consistency and compliance with content policies. Pricing and Commercial Model Raindrop offers tiered plans to suit diverse team sizes and needs, but it can be on the pricier side for many Pakistani users: Starter Plan: $65 per month (PKR 18,300/-) with pay‑as‑you‑go metered usage. Pro Plan: $350 per month (PKR 98,520/-), including custom topic tracking, historical analysis, and on‑premises deployment. To handle massive event volumes, Raindrop leverages Tinybird’s real‑time analytics platform, enabling it to process hundreds of millions of events per day while maintaining performance and scalability. The company is also working on deeper integrations with major AI frameworks to streamline adoption.