Findly

Findly fuses LLMs with a bulletproof semantic layer to turn messy enterprise data into crystal-clear, real-time insights. Trusted by 8,000+ users, it slashes forecasting, risk alerts and white-label analytics to seconds—no code, full SOC 2 security.

Large Language Models as the Cognitive Engine

Findly’s core orchestration layer taps into state-of-the-art transformer-based LLMs fine-tuned on millions of industry-specific queries. Instead of forcing analysts to learn SQL or Python, the LLM translates natural-language prompts into optimized query plans. The model continuously learns from user feedback, improving accuracy by up to 23 % month-over-month according to internal telemetry shared on the Trust Center.

Semantic Layer – The Accuracy Multiplier

While LLMs excel at language, they can hallucinate metrics. Findly neutralizes that risk with a proprietary semantic layer that maps business concepts to verified data schemas. Think of it as a GPS for data: the semantic layer validates every LLM-generated query against canonical definitions of “revenue,” “churn,” or “on-time delivery.” The result is 99.2 % query-level accuracy reported across the last twelve SOC 2 audits.

Real-Time Streaming Architecture

Behind the scenes, Findly leverages Apache Kafka for event ingestion and DuckDB for low-latency analytics, ensuring that price feeds, IoT sensors, or ERP transactions are reflected in dashboards within sub-second latencies. This streaming backbone is auto-scaled via Kubernetes, delivering elastic throughput during market open or Black-Friday-level traffic spikes.