AI marketing OS unifying brand intelligence, creative production, and paid media
Zocket consolidates brand monitoring, creative generation, audience intelligence, and campaign automation into a single AI-native platform for enterprise marketers. The tech stack reveals a production-first architecture: heavy investment in video creation tools (Runway, DaVinci Resolve, CapCut, Adobe suite), LLM inference optimization (vLLM, Triton, SageMaker), and real-time data processing, paired with an active roadmap around LLM-powered features and RAG pipelines. Design hiring dominates the team (4 of 6 open roles), signaling that motion and creative output velocity is the immediate bottleneck.
Zocket is an AI marketing platform built for enterprise consumer brands across e-commerce, FMCG, fashion, and financial services. The product spans four core modules: brand intelligence (real-time sentiment and competitive tracking across 100+ channels), AI-driven creative design (multi-format ad generation with brand guardrails), consumer insights (automated trend analysis and audience intelligence), and paid media orchestration with unified reporting. Founded in 2021 and based in San Francisco, the company operates at a scale of 100+ enterprise customers and is actively building out its design and engineering teams, with current hiring concentrated in India.
Zocket uses a video-production and AI-inference optimized stack: Adobe tools (Photoshop, Premiere, After Effects), Runway and Midjourney for generative creative, DaVinci Resolve and CapCut for editing, OpenAI and Anthropic for LLM, vLLM and Triton for inference optimization, FastAPI and Docker for backend, and AWS/GCP for cloud infrastructure.
Current project focus includes high-converting social video production, content repurposing for ad variations, product/launch videos, LLM-powered feature development end-to-end, and RAG pipelines for scaled creative automation.
Zocket's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.