I built PRISM for marketing teams that depend on paid media to acquire customers. In these teams, research, production work, including assets, and performance data were spread across separate tools and channels. As a result, the chain from strategy → creative briefing → asset production → review broke down, and information was lost at each step.
Rapid creative iteration is now the standard for paid media teams. Teams without systems like PRISM cannot adapt at the required pace.
PRISM connects what the team knows, what it decides to make, and what it learns after a test.
PRISM includes a competitive intelligence system that collects competitor ads from Meta and organic short-form content from Instagram and TikTok. SerpAPI, Apify, Browserbase, yt-dlp, and approved platform APIs support discovery and collection. The system processes transcription and video analysis at the same time. It extracts hooks from the combined evidence, then selects distinct posts through deterministic ranking and ID matching.
PRISM classifies selected content and reports recurring patterns. It uses those patterns to produce creative ideas. Amazon S3 stores the media, while PostgreSQL stores production data. Cost predictions, dashboards, and human review control each run.
I designed PRISM to make explicit choices about audience, message, offer, and production direction. Every creative brief is grounded in the approved brand guidelines and messaging for the company and product. The approved brief then moves through identity references, storyboards, voice, provider tasks, final assembly, and quality checks.
PRISM uses models available through OpenRouter to analyze competitive intelligence and write creative briefs. Asset production uses OpenRouter alongside media providers such as Fal, Kie AI, and Replicate. HyperFrames and FFmpeg handle post-production.
Provider adapters give PRISM a backup path when a provider is unavailable. The system can change providers without changing the job model or losing the production record.
Approved character identity and product references keep later production stages consistent. PRISM uses reference-based continuity, not model fine-tuning or LoRA training. A person must approve each important stage before later work can use it.
PRISM stores prompts, assets, provider records, review decisions, and quality checks. A revision regenerates only the affected asset. Replay preserves approved work and marks dependent work for regeneration. A failed stage can restart from its last valid state.
After launch, PRISM monitors asset performance. Measured results inform the next strategy decision. Each production cycle uses evidence from the previous cycle.