I built PRISM for teams whose research, production work, and performance data were spread across separate tools. Important evidence was lost between strategy, the creative brief, asset production, and review.
PRISM connects what the team knows, what it decides to make, and what it learns after a test.
Turn short-form content into evidence
PRISM includes a short-form intelligence system that is live for clients. It collects selected Instagram, TikTok, YouTube Shorts, and other social content by channel, creator, and hashtag.
SerpAPI, Apify, Browserbase, yt-dlp, and approved platform APIs support discovery and collection. PRISM processes transcription and video analysis at the same time. It extracts hooks from the combined evidence. Deterministic ranking and ID matching then select distinct posts.
The system classifies selected content and reports recurring patterns. It uses the patterns to produce creative ideas. Amazon S3 and PostgreSQL store media and system state. Cost predictions, dashboards, and human review control each run.
Connect strategy to production
I designed PRISM to make explicit choices about audience, message, offer, and production direction. The approved brief then moves through identity references, storyboards, voice, provider tasks, final assembly, and quality checks.
OpenRouter produces structured plans. Kie AI, GPT Image 2, Nano Banana Pro, ElevenLabs, and ByteDance Seedance support media generation. Hyperframes produces an editable composition, and FFmpeg provides a local assembly option.
Provider adapters let PRISM change a provider without changing the job model or losing the production record.
Preserve approved work and measured learning
Approved identity and product references keep later 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.
Measured results return to the next strategy decision. Each production cycle uses evidence from the previous cycle.