Problem
Every AI content workflow collapses at the same point: the system doesn’t know who the brand actually is. Tone guides in PDFs don’t work. The model hallucinates the brand voice. The output is generic. The problem is structural, brand context needs to be machine-readable, not human-readable.
Built
A two-phase system. Phase one: an interview agent conducts a structured 40-question session with the brand owner, extracting positioning, audience, competitive stance, voice, and messaging hierarchy. Phase two: a synthesis agent compresses the transcript into a JSON schema artifact, a lightweight, versioned representation of brand identity that any downstream agent can consume.
Result
First deployed with a Berlin-based equity fund. Content acceptance rate increased from 40% to 91% after first revision cycle. Brand drift across 6 months of AI-generated content: near zero as measured by cosine similarity to baseline samples. Now productizing for SaaS.