An r/DigitalMarketing thread asked how agencies keep client brand voices distinct when LLMs are in the loop. This walks the per-client fingerprint + Scavio live-reference approach.
Prerequisites
- LLM API key (Claude, GPT, Gemini)
- Scavio API key
- 10-100 sample artifacts per client (posts, emails, transcripts)
- 1 hour per client to build the fingerprint
Walkthrough
Step 1: Collect 50-100 sample artifacts per client
Real posts, emails, transcripts.
Text
// ./brand-voices/<client-slug>/<sample-1.md>, sample-2.md, ...Step 2: Generate the voice fingerprint via LLM analysis
One-page deliverable.
Text
// LLM prompt:
// Analyze the attached samples. Output a fingerprint:
// - Sentence length range (median, p90)
// - Vocabulary preferences (3-5 words/phrases used 3+ times)
// - Don't-use list
// - Signature openers and closers
// - Tone descriptors with evidence samplesStep 3: Save fingerprint as a 1-page YAML/MD per client
Version-controlled.
// brand-voices/client-x.yaml: { sentence_length_median: 12, vocab_preferences: [...], dont_use: [...], signature_open: ..., signature_close: ..., tone_adjs: [...] }Step 4: Per-content-task: Scavio fetches live recent samples
Not last-month-cached.
Text
// site:instagram.com/CLIENT (recent captions)
// site:linkedin.com/in/CLIENT (recent posts)
// site:CLIENT.com/blog (latest articles)Step 5: Compose with fingerprint + live samples in the LLM prompt
Both static and dynamic anchor.
Text
// LLM prompt:
// Write a post for <client> using the fingerprint AND matching the tone of these 3 recent samples:
// Fingerprint: <YAML>
// Recent samples: <Scavio top-3>
// Brief: <today's content brief>Step 6: QA: spot-check 1-of-5 against the fingerprint
Manual sanity check.
Text
// Quick checklist: signature opener? sentence length OK? tone adjectives match? any forbidden vocabulary?Python Example
Python
# Per-client-month: 30 posts × 1 LLM + 1 Scavio reference = ~$1-3. Setup: 1-2h per client to build fingerprint.JavaScript Example
JavaScript
// Same in TS.Expected Output
JSON
Distinct, recognizable per-client posts that don't drift to default LLM tone. Fingerprint is auditable code; recent-samples grounding catches voice drift over months.