Most GPT projects collapse under real creative pressure — drift creeps in, tone unravels, hallucinations sneak through. We wanted to see if governance could change that. Seas of Mystery was our stress test: a 10-episode serialized build where every relic, voice, and scene had to hold strict canon without breaking.
Not Chatbots, but Cognitive Systems: Case Study — Seas of Mystery
Most GPT projects break under pressure:
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Drift creeps in.
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Tone collapses.
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Hallucinations masquerade as “creativity.”
At Signal & Salt, we don’t build chatbots.
We build governed systems that think with you.
The Experiment: Seas of Mystery
We stress-tested our governance stack on a serialized docu-myth IP: 10 episodes, dual POV structure, relic-driven mythos, and a grief-loaded tonal spine.
Challenge: Could a GPT hold canon, prevent hallucination, and enforce tonal fidelity across hundreds of pages of draft material — without collapsing?
Governance in Action
Here’s how the stack worked in practice:
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Memory Module (Canon Vault)
All relic behavior, faction logic, and character voiceprints were locked into Canon Keepers. Every draft was validated against these anchors before expansion. -
Voice Module (Tone Lock)
Dual-POV enforcement kept Lark’s grief voice distinct from Sophia’s prophecy cadence. Driftwatch flagged any blur between them. -
Logic Engine (Cut Checks & Tier Modes)
Exploratory drafting ran in LITE Mode. Final episode treatments were executed in PRIME Mode, with strict canon enforcement and “Cut Check” scene audits. -
Training Layer (User Safety)
Writers onboarded through ritual prompts: “Think with me, not at me.” This ensured inputs stayed structured and didn’t trigger scope creep. -
Debug Layer (Drift Audits)
Before lock, we ran Hallucination Traces to map where untagged data might have slipped in. None survived the final redline.
Results
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Voiceprint Fidelity: Every character held consistent across 10 episodes.
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Mythos Integrity: The Relic System remained emotionally grounded — no lore-bloat, no runaway “cosmic” creep.
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Format Viability: Same material exported to TV pitch deck, graphic novel layout, and transmedia lore portal with zero structural rebuild.
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Franchise Potential: Faction arcs, relic circuits, and expansion hooks emerged without forcing invention.
Why It Matters for Developers
This wasn’t a one-off story experiment.
It was proof that modular governance (Memory + Voice + Logic + Training + Debug) prevents drift and collapse in any high-context GPT build — whether you’re building:
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A classroom assistant that must hold curriculum tone,
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A brand GPT that can’t go off-message,
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Or a longform fiction partner that needs myth integrity.
Law of the Forge:
SHIT CONTENT IN → SHIT CONTENT OUT.
Governance is what makes the difference.
Signal&SaltAI
Question for this community:
If you’ve tried building high-context GPTs, how have you handled drift, hallucination, or tonal collapse? Could governance stacks like this support your workflow?
