Cultured Computer
Persistent persona infrastructure. Identity that holds across conversations, models, and time.
LLMs are stateless. No memory between calls, no awareness of where a conversation has been or should go. System prompt adherence degrades 30%+ within 8-12 turns (Li et al., COLM 2024). Larger models drift more, not less (Choi et al., 2024). Persona instructions lose influence over token distance.
The commercial AI stack measures deflection rates, CSAT, and resolution time. None of them measure whether identity held, whether trust formed, or why the user came back.
The gap
66% of CX professionals say empathetic AI is "very important." Over 50% say brand voice is "very important." Nobody measures either.
What We Build
A persistent persona layer that sits between reasoning engines and users. Every response is scored against the persona definition in real time. Below threshold triggers automatic regeneration. The user never sees the failed response.
| Dimension | Question |
|---|---|
| Intent | Why did the user come? |
| Persona | Did identity hold? |
| Emotion | Did trust form? |
| Return | Did they want to come back? |
Current tools tell you "ticket resolved." We measure whether the customer is forming a relationship, whether trust is increasing, and whether they want to come back.
How It Works
A persona is a deployable unit. It ships with a structured identity spec (RICE Framework), a decision architecture that runs once per message, and real-time evaluation that scores every response.
Swap the model underneath. Identity holds.
Explore the Docs
Architecture
Decision loop, measurement layer, and product primitives.
Persona
Persistent identity layers: definition, validation, and model-agnostic delivery.
PersonaPersistBench
Evaluation framework scoring identity persistence across turns and models.
Mercury
Diffusion-based LLM with burst generation and TTS pipeline.
Alyssa
First production persona. Healthcare care companion over SMS and voice.