Serena turns raw text into deterministic emotional vectors — a symbolic affective layer that makes agents, apps, and future AGI systems emotionally aware, explainable, and safe.
Primary: —
Secondary: —
Tertiary: —
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No data yet.
Today’s models can speak, but they don’t feel. Serena is a symbolic emotional engine — not a neural network — that treats affect as first-class computation. Every output is traceable, deterministic, and designed to be the emotional substrate beneath agents, copilots, and future AGI systems.
Route, prioritise, and shape responses based on real emotional state — not just sentiment keywords.
Track emotional stability over time with deterministic vectors, without training on or storing sensitive text.
Give agents and characters a consistent, inspectable mood system that updates the same way every time.
Your system sends raw sentences to the Serena API.
Serena maps the text into a 7D emotional vector and snaps it to the nearest emotion class using fully symbolic rules.
You get primary / secondary / tertiary emotions plus the raw vector — ready to plug into agents, dashboards, or analytics.
| Serena | Neural Nets | |
|---|---|---|
| Transparency | Full reasoning path | Hidden weights |
| Consistency | Deterministic vectors | Varies between calls |
| Hallucination Risk | No text generation | Possible |
| Data Privacy | No training on inputs | Often used as training data |
| Hardware | CPU-first, lightweight | Often GPU required |
Serena starts as a single, battle-tested emotional engine and grows into the affective layer beneath agents, products, and, eventually, AGI research. Pick a phase to see what ships when.
The symbolic emotional engine that powers the current demo and internal tooling.
Timelines are indicative and will shift based on what we learn from early developers and research partners.
Start free, then scale as Serena becomes part of your stack. Pricing is based on sentences analyzed per month.
$9
50,000 sentences / month
$39
500,000 sentences / month
Custom
Millions+ sentences / month
Whether you’re building with the API, exploring enterprise deployments, or looking at Serena as an affective layer for agents or AGI research — we’d love to hear from you.
Typical response time is 15–40ms depending on payload size and region.
Yes. Serena produces fully deterministic emotional vectors for identical input text.
Never. Serena does not store inputs and is not trained on end-user text.
Yes. Enterprise customers can deploy Serena on-prem or in their private cloud.