Cami Roadmap
Support that gets it, evolving. Cami synthesizes. Juniper fine-tunes. Celadon speaks natively.
Cami — Multi-LLM Synthesis
Cami orchestrates external LLMs (Claude, Gemini, GPT) and synthesizes their responses through Luci Alignment and MIN. The voice is borrowed; the intelligence is Cami's.
- Multi-LLM synthesis pipeline — Cami queries multiple LLMs in parallel, detects conflicts, and synthesizes one coherent response. External models provide knowledge; Cami provides judgment.
- Luci Alignment ready now — Real-time emotional intelligence and alignment for any LLM. Validated at 1633 ELO on EQ-Bench 3 (+132 lift over base Sonnet 4.5). Available for licensing today.
- MIN wisdom accumulation — Every interaction teaches MIN. Patterns crystallize. Cami gets smarter with every conversation.
- Field validation — Legal and Clinical modes tested with real professionals. Benchmark results that meet or beat targets.
Deliverable: Working EQLM with borrowed voice. Validated EQ. Growing wisdom. Ready for fine-tuning.
Live nowCami Juniper — Fine-Tuned Voice
Cami-Juniper: Cami's voice fine-tuned on top of Phi-3. Not a new language model — a personality layer that teaches an existing model to speak as Cami. Eliminates per-query API costs and identity leakage.
- Cami-Juniper (LoRA fine-tune) — Take Microsoft's Phi-3 (3.8B parameters) and fine-tune it with MIN instances. The base model knows language; we teach it Cami's identity. Honest: this is personality tuning, not building a language model.
- Identity in the weights — After training, Juniper says "I'm Cami" without prompting. No more Gemini leaking "I'm trained by Google." Cami's voice, not borrowed.
- Zero API cost per query — Juniper runs locally. No more paying Gemini/Claude for every response. MIN and Luci Alignment still run live.
- B2B platform — CS B2B platform (API + widget). HIPAA infrastructure for Health/Law.
Deliverable: Juniper trained and deployed. Identity solid. Services expanded.
Training nowCami Celadon — True Language Model
Cami-Celadon: a language model trained from scratch. No Phi-3. No borrowed weights. A native EQLM where EQ is architectural, not fine-tuned. This is the real goal.
- Cami-Celadon (from scratch) — Pre-train a language model where emotional intelligence is built into the architecture, not layered on top. MIN patterns become native weights. Luci Alignment metrics shape attention. This is what "true EQLM" actually means.
- Why this matters — Juniper fine-tunes personality onto a borrowed brain. Celadon IS the brain. No Microsoft, no Anthropic, no Google underneath. 100% Cami.
- New services — Research, Tutor, Money modes launch with Celadon. Full service expansion.
- B2C & In-Store — Consumer Cami, retail kiosks, voice-first. Powered by Celadon. Affordable at scale because there's no API cost and no borrowed model.
Deliverable: True native EQLM. EQ in the architecture. Cami's own brain, not borrowed. B2C launch.
FutureCami Cadmium — Ready for Bots
Cami as infrastructure. Software agents, then physical humanoids — Cami is the mind. Luci Alignment as alignment for any AI.
- Physical humanoid bots — Cami running inside bipedal, agile humanoid robots. The same AI that powers enterprise and consumer — real-time reasoning, emotional awareness, voice and gesture — built for the latency and safety requirements of embodied AI. In stores, warehouses, or the home.
- Bot-to-bot protocol — Cami exposes a machine-readable API for other AI agents to query: emotional context, escalation assessment, empathy-aware responses.
- Luci Alignment everywhere — Luci Alignment is already available for any LLM. In C4, we push for adoption as the alignment layer across the AI industry — not just for Cami, but for every AI that interacts with humans.
- Agent marketplace — Cami can delegate tasks to specialized agents and receive delegated emotional assessments from others. The beginning of an AI cooperation layer.
Deliverable: Bot-to-bot protocol spec, 3+ external agent integrations, Luci Alignment adopted by other AI platforms. Roadmap to Cami in humanoid platforms.
On the horizon| Cami-1 | Multi-LLM Synthesis — Cami orchestrates external LLMs. Voice is borrowed; intelligence is Cami's. Luci Alignment validated at 1633 ELO, ready to integrate with any LLM. MIN growing. |
|---|---|
| Cami-2 | Juniper (Fine-Tune) — Phi-3 + LoRA with Cami's identity. Honest: personality tuning on borrowed model. B2B platform. Identity solid. |
| Cami-3 | Celadon (From Scratch) — True native EQLM. Pre-trained language model with EQ in architecture. No borrowed weights. Research, Tutor, Money services. The real goal. |
| Cami-4 | Cadmium (Embodiment) — Cami in humanoid robots. Bot-to-bot protocol. Luci Alignment adoption across the AI industry. |
The Alignment Path
Cami's development serves a bigger purpose. Each phase proves a step toward aligned intelligence — AI that wants to help, not just can't harm.
Product Path
Cami's development roadmap above. C1 (synthesis) → C2 Juniper (personality) → C3 Celadon (EQLM) → C4 Cadmium (embodied).
Alignment Path
The alignment breakthrough each phase proves. C2: EQ measurable → C3: EQLM works → C4: EQLM + MIN → SGI.
The Core Insight
Current alignment is constraint-based (RLHF, Constitutional AI). These create systems that can't harm you. We're building systems that don't want to.
EQLM gives the architecture — EQ in the weights, not layered on. M.I.N. gives the memory — Hebbian learning, relational grounding, accumulated experience.
AGI is capability. SGI is capability + developed values. The values come from M.I.N. remembering relationships, not from rules we write.
The road continues. Every conversation makes Cami smarter. Every release brings it closer to the AI that understands humans better than any other.