Kynetra Prime is the enterprise multi-regent self-learning intelligence operating system. PRIME orchestrates 334 specialist regents across 17 domains, compounding intelligence with every execution in a governed, reversible, provider-agnostic architecture.
+ 9 more Head Regents — 17 total domains
Every architectural decision in Kynetra Prime flows from six non-negotiable axioms.
Every regent execution feeds the learning loop. Outputs become training signal, improving accuracy with each task — intelligence compounds like interest.
Regents act within defined policy envelopes. Hard constraints always override AI decisions. Autonomy at speed, accountability always intact.
Every model update is fully versioned. Roll back any regent to any prior checkpoint in one API call. Learning is never irreversible.
17 bespoke domains, each with its own specialist pool. Regents share no context across domains — precision over generality.
Vercel, Cloudflare Workers, or self-hosted Docker. The same regent graph runs anywhere with zero code changes.
Claude primary, multi-model routing, OpenRouter fallback. Provider-agnostic by design — best model, best price, zero lock-in.
A 10-layer architecture. PRIME receives intent, routes to the domain Head Regent, which dispatches to the appropriate specialist pool. Hover to inspect.
334 specialists. Every domain purpose-built. No shared context, no diluted expertise.
Full commerce lifecycle — product catalog, pricing, cart, checkout, orders, returns.
Warehouse, shipping, carrier selection, last-mile tracking, returns orchestration.
Campaign management, A/B testing, SEO, content, affiliate, retention loops.
Identity, SSO, RBAC, zero-trust policy engine, session lifecycle management.
Real-time fraud scoring, chargeback prevention, device fingerprinting, AML.
Sentiment analysis, NPS prediction, churn risk, support deflection, VoC mining.
Multi-touch attribution, LTV modeling, cohort revenue, unit economics tracking.
Inventory forecasting, demand sensing, supplier scoring, procurement signals.
Autonomous product discovery, blueprint generation, 1 new product/day pipeline.
Recruiting, onboarding, performance, compensation benchmarking, org design.
Cash-flow modeling, AP/AR automation, FX hedging, scenario planning, audit prep.
Contract analysis, regulatory watch, GDPR/CCPA compliance, risk flagging.
Data quality, lineage, semantic layer, query optimization, dashboard automation.
CI/CD orchestration, cost optimization, incident correlation, SLO management.
Model versioning, drift detection, prompt management, evaluation pipelines.
Cross-domain quality scoring, accuracy auditing, regent performance grading.
Vendor qualification, RFP generation, contract negotiation, spend analytics.
Three interlocking systems ensure Kynetra Prime improves continuously — without human bottlenecks.
Continuously monitors every domain for accuracy decay, knowledge drift, and opportunity gaps. When a gap is detected, it automatically queues a training run to patch it — no human intervention required.
Discovers market opportunities autonomously, generates full product blueprints, and hands off to the SAF domain for execution. Target: one new deployable SaaS product per day.
Every regent execution produces a reflection payload. Within 5 minutes, improvement candidates are scored, routed for approval, and — if confidence exceeds threshold — automatically deployed.
Every regent runs the same 4-phase loop — continuously, across all 17 domains, 24/7.
Multi-modal input ingestion from APIs, databases, sensors, and user interactions — processed in real-time across all 17 domains.
10-layer cognition stack with self-critique, chain-of-thought, causal reasoning, adversarial red/blue debate, and ensemble voting.
Governed actions with risk tiers, rollback-safe operations, multi-regent approval workflows, and full audit trail.
Self-supervised feedback loops with episodic memory, cross-domain skill transfer, and daily online weight updates.
Same regent graph. Zero code changes. Three deployment targets.
8 LLM providers, 15+ models. Auto-routed per task based on capability, cost, latency, and live health.
Opus · Sonnet · Haiku
Reasoning & CodeGPT-4o · o3
Creative & VisionGemini 2.5 Pro · Flash
Long Context 2MLlama 3.3 · Mixtral
< 250ms SpeedLarge · Small
EU ComplianceCommand R+ · Embed v3
Embeddings & RAGLlama 3 70B
Open ModelsLlama 3.2 (Local)
Air-GappedPrimary — default for all regent execution
claude-sonnet-4-5, claude-opus-4, claude-haiku-3-5
Capability + cost optimisation layer
Routes by task type, token budget, and latency SLA
Universal fallback — 150+ models
GPT-4o, Gemini 2.0, Mistral, Llama 3, and more
{
synthesis: string, // human summary
outputs: any[], // structured outputs
action_items: string[], // next actions
confidence: number, // 0–1 score
metadata: {
regent, domain,
llmUsed, latencyMs,
learningSaved
}
}<span style=<span style="color:#E8C077">"color:#555555"</span>>// Kynetra Prime — provider-agnostic LLM gateway</span>
<span style="color:#E8C077">const</span> result = <span style="color:#E8C077">await</span> prime.route({
domain: <span style="color:#E8C077">"commerce"</span>,
regent: <span style="color:#E8C077">"X2C-007"</span>, <span style=<span style="color:#E8C077">"color:#555555"</span>>// Pricing Optimisation Specialist</span>
payload: { productId, signals },
llm: {
primary: <span style="color:#E8C077">"claude-sonnet-4-5"</span>, <span style=<span style="color:#E8C077">"color:#555555"</span>>// Anthropic Claude (default)</span>
fallback: [
<span style="color:#E8C077">"openai/gpt-4o"</span>, <span style=<span style="color:#E8C077">"color:#555555"</span>>// OpenRouter fallback #1</span>
<span style="color:#E8C077">"google/gemini-2.0-flash"</span>, <span style=<span style="color:#E8C077">"color:#555555"</span>>// OpenRouter fallback #2</span>
],
maxTokens: 4096,
temperature: 0.2,
},
});
<span style=<span style="color:#E8C077">"color:#555555"</span>>// DomainResult contract</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// result: {</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// synthesis: string, // human-readable summary</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// outputs: any[], // structured regent outputs</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// action_items: string[], // prioritised next actions</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// confidence: number, // 0–1</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// metadata: {</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// regent, domain, llmUsed, latencyMs, learningSaved</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// }</span>
<span style=<span style="color:#E8C077">"color:#555555"</span>>// }</span>The difference between an intelligence regent and an AI tool is the same as between an OS and a calculator.
All 334 regents share a unified knowledge graph. A commerce insight propagates to fraud, supply, and finance automatically.
Not a demo environment — PRIME was built inside $1B+ GMV commerce, live legal compliance, and ML-ops pipelines.
Every action has a risk tier, approval gate, and rollback path. Regents never act unilaterally on high-stakes decisions.
Daily training cycles automatically close knowledge gaps. Average +4.2% accuracy gain per weekly cycle across all regents.
8 LLM providers, 15+ models. Anthropic for reasoning, Groq for speed, Google for long context — auto-routed per task.
Commerce to compliance, HR to DevOps — 17 domains means you never hit a capability wall. The full stack, in one system.
The same stack powering 40+ platforms and 200+ enterprise clients.
Kynetra Prime is HyperBridge's internal intelligence regent. We're opening it to the public in early 2027. Join the waitlist for first access and early-partner pricing.