AI Models
Foundation models and custom fine-tunes
The frameworks that make AI feel inevitable—orchestration, memory, personality, and decision-making architecture.
Invisible architectures for agents, workflows, and design logic.
We don't build software—we architect intelligence. Every system we create is designed to learn, adapt, and refine itself through use.
Our frameworks are the invisible infrastructure of tomorrow's creativity—the cognitive scaffolding that makes intelligence feel effortless.
Explore the core frameworks that power adaptive, context-aware systems.
Systems that route, prioritize, and coordinate multiple AI agents autonomously.
Without orchestration, AI is reactive and isolated. With it, systems understand context, delegate tasks, and coordinate multi-step workflows.
Multi-source data orchestration with AI analysis
Click "Watch Demo" to see intelligent workflow coordination
Persistent, semantic understanding that remembers patterns, preferences, and context across all interactions.
Traditional AI treats each conversation as isolated. Our memory systems track emotional states, user preferences, conversation history, and learned patterns.
// Session 1 - Day 1
user: "I want to work on my anxiety"
ai: "Let's explore that. What triggers
your anxiety most?"
// Session 5 - Week later
user: "I'm feeling better"
ai: "That's great! Have the breathing
exercises helped with those work
presentation triggers we discussed?"
// Remembers:
// - Main goal (anxiety management)
// - Specific triggers (work presentations)
// - Recommended techniques (breathing)
// - Progress over time Brand-aware AI that maintains consistent voice, tone, and decision-making across all touchpoints.
Most AI sounds generic. Our personality systems encode brand identity, communication style, and behavioral patterns into the model itself.
Ophelin AI Development Agent
Click "Start Demo" to watch autonomous code generation
Intelligent routing and prioritization that knows when to act, escalate, or defer.
AI shouldn't always respond. Our decision systems evaluate confidence, urgency, and risk to determine the right action.
const decision = await ophelin.decide({
query: "I'm having suicidal thoughts",
context: currentSession,
agents: [chatAgent, crisisAgent]
})
// Decision system evaluates:
// - Urgency: CRITICAL
// - Confidence: HIGH
// - Risk: SEVERE
// Result:
decision.action = 'ESCALATE'
decision.route = crisisAgent
decision.priority = 'IMMEDIATE'
decision.humanReview = true Before and after examples showing the impact of intelligent systems.
// Every request is isolated
user: "Schedule a session"
ai: "What type of session?"
user: "Therapy"
ai: "What day works for you?"
user: "Thursday"
ai: "What time?"
// No memory, repetitive questions // Persistent context & memory
user: "Schedule a session"
ai: "I'll book your usual CBT session
with Dr. Chen. How about Thursday
at 3pm, like last time?"
// Remembers:
// - Session type preference
// - Therapist preference
// - Usual day/time patterns // Hard-coded logic
if (intent === 'therapy') {
return therapyBot(message)
} else if (intent === 'journal') {
return journalBot(message)
} else if (intent === 'schedule') {
return scheduleBot(message)
}
// Brittle, hard to maintain // Semantic routing
const system = ophelin.orchestrate({
agents: [therapyAgent, journalAgent, scheduleAgent],
routing: 'intent-based',
memory: 'shared'
})
// Automatically routes based on:
// - User intent (semantic understanding)
// - Current context
// - Agent availability The technologies behind intelligent systems
Foundation models and custom fine-tunes
Agent coordination and workflow management
Deployment and scaling
User interfaces and experiences
We choose technologies for reliability and performance, not hype. Every tool in our stack is battle-tested in production.
How ideas flow through our system
Philosophy & Research
Technology & Infrastructure
Expression & Collaboration
Ideas → Systems → Experiences → Back to Ideas
Every system learns, adapts, and refines itself through use.
We don't believe in static products. Intelligence is dynamic—it observes, learns, and improves. Our systems are designed to evolve alongside the humans and machines that use them.