AI_LABORATORY
Experimental Benchmarking for Autonomous Agents.
This is where we take "impossible" technologies and forge them into resilient, production-ready solutions. We move beyond theoretical prototypes to engineer autonomous agent behavior in real-world environments: dynamic browsers, complex dashboards, multi-channel, and fragmented human workflows.
Universal Page Controller (UPC)
Autonomous interface control: the agent can operate web apps the way a human does—without APIs.
- UI navigation, form completion, button flows
- Context-aware task chaining ("one request" → multi-step execution)
- Resilient operation when APIs don't exist or are unstable
- Platform agnostic—works across any web application
- Simplified agentic training with minimal configuration
// Testing Environments
WHERE_AGENTS_OPERATE
Real environments, real complexity.
We test agent behavior where it actually matters—not in sandboxes, but in the messy reality of enterprise systems and human workflows.
// Active Experiments
WHAT_WE'RE_BUILDING
Live Avatars
High-fidelity digital presence designed for trust-heavy interactions where first impressions define adoption.
- Consistent tone and demeanor under stress
- Persona control (role, boundaries, escalation rules)
- Multi-channel presence (web, mobile, voice when needed)
Agentic Screen-Share
Real-time "watch + assist + act" systems for guided execution across complex workflows.
- Agents that observe a workflow and propose steps
- Optional action-taking with approvals
- Audit trail of what changed and why
Universal Page Controller (UPC)
Autonomous interface control: the agent can operate web apps the way a human does.
- UI navigation, form completion, button flows
- Context-aware task chaining ("one request" → multi-step execution)
- Resilient operation when APIs don't exist or are unstable
// Previous Projects
WHAT_WE'VE_BUILT
Interactive Widgets
Embeddable AI components that bring conversational intelligence directly into existing interfaces.
- Drop-in chat modules with custom persona configuration
- Context-aware responses tied to page content
- Analytics and conversation logging built-in
Multi-Modal Infrastructure
Unified pipelines for text, voice, image, and document processing across AI workflows.
- Seamless input/output across modalities
- Real-time transcription and synthesis
- Document parsing with structured extraction
Model Context Protocols (MCPs)
Standardized protocols for connecting AI models to external tools, data sources, and APIs.
- Tool registration and invocation framework
- Secure credential handling for third-party services
- Composable action chains with fallback logic
Anora AI
The operating system for agentic AI—orchestrating personas, memory, and execution layers.
- Unified agent runtime with persona management
- Persistent memory and context handling
- Multi-channel deployment (web, voice, API)
AI Continuity Architecture
Resilience layer ensuring uninterrupted AI operations across LLMs, CPaaS, and service providers.
- Automatic failover between model providers
- Service health monitoring and hot-swapping
- Graceful degradation with user transparency
Edge Devices for Wearable AI
Lightweight AI inference optimized for wearables, IoT, and resource-constrained environments.
- On-device model execution with minimal latency
- Privacy-first processing without cloud dependency
- Sensor fusion for contextual awareness
// Evaluation Criteria
WHAT_WE_BENCHMARK
We test agent systems against the stuff that breaks most "AI demos."
Reliability
Can it complete tasks repeatedly without flaking?
Safety
Does it stay inside boundaries and permissions?
Traceability
Can we prove what it used and what it did?
Latency + Cost
Is it usable at scale?
Human-in-the-Loop
Can the right people approve or stop actions?
// Deliverables
LAB_OUTPUTS
What you actually get from working with the Lab.
Working Prototypes
Functional systems with reproducible benchmarks—not just slideshows.
Scenario Libraries
Comprehensive test cases covering normal operations and edge cases.
Failure-Mode Reports
Documented failure conditions with guardrail recommendations.
Deployment Path
Clear roadmap from prototype → pilot → production product.
// Philosophy
WHY_THIS_MATTERS
Stress-test first, ship second.
Most teams try to "deploy AI" before they understand its failure modes. We do the opposite. The Lab exists because real-world AI deployment requires more than impressive demos—it requires systems that work reliably when things go wrong. And they always do.