QMatus EmblemQMatus
ECOSYSTEM & PORTFOLIO

Project Gallery.

From consumer utilities that capture real behavioral signals to bespoke enterprise architectures and foundational edge AI research.

Consumer (B2C)Phase 1

Oh That's Me (OTM)

OTM replaces biased self-quizzes with dynamic situational prompts. The deterministic BRO engine interprets behavioral signals across 72 psychological dimensions, unlocking authentic Mirror and Dipole social connections.

Architectural Highlights:
  • 72-dimension empirical trait engine (L0–L9)
  • Mirror Matching (Cosine similarity >= 0.70)
  • Dipole complementarity growth discovery
  • Dynamic rolling question prefetch queue
72
Trait Dimensions
10
Milestone Buckets
100%
State Reliability
Consumer (B2C)Phase 1

BRO Companion Engine

Decoupled into UI, orchestration, and a pure Python mathematical brain. BRO accumulates confidence without generative model drift, planned for ecosystem-wide portability across all future QMatus daily utilities.

Architectural Highlights:
  • Decoupled 3-layer architecture (UI, Orchestrator, Deterministic Brain)
  • Four relationship maturity progression stages
  • Contradiction detection and confidence convergence math
  • Zero-latency synchronous trait scoring
4 Stages
Maturity Tiers
Pure Math
Deterministic Brain
Ecosystem
Cross-App Context
Enterprise (B2B)Phase 2

Enterprise Concurrency Architectures

Tailored to high-complexity enterprise workflows, eliminating third-party AI dependencies through self-hosted inference and strictly server-locked state persistence.

Architectural Highlights:
  • Massive transaction concurrency with zero locking bottlenecks
  • Automated business intelligence pipelines
  • Server-locked Firestore security models
  • Zero corporate data leakage guarantees
>50k req/s
Throughput
<15ms
P99 Latency
0x
Vendor Dependency
Deep Tech (R&D)Phase 3

Edge-Optimized SLM Networks

Our research cell transitions from implementation to foundational IP ownership, training collaborative networks of domain-specific SLMs running locally without cloud egress.

Architectural Highlights:
  • 1.8B parameter edge-optimized architectures
  • Local multi-agent collaborative execution
  • Near-zero energy consumption envelope
  • 100% on-device private context synthesis
1.8B SLM
Target Size
0 B
Data Egress
75x
Power Reduction

Have a custom architectural challenge?

We architect bespoke solutions for organizations requiring massive concurrency and localized models.

Discuss Requirements →