Value proposition
Fast-response AI inference for conversational and agent traffic, with predictable cost profiles for scaling workloads.
Investor Relations
Qorinix is positioned as a high-speed AI inference platform that combines low latency, high throughput, and disciplined execution for a practical valuation thesis.
Investment Thesis Snapshot
We are pursuing a capital-efficient path to become one of the leading fast-response inference providers, with clear positioning against OpenAI API and Google Vertex AI on price-performance and delivery speed.
Execution thesis
Early advantage is built through software-lean operations, domain-specific model growth, and infrastructure leverage over time.
Quarterly Update (Q2 2026)
| Metric | Current | QoQ Trend | Signal |
|---|---|---|---|
| Inference Throughput | 38 baseline clusters in active use | +11% | Automated workload routing + queue calibration |
| TTFT Improvement | 17% faster median first-token | Improved | Prompt-normalization and routing refinement |
| Commercial Conversion | 11.4% pilot to paid | +3.2pp | Refined onboarding + usage governance |
| Audit & Trace Coverage | 99.6% control completion | +0.8pp | Event immutability and support trace loops |
KPI Dashboard
Benchmark, rollback, and release governance remain fixed to reduce execution drag.
Throughput-aware scheduling protects latency commitments in production windows.
Use-cases are extending from pilots into reusable domain templates.
Trial-to-paid path is validated by support response, usage telemetry, and conversion quality.
Milestone Status
Q2
Inference Runtime Release Loop
Core routing, benchmark, and replay controls reached stable production cadence.
Q2-Q3
API Observability and Usage Governance
Token, latency, and anomaly signals now feed operational and commercial review.
Q3
Domain Model Expansion
Launch of additional domain-specific LLM profiles and go-to-market templates.
Investor Factsheet
Qorinix Investor Factsheet
Download the one-page diligence brief: thesis, KPI baseline, roadmap, and key execution risks.
Progressive Roadmap
Now
Scale inference runtime and API operations while strengthening benchmark governance and usage controls.
Next
Broaden domain-specific models and workflow stacks for speed-sensitive verticals.
Long-term
Introduce specialized compute paths and chip-assisted infrastructure for sustained cost/performance advantage.
IP Program: Candidate Patent Families
Adaptive Latency Routing
Dynamic routing policy balancing queue depth, workload class, and target response profile.
Replay Integrity Protocol
Deterministic replay packaging for internal due diligence and operational consistency.
Throughput Smoothing
Batch timing and memory-window controls to reduce tail latency under burst conditions.
Tenant Isolation Controls
Resource partitioning for mixed-priority inference and policy-driven fairness.
Candidate areas are directional and for strategic discussion only; they do not represent legal commitments.
Investor Contact
For diligence, partnerships, or strategic conversations, contact the Qorinix team.