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AGILEra
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Investor Relations · Seed Round

Challenging C3.ai and Tazi
with transparent, validated AI

Staz Media Corp is raising a $3M seed round to accelerate go-to-market for AGILEra and DDLM-69 — a patent-pending probability engine that competes directly with enterprise AI platforms at a fraction of the cost.

$3M
Seed Target
13% equity
$200B+
TAM — AI for business intelligence
Gartner 2024
v0.8.2
Production — live pipeline
Polygon · Supabase · Vercel Edge
2026
Patent filing deadline
US Prov. 63/889,131

The Problem with Current Enterprise AI

C3.ai — expensive and opaque

Enterprise licenses from $300K–$1M+/yr. 6–18 month implementations. Black-box models with no walk-forward validation. Revenue ~$310M but net loss exceeds $270M annually. Customers cannot verify model accuracy.

Tazi — narrow and static

Adaptive ML focused on insurance/financial services tabular data. Relies on historical datasets without real-time institutional signal integration. No regime-change detection. No cross-asset probability ensemble.

The Three-Product Stack

Staz Media Corp has built three interlocking products that form a complete autonomous revenue stack. Each layer feeds the next — no simulation, no duct tape.

01
DDLM-69
ich8.org
Revenue Engine

Walk-forward ML pipeline: regime detection, probability ensemble, whale flow — live every run. Patent pending US 63/889,131. Powers every downstream signal.

02
Nexus-AGILE
stazmediacorp.com/nexus
Lead Generation Layer

Signal-weighted lead scoring driven by live DDLM-69 probabilities. PIPELINE-qualified accounts are auto-routed into AGILEra CRM on threshold cross — no manual SDR queue.

03
AGILEra CRM
agilera.ai
CRM · Deal Intelligence

Leads arrive pre-scored with deal timing, churn risk, and pipeline probability already computed. Challenges Salesforce and HubSpot with validated predictive intelligence.

Our Competitive Edge — DDLM-69

Walk-Forward Validated

Every signal validated out-of-sample using walk-forward testing — the same standard used by institutional quant funds. No look-ahead bias. No overfitted backtest.

Transparent Ensemble

Multi-model ensemble (trend, mean-reversion, volatility) with dynamic Sharpe-weighted allocation. Weights are auditable, not hidden in a vendor black box.

Real Institutional Data

Polygon.io tick data + whale flow detection + Supabase event pipeline. Not synthetic, not tabular-only. Live market regime detection at sub-minute refresh.

Patent-Pending Architecture

US Provisional 63/889,131 filed Sept 26 2025. Non-provisional in process. 22 claims covering the probability ensemble architecture and adaptive recalibration method.

Deployable in Days

Vercel Edge runtime. No 6-month onboarding. No $500K consulting. Single API endpoint returns calibrated probability output with full explanation payload.

Honest Uncertainty

Every prediction includes entropy score, confidence interval, and regime flag. We quantify what we do not know — competitors do not.

Use of Funds — $3M Seed

40%
$1.2M · Engineering & ML
Full-time ML engineers, data pipeline scaling, Nexus module completion
30%
$900K · Go-to-Market
Enterprise sales, partnerships, pilot programs with financial services firms
20%
$600K · Infrastructure
Supabase scaling, Polygon tier upgrade, edge compute expansion, security audit
10%
$300K · Legal & IP
Patent prosecution, non-provisional filing, corporate structure, compliance

Founder

SS

Stanislav Severyanov — Sole Founder & Inventor

10+ years mechanical engineering — medical devices, defense, automotive, industrial systems. Patent development background. Founder of Staz Media Corp (8 years operations). Self-built the full DDLM-69 stack solo: ML pipeline, data engineering, probability engine, and all 3 production deployments. Sole inventor on US Provisional 63/889,131. Orlando, FL.

Medical DevicesDefense / IndustrialML EngineeringPatent DevelopmentFull-Stack

Ready to talk?

Serious inquiries only. Deck and technical documentation available on request.

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