ืฉื™ื•ื•ืง ืžื‘ื•ืกืก AI

ื”ืฉื™ื’ื• ื™ื•ืชืจ ืœืงื•ื—ื•ืช ื‘ืคื—ื•ืช. ื‘ืื•ืคืŸ ืื•ื˜ื•ืžื˜ื™.

ืฆืคื• ื‘ืœื•ื— ื”ื‘ืงืจื” ืžื‘ื•ืกืก ื”-AI ืฉืœื ื• ืžื ืชื— ื‘ื™ืฆื•ืขื™ื ื‘ืื•ืคืŸ ืจืฆื™ืฃ ื‘ื›ืœ ืขืจื•ืฆื™ ื”ืคืจืกื•ื ื•ืžืขื‘ื™ืจ ืชืงืฆื™ื‘ ื‘ืื•ืคืŸ ื“ื™ื ืžื™ ืœืžืงื•ืžื•ืช ืฉืžื ื™ื‘ื™ื ืืช ื”-ROI ื”ื’ื‘ื•ื” ื‘ื™ื•ืชืจ ื•ืืช ื”ืœื™ื“ื™ื ื”ื–ื•ืœื™ื ื‘ื™ื•ืชืจ.

PythonTensorFlowNext.jsGoogle Ads APIMeta Marketing APIBigQuery
Live Simulation

The ROAS Multiplier

Watch the AI detect that Google Ads outperforms Facebook, then dynamically reallocate budget to maximize your return on ad spend.

Daily Spend

โ€”

Overall ROAS

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AI Engine

STANDBY

Budget Allocation

Awaiting optimization cycle...

Architecture Deep-Dive

Attribution & Prediction Engine

Multi-touch attribution data flows into a TensorFlow model that predicts future ROAS and outputs optimal per-channel budget allocations.

Customer Journey Data

Multi-touch attribution

AI Prediction Model

TensorFlow / PyTorch

Optimal Budget Allocation

Per-channel output

Beyond last-click attribution: Our AI predicts future ROAS to optimize spend proactively.

ืื™ืžืคืงื˜ ืขืกืงื™

ืžืกืคืจื™ื ืžื“ื•ื™ืงื™ื

ืชื•ืฆืื•ืช ืžื”ื˜ืžืขืช ื”-AI Ad Optimizer ืขื‘ื•ืจ ืžื•ืชื’ D2C ืฉืžื•ืฆื™ื $30,000 ื‘ื—ื•ื“ืฉ ื‘-Google, Facebook ื•-TikTok.

+40%

ืขืœื™ื™ื” ื‘ื”ื—ื–ืจ ืขืœ ื”ื•ืฆืื•ืช ืคืจืกื•ื

ื”ืงืฆืื” ืžื—ื“ืฉ ืžื•ื ืขืช AI ืžื–ืจื™ืžื” ืชืงืฆื™ื‘ ื‘ืื•ืคืŸ ืจืฆื™ืฃ ืœืขืจื•ืฆื™ื ื‘ืขืœื™ ื”ื‘ื™ืฆื•ืขื™ื ื”ื’ื‘ื•ื”ื™ื ื‘ื™ื•ืชืจ

-25%

ื”ืคื—ืชื” ื‘ืขืœื•ืช ืœื”ืžืจื”

ืžื•ื“ืœื™ื ืฉืœ ื ื™ื‘ื•ื™ ืžื–ื”ื™ื ืืช ื ืชื™ื‘ื™ ื”ืจื›ื™ืฉื” ื”ื–ื•ืœื™ื ื‘ื™ื•ืชืจ ืขืœ ืคื ื™ ื”ืคืœื˜ืคื•ืจืžื•ืช ื”ืฉื•ื ื•ืช

0ย ืฉืขื•ืช

ื ื™ื”ื•ืœ ืชืงืฆื™ื‘ ื™ื“ื ื™

ืžื—ื–ื•ืจื™ ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืื•ื˜ื•ืžื˜ื™ื™ื ืœื—ืœื•ื˜ื™ืŸ ืžื—ืœื™ืคื™ื ืฉืขื•ืช ืฉืœ ื ื™ืชื•ื— ื™ื“ื ื™ ื‘ื’ื™ืœื™ื•ื ื•ืช ืืœืงื˜ืจื•ื ื™ื™ื

ื ืžืืก ืœื›ื ืœื‘ื–ื‘ื– ืชืงืฆื™ื‘ ืคืจืกื•ื?

ืื ื—ื ื• ื‘ื•ื ื™ื ืœื•ื—ื•ืช ื‘ืงืจื” ืžื‘ื•ืกืกื™ AI ืฉืžื‘ืฆืขื™ื ืื•ืคื˜ื™ืžื™ื–ืฆื™ื” ืื•ื˜ื•ืžื˜ื™ืช ืœื”ื•ืฆืื•ืช ื”ืคืจืกื•ื ืฉืœื›ื ื‘ื›ืœ ืคืœื˜ืคื•ืจืžื” โ€” ื›ื“ื™ ืฉื›ืœ ื“ื•ืœืจ ื™ืขื‘ื•ื“ ืงืฉื” ื™ื•ืชืจ.

ื‘ื•ืื• ื ื“ื‘ืจ