AI Infrastructure

Intelligence Built Into
Every Layer

Metapeak is designed from the ground up as an AI-enabled platform. Machine learning is not a feature added on top — it is the mechanism that drives auctions, optimises yield, and surfaces insights across our entire stack.

Architecture

How the AI Pipeline Works

Metapeak processes telemetry and campaign logs to power analytics and model training. Raw signals are ingested from the SDK and bidding layer, stored in object storage, and routed through a data warehouse for feature engineering.

Managed ML infrastructure handles model training and serving — producing near-real-time inference results that feed back into auction decisions, placement scoring, and the publisher dashboard.

Every optimisation decision is auditable: publishers can see which models influenced a recommendation and why, keeping the system transparent and trustworthy.

Data & ML Pipeline
Client SDK
Telemetry & ad signals
Ingest & Storage
Object store + event stream
Data Warehouse
Feature engineering & analytics
Model Training & Serving
Managed ML infrastructure
Auction Engine & Dashboard
Real-time decisions + publisher insights
Capabilities

What Our Models Do

A set of focused ML capabilities built directly into the platform, designed to improve outcomes across every layer of the monetisation stack.

CTR & CPM Prediction

Regression and classification models trained on historical campaign data forecast click-through and CPM outcomes per impression — improving auction decisions before a winner is selected.

Creative Ranking

Dynamic creative optimisation selects the best-performing creative variant per impression based on predicted engagement — reducing wasted inventory and improving user relevance.

Pricing & Bid Shading

Inventory pricing models and bid shading recommendations ensure floor prices and auction strategies adapt to real market conditions — maximising revenue without leaving demand on the table.

Anomaly & Fraud Detection

Automated flags surface abnormal traffic patterns, bot activity, and suspicious click signals — protecting publisher inventory quality and advertiser trust without manual review.

Revenue Forecasting

Short-term and monthly revenue predictions give publishers advance visibility into expected monetisation performance, enabling better planning and capacity decisions.

Demand Routing Optimisation

Per-impression partner scoring routes each request to the demand sources most likely to return competitive bids — reducing latency and improving fill without additional integrations.

AI infrastructure
Design Philosophy

AI That Earns Its Place

We apply machine learning where it genuinely improves outcomes — bid scoring, yield optimisation, anomaly detection. We don't add AI labelling to functions that are better handled by straightforward rules or configuration.

Models are trained on aggregated, anonymised campaign signals. No personally identifiable information is used for training or inference. Data retention and access controls are defined per publisher agreement and follow privacy-first principles.

Inference runs close to the auction loop — decisions are made in milliseconds, without adding meaningful latency to the ad call. Performance remains the baseline requirement; intelligence operates within it.

Data & Privacy

Metapeak processes aggregated telemetry and campaign logs. No PII is used for modelling. Data retention, access controls, and deletion policies are governed by publisher agreements and designed around GDPR-compatible principles.

No PII in training data
Aggregated signals only
Publisher-controlled retention
GDPR-compatible design
Get Started

Ready to put AI to work
on your monetisation?

Talk to our team about integrating the Metapeak platform and enabling AI-assisted optimisation for your inventory.

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