TM
TradeMasterGrowth Engine
PLATFORM GUIDE · STAKEHOLDER BRIEF

Turn a market signal into governed growth—then reuse what works.

TradeMaster is an agentic lifecycle-growth platform. It finds high-value moments, coordinates specialized agents, produces approval-ready campaigns, and closes the loop with incremental measurement.

Explore the live prototype →See how an accelerator works ↓
HOW THE PLATFORM WORKS

A closed operating loop—not another campaign dashboard.

Every step creates a durable artifact that the next step can inspect, govern, and improve.

01

Observe

Continuously qualify external demand signals, customer behavior, catalog readiness, and campaign outcomes.

02

Prioritize

Rank the next-best opportunities by urgency, confidence, addressable audience, and modeled value.

03

Generate

Coordinate specialized agents to create a complete campaign package from approved data and product facts.

04

Govern

Check claims, inventory, contact policy, event sensitivity, and audience suppressions before human review.

05

Learn

Compare treatment with a matched holdout and write the useful learning back into the next recommendation.

WHAT MAKES IT AN ACCELERATOR

Package the hard parts once. Launch the next use case with proven context.

An accelerator is more than a prompt. It is a reusable operating package that combines a trigger, approved data, agent responsibilities, guardrails, channel artifacts, and an experiment design.

EXAMPLE · WEATHER-TO-DEMAND

Forecast threshold→Eligible trade audience→Catalog-grounded campaign→Compliance gate→Matched holdout

1

Trigger

The condition that starts the workflow: weather, water levels, air quality, permit momentum, category gaps, or reorder cadence.

2

Data contract

The minimum approved inputs each agent may use, with source, freshness, and synthetic boundaries retained.

3

Agent swarm

Narrow agents for signals, segmentation, campaign creation, compliance, and measurement coordinated by one director.

4

Guardrails

Reusable brand, catalog, privacy, suppression, sensitivity, and human-approval policies.

5

Measurement

A hypothesis, primary KPI, matched control, and learning question designed before activation.

WHY THIS CHANGES THE DELIVERY MODEL

From isolated use cases to a compounding growth capability.

ONE-OFF AUTOMATION
  • Logic rebuilt for every campaign
  • Prompts and policies hidden in handoffs
  • Measurement added after launch
  • Learning stays with one team
→
ACCELERATOR MODEL
  • Reusable triggers, tools, and agent contracts
  • Evidence and governance visible by default
  • Experiment design built before activation
  • Learning improves the next recommendation
PATH FROM DEMO TO VALUE

Start narrow, prove incrementality, then scale the pattern.

NOW01

Stakeholder prototype

Live public demand signals, synthetic commerce activation, working AI orchestration, and inspectable governance.

PILOT02

Prove one accelerator

Connect approved customer and order aggregates, select one channel, define holdouts, and validate incremental impact.

SCALE03

Build the growth system

Add reusable accelerators, production channel connectors, monitoring, access controls, and a shared learning library.

PROTOTYPE DATA BOUNDARY

Real timing signals. Explicitly modeled activation.

Weather, National Weather Service alerts, U.S. Census permit momentum, USGS water gauges, AirNow AQI, and NASA satellite fire detections are live public inputs. Customer audiences, commerce behavior, revenue estimates, and experiment outcomes remain synthetic until approved production data is connected.

READY TO SCALE THE PATTERN?

Move from one governed signal to an enterprise AI operating model.

Open the control plane to inspect the cross-functional accelerator portfolio—including Water risk and Indoor air quality—alongside policy coverage, live source health, model routing, a 90-day plan, and the value scenario.

Open the AI control plane →