AI Agents for Revenue Growth

Who We Are

Built by operators who lived the problem

Former operators at Amazon and Google with deep expertise in pricing, demand, and inventory systems — now building AI that learns continually from real outcomes.

10+

Years in Pricing Systems

$B+

Revenue Managed

Learning-First Approach

100%

Human-in-the-Loop

The Reality

What Customers Tell Us

Revenue rarely disappears in one big moment. It leaks quietly — through delays, missed signals, and decisions made too late.

Pricing Reacts Too Slowly

Cost shocks, demand changes, and capacity shifts move faster than your pricing systems can respond.

Signals Arrive Too Late

Teams see outcomes, not early warning signals. By the time you react, the window has closed.

Feedback Loops Are Broken

Decisions fail downstream in ops, inventory, or compliance. Learning happens too late — or not at all.

The Insight

"The constraint isn't data or intent — it's the lack of a learning layer between pricing actions and real-world outcomes." - Head of Commercials, Fortune 500 CPG

Our Approach

Learning Faster > Predicting Perfect

Most pricing programs try to optimize a price point. We help you learn from real outcomes.

The Old Model

• Assumes stable demand curves
• Requires full visibility across channels
• Infrequent, batched decisions
• Price-match with rigid constraints

WHAT WE DO

Learning-First

Smaller, scoped pricing actions
Executed more frequently
Designed to learn from outcomes
Guard-railed & human-approved

What This Enables

→ Faster response to imbalance
→ Higher confidence in pricing moves
→ Reduced reliance on assumptions
→ Direct impact on revenue & margin

See It In Action

How Our Agents Work

From signal detection to action — with humans in control at every step.

Sense Signals

The agent continuously observes changes across pricing, demand, inventory, and cost signals as new data becomes available.

Understand What Matters

The agent analyzes these signals to identify where value may be at risk, explains why it matters, and highlights areas worth attention — in plain language.

Recommend Controlled Actions

The agent proposes small, scoped pricing actions designed to test specific hypotheses, always within defined guardrails and requiring human approval.

Learn From Outcomes

As results come in, the agent measures real-world impact, updates its understanding, and improves future recommendations over time.

Learning compounds when decisions are structured, measured, and repeated.

Our Philosophy

How We Build Revenue Agents

Principles we design and ship by.

Purpose-Built Agents

Built for specific revenue problems — not generic AI.

Human-Approved Actions

Every action stays within guardrails and human control.

Learning Over Autonomy

We help teams learn faster, not hand control to black boxes.

Outcome-First Metrics

Revenue and margin impact over dashboards and vanity KPIs.

We build disciplined systems that help teams make better decisions, sooner — and actually execute them.

Put an Agent to Work on a Real Revenue Problem

We deploy agents into a small number of live environments at a time.

Based on evidence, not promises.

Revenue growth doesn't come from modeling harder.