The Decision Engine
Seven autonomous agents.
One brain.
Replenit's retail agents don't run in parallel — they collaborate. Maestro coordinates seven specialists, each reasoning at the individual customer level, to drive every revenue, retention, and recovery decision in real time.
Replenishment · Predictive Timing
Predict the exact moment each customer is about to run out — and trigger the reorder before they think about it.
The decision
“Olaplex No.4 depletion predicted within 24 hours”
Hydrating Cleanser 250ml
Depleted in 24h
How others do it
Fixed delays such as "send after thirty days" apply the same timing for everyone, regardless of actual consumption.
How Replenit reasons
Replenit predicts each customer's consumption speed for every product and triggers at the exact time they are likely to run out.
Cross-Sell · Contextual Reasoning
Surface complementary products with reasoning — not random pairings — at the exact moment the customer needs them.
The decision
“Hair straightener buyer with no heat protection in care routine”
Anchor
GHD Platinum+
$279
Paired
Olaplex No.7
$30
Reason: Heat damage gap in routine
+52% AOVHow others do it
Generic recommendation widgets show the same add-ons to every visitor. No reasoning, no timing, no awareness of where the customer is in their journey.
How Replenit reasons
Replenit reasons about each customer's purchase history and timing to surface the right complementary product at the right moment.
Engagement · CLTV Recovery
Detect declining customer lifetime value early — and craft the engagement that reverses the trajectory before it churns.
The decision
“Customer CLTV dropped 22% in 90 days across 3 categories”
CLTV (90 days)
recovery initiated
How others do it
Static lifecycle flows run the same cadence regardless of whether a customer's value is growing or declining.
How Replenit reasons
When a customer's CLTV begins to drop, Replenit analyzes their motives, preferences, and timing to craft the engagement that reverses the trajectory.
Product Churn · Early Signal Detection
Spot when a customer silently drops a product — even while they remain active with your brand — and intervene at the SKU level.
The decision
“High churn risk — Huggies Snugglers depleted 62 days ago”
Active
Pampers Pure
cadence: 22d
At risk
Huggies Snugglers
gap: 62d (2.5×)
● Active brand customer — but silently dropped one SKU
How others do it
Retention reacts to absence after it is too late. The same "we miss you" email goes to everyone inactive past 90 days.
How Replenit reasons
Replenit detects early churn signals by analyzing each customer's purchasing habits and product consumption patterns weeks before they disengage.
Winback · Contextual Reactivation
Bring dormant customers back with the right product paired with the right offer — not a blanket discount blast.
The decision
“Dormant 120 days · strong furniture affinity · 74% reactivation odds”
Dormant
120days
Reactivation
74%
Söderhamn Sofa Section
+ 12% targeted incentive
How others do it
Blanket discount blasts to anyone inactive past a threshold. No personalization, no timing logic, no product context.
How Replenit reasons
Replenit identifies the optimal reactivation window and selects the right product and promotion combination to bring the customer back.
Promotion · Optimal Moment
Decide when a discount is worth it — and exactly how deep — without eroding margin on customers who'd convert anyway.
The decision
“Engagement agent requested intervention · 15% optimal bracket”
Discount brackets
optimal selected
How others do it
Calendar-driven promotions sent in bulk, treating every customer as equally price-sensitive at the same time.
How Replenit reasons
Replenit identifies the precise moment a promotion adds value, when a customer needs the product anyway.
Substitute · Intelligent Alternatives
Turn every out-of-stock moment into recovered revenue with a substitute the customer actually wants.
The decision
“Replenishment agent detected out-of-stock product”
Out of stock — find a fit
Sold out
Olaplex No.4
Bond Shampoo
Matched
Olaplex No.5
Conditioner — same brand affinity
How others do it
When a product is unavailable, customers see a generic "out of stock" page or unrelated alternatives.
How Replenit reasons
Replenit uses synthetic data generation to reason about each customer's preferences and identify the most relevant substitute.
One brain
The agents collaborate
A single customer signal cascades across multiple agents. Maestro picks the right specialist, in the right order, with the right rationale.
Product Churn
detects silent drop
Engagement
crafts the engagement
Promotion
sizes the offer
one of millions of cross-agent handoffs Maestro orchestrates daily
Deploy seven agents in days, not quarters.
No journey builders. No segments to maintain. Replenit's Maestro coordinates every decision the moment a customer signal arrives.