You promoted what was already out of stock.
Your personalization engine makes millions of decisions per minute. But the context behind each decision is stale, scattered, or inconsistent.
Every impression against stale state is a missed conversion—or worse, a dead-end click.
< 30ms
Feature Serving
Real-time, not cached
Live
Inventory State
Stock at decision time
Current
Session Intent
Latest click, not last batch
Atomic
Multi-signal
All context coexists
The Hidden Problem
Personalization stacks are brittle under load
Most teams believe they've solved personalization latency. Kafka streams events. Redis caches features. Feature stores serve embeddings. But under load, these systems break in subtle ways—your scoring API evaluates against state that never coexisted.
Inventory lags
The cache says 50 units. There are actually 0. You promote items users can't buy.
Session state drifts
User intent shifted 3 clicks ago. Your feature store still sees "browsing." Personalization collapses.
Signals disagree
Inventory from one cache. Velocity from another. Pricing from a third. They never coexisted.
The Staleness Window
At 10M impressions/hour, a 2-second cache lag promotes 5,500 out-of-stock items per minute.
What Actually Happens
When context goes stale, conversions collapse
These aren't edge cases. They're the default failure mode of scattered, cached state.
You promoted what was already out of stock.
Flash sale launched. Your recommendation engine pulled inventory from a cache refreshed 2 seconds ago. By serving time, the top 5 SKUs had zero units. 50,000 users clicked through to 'Out of Stock' pages.
Stale inventory in the serving path creates dead-end impressions.
10,000 users saw the same 'personalized' slot.
Your feature store computed session embeddings at T-30s. The user's intent shifted from browsing to buying. Your model scored against old intent. Everyone in the cohort got generic fallback.
Lagging session state collapses personalization to batch.
The velocity feature said 'trending.' It wasn't.
Your ML model boosted items with high sales_last_5min. But that feature was computed at cache refresh—90 seconds ago. The trend ended. You promoted items nobody wants.
Stale velocity signals drive traffic to cooling SKUs.
How Tacnode Delivers
Complete · Consistent · Current
Watch a decision that would be impossible with stale, scattered state—resolved in 28 milliseconds.
Context that's all three—simultaneously
Complete
Session, features, inventory, history—all accessible together. One query, full picture.
Consistent
All data reflects the same point in time. No conflicting snapshots. No phantom state.
Current
Freshness in milliseconds. Live inventory, live session, live velocity—at decision time.
Signals that can't wait for cache refresh
Once any one of these features is fast-moving, the entire decision becomes time-critical.
Inventory & Velocity
Stock levels, sales_last_5s, clicks_last_30s—signals that capture what's happening now, not what happened at cache refresh.
Session Intent
A single click can flip intent from browsing to buying. Your decision logic must see the latest state, not the last batch.
Policy & Compliance
Fraud flags, quality holds, risk gates flip instantly. The moment they change, decisions must change.
Fulfillment Signals
Delivery slots, warehouse congestion, fulfillment latency—demand pushed into bottlenecks the system can no longer serve.
Is this your problem?
If your recommendations touch shared state that changes faster than your serving layer refreshes it, you need context that's complete, consistent, and current.
When you need this
- •Per-request personalization at scale
- •Inventory or pricing changes faster than cache TTL
- •Session state matters for scoring
- •Multiple data sources feed the decision
When you don't
- •Batch recommendations refreshed hourly
- •Static catalog with stable inventory
- •No shared mutable state between decisions
Stop recommending what's already gone
We'll walk through your personalization stack and show you where staleness creates dead impressions.