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Use Case

Real-Time Personalization

When every page view triggers a personalized recommendation rendered immediately to the user, there is no downstream reconciliation. Once the recommendation is emitted, the system has already acted.

28 milliseconds of coherence

From click to personalized result—watch how real-time context enables decisions that would be impossible with stale data.

Start
User browses
Session begins
StartComplete
Input
Studio Headphones
Premium Audio
Tacnode Context Lake™
Session
buying intent
Stock
12 left
Promo
20% off
Affinity
premium
Output
Wireless Earbuds
$79
Studio Headphones
$199
20% OFF
Portable Speaker
$34
Served to user
Synchronizing context…

The stakes are immediate

At scale, recommendation is not an exploratory query. It is the decision.

That decision determines which products receive exposure, which items accumulate velocity, how inventory is depleted, how demand propagates through fulfillment, and which sellers benefit or suffer.

Errors are not abstract. They materialize instantly as missed revenue, degraded user trust, and operational imbalance. There is no slack in this system. No retries. No eventual correction.

Operating at scale

Within millisecond latency budgets, determine what logic to apply and what state to evaluate—then commit to an outcome that immediately affects shared resources.

Millions

Active products

Millions

Concurrent sessions

Millions/min

Recommendation decisions

Milliseconds

End-to-end latency

Semantic interpretation drives logic

Two users viewing the same catalog generate structurally different decision predicates based on real-time session interpretation.

Session: Price-Sensitive

inventory_available > 0

AND price < 50

AND sales_last_30s > 5

AND competitors_in_stock < 3

Session: Quality-Sensitive

inventory_available > 0

AND review_rating_last_100 > 4.5

AND return_rate_last_7d < 0.03

AND seller_reliability > 0.95

These are not parameterized variants of a fixed query template. They reference different features and encode different decision logic. The structure itself is derived from semantic interpretation of session history.

Fast-moving, decision-gating features

Once any one of these features is fast-moving and decision-gating, the entire predicate becomes time-critical. Evaluating parts against stale state means deciding against a combination of facts that never coexisted.

Inventory & Velocity

Stock levels, sales_last_5s, clicks_last_30s, trend acceleration—signals designed to capture what is happening now.

Session Intent

A single click can flip intent from browsing to buying, activating different constraints and feature sets in real-time.

Policy & Compliance

Fraud flags, quality holds, risk_category gates flip instantaneously. 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.

Coherence requires

Shared · Live · Semantic

Correct personalized recommendations at scale require three properties to hold simultaneously within a single decision boundary:

S

Shared

All agents query the same state—no conflicting snapshots under concurrency

L

Live

Decisions reflect current reality—millisecond freshness, not eventual consistency

S

Semantic

Context is understood, not just stored—logic applies based on meaning

Tacnode Context Lake delivers Decision Coherence by unifying these properties within one system, rather than attempting to coordinate multiple systems whose guarantees stop at their boundaries.

Ready to see it in action?

Book a demo to explore how Tacnode Context Lake delivers Decision Coherence for real-time personalization.