Systems act on
different versions of the present.
Fraud checks, eligibility, personalization, and AI agents often evaluate stale or conflicting state. The logic isn't wrong — the systems don't agree on what's true right now.
The Shift
The Old Era
Humans run analytics, then decide. Time to review, reconcile, and align. Stale context is acceptable — humans close the gap before acting.
The New Era
AI Systems make decisions continuously — in milliseconds, concurrently, with no time to reconcile. State changes faster than it propagates. Incorrect decisions compound at scale.
Your analytics stack was designed for the old era. It wasn't built for automated decisions.
The Problem
The Context Gap
A fraud burst gets through. An approval goes out on an account already at limit. A promo is offered to someone who redeemed it seconds ago. The logic was correct — the context was wrong.
Every automated decision has a window to act. Context that doesn't arrive in time produces outcomes that can't be undone.
The Pillars
Shared · Live · Semantic
A Context Lake is defined by Decision Coherence — these three pillars are what any implementation must satisfy.
One shared reality
Shared
Every agent and service reads from the same committed state. What one sees, all see — no diverging views, no reconciliation.
No stale context
Live
Ingestion to query in sub-seconds. Decisions never run against a cached snapshot or a delayed pipeline.
Meaning built-in
Semantic
Structured queries, vector search, and feature computation in one system — no fan-out to separate engines.
The Solution
Tacnode Context Lake
A production implementation of the Context Lake specification — the singular point of truth for every automated decision.
Systems of Record
Tacnode Context Lake
Shared · Live · Semantic
Decision Systems
Use Cases
Built for Real-Time Decisions
From fraud detection to AI agents — systems that can't afford context gaps.
Fraud Detection
Evaluate every transaction against customer history and network patterns within 50ms authorization windows.
Personalization
Real-time recommendations based on live session behavior, inventory availability, and customer history.
Credit & Underwriting
Dynamic risk scoring against live context — so decisions reflect reality, not yesterday's snapshot.
Identity & Policy Enforcement
Atomic access control, rate limiting, and policy enforcement — where decisions and mutations happen in the same boundary.
Developer Experience
Speaks PostgreSQL
Connect with psql, your favorite ORM, or any PostgreSQL driver. Your SQL skills apply directly.
- Full SQL support — JOINs, CTEs, window functions
- Native vector search with pgvector syntax
- Works with existing tools, libraries, and workflows
-- Get customer context for fraud scoring
SELECT c.id, c.risk_score,
v.recent_transactions,
e.embedding <-> query_embedding AS similarity
FROM customers c
JOIN velocity_features v ON c.id = v.customer_id
JOIN embeddings e ON c.id = e.customer_id
WHERE c.id = $1
AND v.window = '15m';
-- Query latency: 4ms | Data freshness: 23msProduction Grade
Built to hold under real load.
Decision coherence at the application layer requires production guarantees at the infrastructure layer — not independent features, but what makes the three-pillar architecture viable under real load.
Explore the full productThe next decision is already running.
Make sure it sees the world as it is — not as it was.