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.
Tacnode Context Lake™ keeps services, models, and agents operating on the same current context.
The Shift
The Old Era
Humans analyze data over hours. Dashboards. Meetings. Discrete decisions. Stale data and eventual consistency are acceptable. Humans reconcile differences before acting.
The New Era
AI Systems decide in milliseconds. Continuously. Stale data and conflicting context don't cause delays— they produce incorrect decisions that compound at scale.
The Goal
Decision Coherence
When multiple AI systems evaluate the same situation, they reach consistent conclusions — no conflicts, no contradictions, no reconciliation needed.
This only happens when every system acts on the same live, shared context.
The Solution
Tacnode Context Lake
A distributed context layer where AI systems operate on the same live representation of reality — no synchronization, no stale copies.
Integrated systems
Tacnode Context Lake
Shared · Live · Semantic
AI consumers
The Pillars
Shared · Live · Semantic
Context Lake is shared, live, and semantic. These three properties enable decision coherence at scale.
One shared reality
Shared
A shared context layer where all decision-makers — including stateful AI agents — operate on the same reality. No silos.
No stale context
Live
Data freshness by design. Decisions operate on current context — not delayed updates or eventual reconciliation.
Meaning built-in
Semantic
Context carries interpretations via semantic operations, so decisions don't diverge over what the data means.
Use Cases
Built for Real-Time Decisions
From fraud detection to AI agents — systems that can't afford stale data.
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
Battle-Tested
Beyond Decision Coherence — proven under production load.
Decision Guarantees
Strong consistency, high concurrency, bounded latency, and semantic correctness — the guarantees real-time decisions require.
Unbounded Elasticity
Horizontal scaling without limits — expanding and shrinking in seconds while the system remains fully online.
Workload Isolation
Multi-tenant workloads operate concurrently — batch ingestion won't slow down real-time decisions.
High Availability
Always-on with automated failover and zero-downtime upgrades — no maintenance windows, no planned outages.
Collective intelligence for your AI systems.
Enable shared, live, and semantic context so automated decisions stay aligned at scale.