Real-Time AI
Context Infrastructure
Tacnode Context Lake is a distributed context infrastructure that keeps AI agents and services operating on the same shared, live, and semantic context.
No composition required. No stale decisions. Every automated decision evaluates the same current version of reality.
The problem
Why existing data stacks break under AI workloads
Data was designed for settled state
Traditional data systems were built for decisions made after the fact — write first, read later. Transactional state lives in OLTP. Features and aggregates are computed in a warehouse. Search runs in a separate engine. These systems are optimized for throughput and cost, not for queries made while state is still changing.
Teams reassemble context in application code
Because no single system owns decision-time data, teams stitch it together in application logic, pipelines, and glue code. A fraud check fans out to three systems. A personalization model assembles its own context before scoring. Every team maintains its own view of the same reality — maintained separately, aging at different rates.
Concurrent decisions evaluate reality differently
When AI agents and services each assemble context independently, they don't evaluate the same state — they evaluate their own version of it. Two parallel fraud evaluations can reach different conclusions about the same transaction. An agent acts on inventory that another agent already consumed. What changed isn't data volume or query latency. It's when and how decisions happen.
"Fragmentation is the outcome, not the cause."
When decision-time context is incomplete, inconsistent, or outdated, systems produce confident but incorrect outcomes.
How it works
Context Lake Architecture
How Tacnode Context Lake stores, evaluates, and serves authoritative decision context.
Shared Context. Supercharged Agents.
All data. Any agent or app. Powered by live context, at any scale.
The Pillars
Three pillars. One system.
Each pillar is necessary. None is sufficient alone.
Shared context without live freshness means agents agree on the wrong answer — they coordinate correctly but act on outdated state. Live context without shared consistency means agents each see reality, just different moments of it. Semantic context without the other two means meaning is computed over stale, inconsistent state — precise but wrong.
The three pillars form a single constraint. A context layer that delivers all three simultaneously is what makes decision coherence possible. Tacnode enforces this within one system boundary — not through coordination between three separate systems.
Shared Context
A shared context layer where all decision-makers — including stateful AI agents — operate on the same reality. No silos.
Live Context
Data freshness by design. Decisions operate on current context — not delayed updates or eventual reconciliation.
Semantic Context
Features, aggregates, vector search, and LLM-derived signals — all computed and queryable in one unified system.
The composition problem
You can't compose coherence from separate systems
A feature store, a lakehouse, a vector database, and a cache each cover part of the problem. But none of them know about the others — and that's exactly what causes the incoherence Tacnode is designed to eliminate.
Feature Store
Online feature servingNo transactional reads, no vector search, no shared consistency across consumers.
Data Lakehouse
Analytical queries and batch ETLMinutes-old data. No strong consistency. Not designed for sub-second decision latency.
Vector Database
Semantic search and embeddingsIsolated from structured state. No consistency guarantees with the data it relates to.
Cache
Low-latency readsStale by design. No semantic layer. Drift is a feature, not a bug.
When these systems operate independently, each serves its own version of state — computed at different times, with different consistency guarantees, with no shared transaction boundary. You can assemble all four and still have the coordination problem. Coherence can't be bolted on from the outside. It has to be enforced within a single system boundary.
Workloads
What you can build with Tacnode Context Lake
All workloads run on the same underlying system and share the same consistency, freshness, and semantic guarantees.
AI Agent Memory
AI agents operate in continuous loops: observe, decide, act, repeat. When the context an agent reads doesn't reflect current system state, it plans against a reality that no longer exists — triggering re-plans, token waste, and cascading failures across parallel workloads. Tacnode externalizes agent memory into shared infrastructure. Writes are atomic, reads are consistent, and what one agent learns all agents see instantly.
Explore workloadFeature Store
Most feature stores compute in batch, materialize elsewhere, and sync into serving systems. By the time your model reads a feature, it reflects a past state — not decision-time truth. Two parallel evaluations can read the same stale value and both approve what only one should. Tacnode computes and serves features directly on live system state. Features update as source data changes — not when pipelines run.
Explore workloadDecision-Time Analytics
Operational decisions driven by analytics are only as good as the analytics' freshness. When the aggregate your control system reads lags the events that produced it — even by seconds — decisions operate on incomplete state. At 20K RPS, a 200ms coordination gap admits thousands of actions before downstream systems react. Tacnode collapses ingestion, transformation, and serving into one transactional boundary where all consumers read from the same committed state.
Explore workloadEngineering
Why decision coherence is possible with a context lake
Structural decisions that define how Tacnode Context Lake is engineered — and why the three-pillar architecture holds under real production load.
Single Integrated System
Tacnode is built as one cohesive system rather than a composition of loosely coupled services. Consistency, freshness, and semantic guarantees hold end-to-end — not just within each service boundary.
Separation of Compute and Storage
Compute scales independently from storage. Decision workloads expand and shrink without moving data, enabling workload isolation: independent agents and services run concurrently without interfering with each other's latency or throughput.
Temporal Versioning by Construction
All state is versioned over time as part of the core system — not reconstructed from logs or replay pipelines. This enables time-aware decisions, correct as-of evaluation, and safe replay and simulation without external tooling.
PostgreSQL-Compatible by Construction
Tacnode exposes a PostgreSQL wire protocol natively — compatibility is a first-order design constraint, not a translation layer. Any PostgreSQL client, ORM, BI tool, or driver connects without modification.
Production Grade
Production guarantees for Tacnode Context Lake
Decision coherence at the application layer requires production guarantees at the infrastructure layer. These aren't independent features — they're what makes the three-pillar architecture viable under real load.
Decision Coherence
Strong consistency, live freshness, and semantic correctness — the three guarantees that keep real-time decisions aligned across concurrent consumers.
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.
"Minutes to hundreds of milliseconds."
A large-scale food delivery platform uses Tacnode Context Lake to achieve sub-second end-to-end reactivity — reducing the time from a customer action to usable backend context from minutes to hundreds of milliseconds, powering in-session personalization that reacts to consumer intent as it forms.
Leading food delivery platform — millions of customers
<1s
End-to-end reactivity
Minutes → ms
Context latency reduction
Millions
Customers served
FAQ
Common questions about Tacnode Context Lake
What is a context lake?
A context lake is a distributed infrastructure layer that closes the context gap — the inability of decision systems to access complete, consistent, and current context within their validity window. It provides shared, live, and semantic context for AI agents and services making real-time decisions. Unlike a data lake — optimized for analytical storage — or a feature store — optimized for ML feature serving — a context lake is built for the moment of decision: when multiple systems must act on the same current state simultaneously, with strong consistency and sub-second freshness.
How is Tacnode different from a feature store?
Feature stores serve precomputed features to ML models at serving time, but they don't provide strong consistency across concurrent reads, don't support transactional semantics, and don't unify structured queries with vector search. Tacnode maintains all three — live features, relational state, and semantic search — under a single consistency model, so concurrent evaluations always see the same committed snapshot.
Does Tacnode replace my existing database?
Tacnode operates alongside your operational database as a purpose-built context layer. Your OLTP database continues handling transactional writes. Tacnode maintains a continuously-updated, semantically-enriched view of that state optimized for real-time decision queries across multiple concurrent consumers — eliminating the need to fan out reads across separate systems.
How does Tacnode handle data freshness?
Tacnode ingests changes from streaming and batch sources and makes them immediately queryable — no intermediate staging, no ETL delay. The result is sub-second end-to-end freshness from source event to queryable context. In production deployments, end-to-end context latency has been reduced from minutes to hundreds of milliseconds.
What workloads does Tacnode support?
Tacnode is designed for three primary decision-time workloads: AI agent memory (shared, durable state across agent loops), feature serving (live feature computation with no training-serving skew), and decision-time analytics (low-latency queries over continuously-updated operational state). All three run on the same underlying system with the same consistency and freshness guarantees.
Is Tacnode available on AWS?
Yes. Tacnode Context Lake is available on AWS Marketplace, enabling deployment directly within your AWS environment with private network connectivity via PrivateLink and integration with existing AWS data infrastructure.
What does PostgreSQL-compatible mean?
Tacnode exposes a PostgreSQL wire protocol, meaning any PostgreSQL client, ORM, BI tool, or driver connects without modification. Your existing SQL queries and application integrations work as-is. Compatibility is a first-order design constraint — not a translation layer — so behavior is consistent and predictable.
How does a context lake differ from a data lakehouse?
A data lakehouse is optimized for analytical queries over historical data — batch-oriented, eventually consistent, and built for throughput. A context lake is optimized for the moment of decision — sub-second freshness, strong consistency across concurrent readers, and unified structured and semantic queries. They serve different parts of the data stack: lakehouses for after-the-fact analysis, context lakes for in-flight decisions.
Can Tacnode replace Redis or a dedicated cache for AI context?
Redis and dedicated caches provide fast reads but are stale by design — they serve a copy of state, not current state. They have no consistency model across concurrent consumers, no semantic layer, and no transactional semantics. Tacnode is not a cache: it maintains a live, strongly-consistent view of context that all consumers read from simultaneously, with full SQL and vector search support.
Explore
Where to go next
Collective intelligence for your AI systems.
Enable shared, live, and semantic context so automated decisions stay aligned at scale.