Tacnode Context Lake
for coherent AI decisions
Tacnode maintains a shared, live, and semantic context so AI systems make coherent decisions under concurrency — ensuring your agents and apps never have to act on incomplete, conflicting, or outdated context.
The real problem
This is not caused by data fragmentation
Modern systems don't struggle because data is split across databases. They struggle because decisions are made with incomplete, inconsistent, or outdated context.
Services, agents, and applications act continuously while state is still changing. Traditional data systems were designed for settled state — write first, read later — not for decisions made mid-flight.
Transactional state lives in OLTP databases. Features and aggregates are computed elsewhere. Search and retrieval run in separate engines. Over time, data spreads across systems as a workaround.
When no system owns decision-time data, teams reconstruct meaning in application code, pipelines, and glue logic. At that point, data becomes context. Because this happens independently, concurrent decisions evaluate the same reality differently.
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When decision-time context is incomplete, inconsistent, or outdated, systems produce confident but incorrect outcomes.
Built for the machine-driven era
For teams building AI-native applications where agents and services make decisions continuously
AI Agent Teams
Build multi-agent systems that act from a shared, continuously updated context.
ML Engineers
Build and operate models that act correctly on shared, decision-time context.
Data/Context Engineers
Define and maintain shared state, entities, and features so meaning stays consistent at decision time across systems.
Platform/Infra Engineers
Make correctness and coordination under concurrency a system guarantee, not an application burden.
Built-in guarantees
Shared · Live · Semantic
Tacnode enforces these properties so decisions evaluate against the same reality.
One shared reality
Shared
All decision-makers retrieve context — raw and derived — from the same shared reality. No silos.
No stale context
Live
Decisions evaluate against current context — not delayed updates or eventual reconciliation.
Meaning as a first-class primitive
Semantic
Context carries shared interpretations, so decisions don't diverge over what the data means.
System Architecture
A unified boundary for semantic operations, transactional state, and temporal guarantees
Explore Workloads
Dive into each workload — Agent Context Layer, Feature Store, Realtime Data Warehouse — and see how Context Lake enables Decision Coherence.
View WorkloadsTechnical Documentation
API references, integration guides, and the canonical document on Decision Coherence.
View DocsCollective intelligence for your AI systems.
Enable shared, live, and semantic context so automated decisions stay aligned at scale.