Positioning

Learn how Tacnode Context Lake enables coherent AI decisions through Shared, Live, and Semantic context.

The Real Problem

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 They struggle because decisions are made with incomplete, inconsistent, or outdated context.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. Because this happens independently, concurrent decisions evaluate the same reality differently.

Shared · Live · Semantic

Tacnode Context Lake enforces three properties so decisions evaluate against the same reality:

Shared — One Shared Reality

All decision-makers retrieve context — raw and derived — from the same shared reality. No silos.

Multiple agents and services query the same state
No conflicting snapshots under concurrency
What one agent writes is immediately visible to all

Live — No Stale Context

Decisions evaluate against current context — not delayed updates or eventual reconciliation.

Data becomes queryable the instant it arrives
Features and aggregates update incrementally as data changes
No batch lag, no stale caches, no “pipeline catching up”

Semantic — Meaning as a First-Class Primitive

Context carries shared interpretations, so decisions don’t diverge over what the data means.

Embeddings, classifications, and derived features computed within the transactional boundary
All consumers share the same semantic state
Predicates and interpretations are evaluated atomically with state

Tacnode Context Lake Architecture

Tacnode Context Lake is a single integrated system — not a composition of loosely coupled services. It’s PostgreSQL-compatible and optimized for multi-cloud environments.

System Design Principles

Single Integrated System Tacnode is built as one cohesive system rather than a composition of loosely coupled services. No external reconciliation. No cross-system coordination. Guarantees hold end-to-end.

Separation of Compute and Storage Compute scales independently from storage so decision workloads can expand and shrink without moving data. This enables 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. Time-aware decisions. Correct “as-of” evaluation. Safe replay and simulation.

PostgreSQL-Compatible by Construction Tacnode is designed to speak PostgreSQL natively, so compatibility is a first-order constraint — not a translation layer.

Data Ingestion Layer

Supports a wide range of data source types to meet evolving enterprise data needs:

Relational databases: MySQL, PostgreSQL, and others
NoSQL databases: MongoDB and others for flexible storage and querying of semi-structured data
Cloud object storage: Amazon S3 for managing large-scale data storage and retrieval
Streaming data: Kafka, CDC, and event streams
IoT device data: for gathering data from various IoT devices

Data Storage Layer

Built on cloud storage resources, Tacnode’s centralized storage abstraction layer efficiently manages and schedules data storage in a multi-cloud environment:

Columnar + hybrid storage for both analytical scans and point lookups
Tiered storage: hot data in high-performance storage, cold data moves automatically to cost-effective object storage
Data backup and replication across cloud providers (AWS, Google Cloud, Azure)

Distributed Computing Layer

Features Nodegroups — Tacnode’s main computational resource:

Each Nodegroup consists of computing units that can be elastically scaled according to demand
Workload isolation ensures different workloads don’t interfere with each other
Containerization technology and cloud-native scheduling optimize resource allocation

Cloud Services Layer

Data Integration: Simplifies data inflow with built-in sync from external sources
Observability: Monitor system status, optimize performance, respond to events
Security: Data encryption, access control, audit logging, compliance
Billing: Transparent and flexible billing for services used

API and Service Layer

The top layer provides application integration and service interfaces:

PostgreSQL wire protocol for all database operations
Search service for full-text and semantic search
Real-time decision platform for stream processing and automated workflows
AI/ML integration for feature serving, RAG, and agent memory