Tacnode Instant Lakehouse
Slow Data Delays Decisions.
Act in Milliseconds.
Start ITERATing with Tacnode— go from data to decisions in milliseconds, at any scale. Unify transactions and analytics in a PostgreSQL-compatible platform to power AI, streaming insights, and real-time applications.
A True Unification of Data Management Systems
No more data silos, architectural complexities, unpredictable latencies and unavoidable inconsistencies.
Instant Lakehouse
Real-time at scale without compromise on performance
- Sub-second application driven analytics
- Zero-latency data freshness
- Unlimited linear scalability
Online Retrieval
Omni-search for modern AI applications
- Real-time vector and semantic search
- Hybrid search across all data types
- Zero-latency index updates
PostgreSQL Compatible
Built on PostgreSQL's proven architecture with high compatibility
- Compatible with PostgreSQL tools
- Growing ecosystem of supported extensions
- Optimized for both transactional and analytical workloads
Built for the Cloud
Designed and built for modern distributed infrastructure
- Instant elasticity for dynamic workloads
- Complete workload isolation
- Cloud-agnostic by design
A Better Lakehouse
Stand out with real-time analytics — unmatched performance, strong consistency, zero data latency.
User Retention Analysis
A leading digital advertising SaaS platform
8x
Average performance improvement
Sales and ERP Data Analysis
A trending global e-commerce group
100x / 9x
Max / Avg query time reduction
IoT Data Processing and Analysis
A global communication services conglomerate
9x / 4x
Throughput improvement / Cost reduction
A Better Database
Better Relational. Better NoSQL.
Single Point Of Truth
One home to all data your applications will ever need:
StructuredSemi-structuredUnstructuredAlways consistentAlways in real timeAlways authoritativeUnlimited scalabilityHigh availability and reliabilityCentralized management
Bring AI to Your Data
Extract knowledge to discover insights.
By training/feeding AI with your private data:
Fine-tuning/Retrieval-Augmented Generation (RAG).
Make real-time predictions and decisions.
Use cases:
By training/feeding AI with your private data:
Fine-tuning/Retrieval-Augmented Generation (RAG).
Make real-time predictions and decisions.
Use cases:
Knowledge baseIntelligent recommendationsOmni search
create-embeddings.sql
insert.sql
CREATE TABLE embeddings ( id SERIAL PRIMARY KEY, text VARCHAR(255) NOT NULL, embedding VECTOR(768) NOT NULL, create_date DATE DEFAULT CURRENT_DATE);
INSERT INTO embeddings (text, embedding) VALUES ('This is a sample text.', '[0.1, 0.2, ..., 0.768]'),('Another example text.', '[0.3, 0.4, ..., 0.768]'),
SELECT * FROM embeddings;
id | text | embedding | create_date----+------------------------+------------------------+------------- 1 | This is a sample text. | [0.1, 0.2, ..., 0.768] | 2023-08-22 2 | Another example text. | [0.3, 0.4, ..., 0.768] | 2023-08-22(3 rows)