Point of Data
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The reality understanding layer

Helping AI agents
understand
actual reality.

Turn real-world signals into the context, memory, and intelligence AI agents need to make better decisions — and take meaningful action.

NEURAL MEMORYANY AGENT STACKYOUR CLOUDFULLY TRACEABLE
SIGNALS → CONNECTIONS → UNDERSTANDING
EARLY ACCESS OPEN — DESIGN PARTNERS BUILDING WITH US
NORTHWIND
ATLAS RETAIL
MERIDIAN
CASCADE LOGISTICS
HELIX HEALTH
01 — THE GAP

AI can reason. It still needs context.

A customer is not just a record. A product is not just a SKU. An event is not just a data point. Meaning comes from understanding the relationships between everything.

TODAY'S AI CAN
✓Understand language
✓Generate content
✓Analyse information
✓Write code
✓Answer questions
AGENTS ALSO NEED TO KNOW
What exists
What is happening
What changed
Why it changed
What actions make sense
02 — THE MISSING LAYER

From data to understanding

Businesses already have enormous amounts of data. Data alone does not create understanding. Point of Data transforms real-world signals into an evolving model of the people, products, systems, and events that make up your business.

Real world
People, products, places, systems.
Signals
Events, records, documents, APIs.
Understanding
Entities, relationships, state, history.
AI agents
Context they can reason over.
Decisions + action
Work that actually completes.
03 — EFFICIENCY

Gains from architecture, not hardware tuning.

75%
Less energy per workload
2×
Data computing capacity on existing hardware
85%
Reduction in checkpoint storage for neural-network model training
Event-sourced storage with decreasing marginal cost.
Minimal synchronization.
Near-zero idle compute.
Reduced data movement.
04 — THE PRODUCT

Reality Explorer

Not a graph database view. A living map of what your agents understand right now — and why.

Explore the platform →
reality-explorer / production
12,480 SIGNALS/SEC
ENTITIES
CONNECTED SOURCES
Shopify · orders
Postgres · customers
Segment · events
Zendesk · tickets
SF API · shipments
Sarah Chen
PREMIUM · 4Y
Resolved from 6 systems · 1,284 signals
UNDERSTANDING MAP
Sarah Chen
Premium member since 2022 · LTV $8,420
PERSON
purchased
Trail Running Shoes — Terra 4
Order #48812 · $184.00 · 2 days ago
PRODUCT
fulfilled by
Shipment SF-1129
Carrier delay at Reno hub · 3 days late
EVENT
affects
Trail race — Sept 14
Stated deadline in chat, 6 weeks ago
CONTEXT
informs
TIMELINE
JUN 02
Joined premium tier
AUG 28
Mentioned race date
SEP 01
Placed order #48812
SEP 03
Carrier delay logged
AGENT CONTEXT
What does this agent understand?
CUSTOMER
Premium member, 4 years, never escalated
ISSUE
Shipment delayed 3 days at carrier hub
STAKE
Needs shoes before a race on Sept 14
RECOMMENDED ACTION
Offer expedited replacement from the Sacramento warehouse — arrives Sept 9, no cost to customer.
Execute
Explain
provenance: 6 sources · 4 systems
confidence: 0.94
05 — BEYOND RAG

Retrieval finds information.
Understanding creates intelligence.

TRADITIONAL RAG ARCHITECTURE
question
↓ search
↓ retrieve documents
↓ generate response
Answers “where can I find information about this?”
AGENT ARCHITECTURE WITH POINT OF DATA
reality changes
↓ point of data understands context
↓ agent reasons over the situation
↓ action happens
Answers “what is happening, why, and what should happen next?”

Built for the agent era

All use cases →
Retail & commerce
Customers, products, inventory, demand.
Customer service
From answering questions to solving problems.
Supply chain
Inventory, suppliers, demand, transport.
Security
Users, systems, events, patterns.

Give your AI agents an understanding of the world.

Build AI systems that don't just know information. Build AI systems that understand.