<50ms query latency
+200% relevant context

The only vector database with a feedback loop

Connect your data. Train your models. Query text, user or session context and retrieve relevant results in milliseconds.

$100 free credits. No credit card required.
Vox
ShapedQL

One query. Any use case.

Retrieve by text, user ID, or item ID — whatever context you have.

Retrieve
Hybrid search across multiple indexes
Filter
Hard constraints & business rules
Score
ML models & value functions
Reorder
Diversity & exploration

Agent context

01
02
03
04
SELECT doc_id, title, content
FROM
  semantic_search("How do I authenticate the API?"),
  keyword_search("How do I authenticate the API?")
05
WHERE verified = true
06
07
08
ORDER BY
  colbert_v2(item, "How do I authenticate the API?") +
  recency_score(item)
09
10
REORDER BY
  diversity(strength=0.3)

Unlike a vector store, Shaped knows who's asking. Results are personalised per user and get better with every interaction.

Personalized hybrid search

01
02
03
04
SELECT title, description
FROM
  semantic_search("wireless headphones"),
  keyword_search("wireless headphones")
05
06
07
ORDER BY
  colbert_v2(item, "wireless headphones") +
  click_through_rate_model(user, item)

Blend semantic and keyword search, reranked by what this user actually clicks.

Also available via Python SDK, TypeScript SDK, or MCP. See docs

Built for decisions, not documents

Legacy RAG stacks require endless duct tape. Shaped unifies retrieval, ranking, and learning in one query.
Traditional agent stacks
$1.50 per answer
Retries from bad context add up fast.
$0.03 per answer
50x cheaper. No retries. No surprises.
Retry loops
Hallucinate, retry, repeat.
No retry loops
Right results first time, every time.
Sliced data
5 systems. 5 calls. Bad context.
Unified context
One call. Every data source. Instant.
Static embeddings
Never learn. Never improve.
Feedback loop
Every interaction makes results better.
5 services
Pinecone + Cohere + Redis + pipelines + glue.
One query
Replace your entire retrieval stack.
Text only
Can't retrieve by user or item.
Any context
Text, user ID or item ID.
Similarity matching
No business logic. No personalisation.
Ranks for decisions
Behavioural signals + business rules.
Build everything yourself
Pipelines, connectors, maintenance.
30+ native connectors
Plug in. Stream. Done.
Three-layer architecture

A unified, queryable relevance engine

Shaped is an end-to-end relevance engine designed for real-time personalization and agent memory.

Query layer

Real-time retrieval & ranking
ShapedQL
SQL
interface
Ranking
Multi-stage
pipeline
Results
<50ms latency

Intelligence layer

ML models & embeddings
Embeddings
Dynamic
vectors
Training
Continuous
learning
Models
User + item
context

Data layer

30+ connectors
Connectors
Batch +
streaming
Tables
Unified
schemas
Views
SQL
transforms

10x

Increase in experimentation velocity

Ship and test new ranking models in days, not months

~7 days

Time to first experiment

From data connection to production in under a week

Connect your stack. Unify your data.

Combine batch and real-time data in a single schema
Data warehouses
Snowflake
BigQuery
Redshift
Databricks
Analytics Applications
Amplitude
Segment
Rudderstack
Posthog
Streaming
Kinesis
Kafka
Pub/Sub
Catalog Storage
Postgres
MongoDB
Shopify

30+ Native Connectors

Ingest raw event logs directly from your warehouse or real-time streams. No ETL required.

Enterprise-grade security

SOC 2 Type II certified, GDPR/HIPAA compliant, and backed by a 99.95% uptime SLA.

Secure + Compliant
SOC 2 Type 2, GDPR,  CCPA,  HIPAA
Enterprise scale
5B+ documents
Reliable
99.95% Uptime

Enterprise ready

Secure
SOC 2 Type 2
Compliant
GDPR,  CCPA, HIPAA
Fast
<50ms Latency
Reliable
99.95% Uptime
Scalable
1,000+ QPS

Get started this week

Connect your data, build your first model, and deploy to production in 7 days. No infrastructure required.

Day 1
Connect data
Day 2-5
Build & iterate
Day 7
Push live