Join SQL, NoSQL and API data in one place
Query SQL, NoSQL, REST APIs and documents where they already live. Join a Postgres table to a MongoDB collection to a REST endpoint in one query, without moving a row. Dashboards, agents, apps and embeds all read the same definitions and permissions.
No ETL · no data movement · row-level security enforced at the dataset
Connect everything. Move nothing.
Federate. Govern. Serve.
Three steps. None of them is a pipeline.
Query each source in its own language
Point Knowi at your SQL databases, NoSQL stores, REST APIs, files and documents. Credentials and network access, nothing else. No extraction step, no landing zone.
One definition of every metric
Join, clean and transform across sources into a virtual dataset with Cloud9QL. Definitions, permissions, row-level security and lineage live on the dataset, not in each tool that reads it.
Every consumer reads the same layer
Dashboards, agents, apps, embeds and external tools all read the governed dataset. The contract holds everywhere it is consumed.
Relational tables and nested JSON in one query.
The joins that normally need a pipeline run at query time. A new source is a configuration change, not an engineering project.
- Join relational tables to nested JSON documents
- Combine transactional data with event logs or API responses
- Build cross-source metrics: usage in MongoDB, billing in Postgres, CRM in Salesforce
Transformations without a pipeline.
Aggregate, filter, group and derive across any source with one language. Define the logic in the dataset and every dashboard, embed and agent inherits it. Change it once and it changes everywhere.
- Flatten nested JSON and arrays without a modelling step
- Calculate new metrics and derived fields inline
- Apply business logic once, reuse it across every consumer
select account_id, sum(seats_used) as used
expand usage.sessions;
join postgres.invoices on account_id;
join salesforce.Account on external_id;
select *, used / seats_sold as utilisation
Definitions, not copies.
A virtual dataset sits on top of your live sources. No sync job to fail, no overnight window, no second copy of the number.
- Curated for business teams, data apps and AI agents
- Always current, because it is reading the source
- Reusable across dashboards, reports and external tools
Permissions live on the dataset.
Access is enforced where the query runs, so a chart, an embed, an agent prompt and an MCP client all hit the same rules. Nobody gets a wider view by asking a different way.
- Role and group-based access control
- Dataset, field and row-level security
- Multi-tenant isolation for SaaS use cases
- Full auditability across every consumer
Operational data joined to the system of record.
Fast-moving event data rarely lives beside the transactional data it needs to be read against. Knowi joins them without staging either one.
- Connect to streaming and log sources including Kafka, Elasticsearch and DynamoDB Streams
- Join real-time data with warehouse or transactional databases
- Power live dashboards, alerts and ops views from the same unified layer
Agents read the governed dataset.
Knowi's agents run inside the data layer, against the dataset you defined and the permissions on it. The answer matches the dashboard because it is the same query.
- Ask questions in plain English across every unified source
- Get insights, summaries and recommendations grounded in governed data
- Detect trends and anomalies automatically
- Use the same layer in dashboards, embeds and MCP clients
Connecting SQL, NoSQL, cloud, APIs and documents.
What the unified layer looks like in the product.
Your cloud data warehouse, and everything it does not hold.
Every connector, queried in place and joined at query time. Nothing has to land in a warehouse first.
MySQLSQL ServerOracleAmazon RedshiftGoogle BigQuerySnowflakePostgreSQLTeradata
Trino
Databricks
ClickHouse
SAP HANA
Amazon Athena
Presto
Dremio
AgensGraph
OpenSearch
Amazon DynamoDB
Apache Hive
OrientDB
InfluxDB v2
Aerospike
TigerGraphREST APIsSalesforceApache SparkGoogle Analytics
Stripe
NetSuite
Shopify
Klaviyo
JiraAirtableQuickBooks
Google Ads
Google Search Console
HubSpot
Mailchimp
Amplitude
Facebook
Instagram
YouTube
OpenAI
ZendeskSmartsheet
BigCommerceAmazon CloudWatch
TikTok
Recharge
Judge.me
Autopilot
Azure Monitor
GCP Monitoring
CSVGet your data together for AI. Without moving it.
AI agents and copilots are only as useful as the data they can reach. In most enterprises that data sits in a search cluster, a system of record and a warehouse, often across sites. Knowi queries each one in its own language and joins the results, so your people and your agents work from the same data, and nothing is copied first.
Search + system of record + warehouse
Join event data in Elasticsearch to customer and order records in Oracle, next to history in Snowflake or Databricks.
Documents + relational + APIs
Nested MongoDB documents joined to SQL Server tables and API data, without flattening the JSON first.
Time series across sites
Machine and sensor data from each site, next to the operational records it belongs to.
Many sites or products, one curated view
Curated datasets across plants, regions or product lines, in front of thousands of viewers in your own portal or product.
Your agents. All of your data.
Most AI programmes stall on access, not on models. Agents need the search cluster, the system of record and the warehouse at once, with the same definitions and permissions your people use. Knowi gives them one governed layer to work from.
Knowi agents
Ask in plain language. Agents query across sources, build dashboards, and deliver reports and alerts.
Your assistant
Connect Claude or any MCP client through the Knowi MCP server, so your assistant queries the same governed data.
Your own product
Embed Knowi's agent chat in your own application, so your customers or teams ask questions where they already work.
Federation is the start, not the product.
Data virtualization and federated query engines let you run one query across many sources. That solves access. Teams still need somewhere to build dashboards, embed analytics in their product, set alerts and deliver reports, so another tool usually follows. Knowi does the federation and the work on top of it in one place.
Runs where your data lives.
Knowi Cloud
Managed by us. Connect your sources and start.
Inside your network
An in-network agent queries sources behind your firewall and joins them there.
On-premise
Run the full platform in your own environment. Deployment docs
Without ETL pipelines.
One copy of every number
No hopping between tools, exports and three slightly different versions of the same metric.
No modelling project in front of the first dashboard
The months of ETL and schema design that normally come first are not required.
New sources are configuration
Add a database, an API or a document set without redesigning the stack around it.
Every use case runs on one layer
Dashboards, embedded analytics, data products and AI agents, all reading the same governed definitions.
Questions about unified data.
What does unifying data mean in Knowi?
A single governed layer that queries, joins and shares data across SQL databases, NoSQL stores, REST APIs, files and documents. You define a dataset once and every dashboard, embedded experience and AI agent reads from that same definition.
Does Knowi move or copy my data?
No. Knowi connects directly to your sources and runs queries on top of them. Virtual datasets are definitions, not copies, so there is no sync lag and no second place for the data to drift.
How is this different from a data warehouse?
A warehouse requires you to move and model data before you can use it. Knowi works with data in place across operational systems, warehouses and APIs. You can keep the warehouse. Knowi lets you include everything that never made it in.
Can I join SQL, NoSQL and API data in one dataset?
Yes. That is the core of it. Cloud9QL performs cross-source joins so relational tables, JSON documents and API responses can be blended in a single query.
What is Cloud9QL?
Knowi's query and transformation language. It aggregates, filters and groups across any source, flattens nested JSON and arrays, and calculates derived fields. Apply the logic once in a dataset and every consumer inherits it.
Will this work with our existing BI and AI tools?
Yes. Use Knowi's own dashboards, embedded analytics and agents, or expose the unified datasets as a data service to other tools and workflows.
How does Knowi handle security and governance?
Role and group-based access, field and row-level security, and multi-tenant isolation. Permissions are enforced on the dataset, so they hold across dashboards, embeds and agent queries alike.
Can AI agents query the unified layer?
Yes. Knowi's agents run inside the data layer, so they resolve against the governed dataset and its business terms rather than guessing at raw schema.
Can you join SQL and NoSQL data in one query?
Yes. Cloud9QL joins across SQL databases, NoSQL databases, REST APIs, files and documents at query time, so a single dataset can span MongoDB, Postgres and a REST endpoint without moving any of it into a warehouse first.
What is a data integration platform without ETL?
It is a layer that queries source systems directly and joins the results, instead of extracting, transforming and loading the data into a separate store on a schedule. The trade-off is freshness and setup time against the control a warehouse gives you, and the two can run side by side.
Do I still need a cloud data warehouse?
Not to get started, and not for every use case. Knowi queries SQL databases, NoSQL databases, REST APIs and documents where they live, so you can build on sources a cloud data warehouse has not loaded yet. Where a warehouse already exists, Knowi treats it as one more source and joins it to the rest rather than replacing it.
How is this different from building ETL pipelines?
ETL pipelines extract, transform and load data into a separate store on a schedule, so what you analyse is a copy that is as fresh as the last run. Knowi queries the sources directly and joins the results at query time. The trade-off is freshness and setup time against the control and cost predictability a pipeline gives you, and most teams end up running both.
What does data as a service mean here?
Data as a service means the data is exposed as a governed, reusable dataset that any dashboard, agent, embedded view or API consumer can query, rather than something each team re-extracts for itself. In Knowi a dataset is defined once and reused everywhere it is needed.
How do we get data from Oracle, Databricks and Elasticsearch ready for AI agents without a migration?
Connect each source to Knowi. Knowi joins them into governed datasets where the data already lives, and Knowi agents, your own assistant through the Knowi MCP server, or agent chat embedded in your app all work from those datasets. There is no warehouse project in front of the first agent.
How do I query Elasticsearch, Oracle and Snowflake together without ETL?
Connect each one as a datasource in Knowi. Knowi queries Elasticsearch in its own query language and Oracle and Snowflake in SQL, then joins the results into one dataset. You choose live queries or cached results. Nothing is copied into a warehouse first.
How is Knowi different from a data virtualization tool?
A data virtualization or federated query engine gives you one query across many sources. Knowi does that too, and adds dashboards, embedded analytics, alerts, scheduled reports and AI agents on the same dataset, so you do not need a separate BI layer on top.
Can our own copilot or Claude use Knowi?
Yes. Connect Claude or any MCP client through the Knowi MCP server, or embed Knowi's agent chat in your own application.
Can Knowi run inside our own network?
Yes. You can run Knowi on-premise, or keep Knowi Cloud and use an in-network agent that queries sources behind your firewall.
Does this work if we already use Databricks or Snowflake?
Yes. Knowi queries Databricks SQL and Snowflake directly and joins them with the sources your lakehouse or warehouse does not hold, such as Elasticsearch or MongoDB.
One governed layer over data you never move.
Everyone works from the same numbers and the same rules.
No ETL · no data movement · governance on the dataset