Knowi
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ThingSpeak
AWS IoT
Axonize
Azure IoT Hub
AWS IoT Core
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Knowi is the Agentic Analytics Platform. It unifies data from anywhere, adds a governed semantic layer, and deploys AI agents that analyze, monitor, and act on live business data.
Traditional BI tools bolt AI onto dashboards. Knowi was built AI-first. The semantic layer defines your metrics, dimensions, and business logic once across every source, so dashboards, queries, and AI agents all reason from the same source of truth. Agents then run continuously: they monitor KPIs, detect anomalies, investigate root causes, and alert your team without anyone opening a dashboard.
Connect 70+ sources natively: SQL, NoSQL (MongoDB, Elasticsearch, InfluxDB), REST APIs, cloud warehouses, SaaS applications, and documents. Query and join across them live, with no ETL and no data movement, so teams go from raw data to answers 10X faster.
What you get
Enterprise-grade by default: SOC 2 Type II, HIPAA, GDPR, SSO, role-based access, encryption in transit and at rest, full audit logging. Trusted by teams at Lockheed Martin, Verizon, Tata, Paramount, Infosys, and Autodesk.
Teams can connect their first sources and build live dashboards within days, not quarters.
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Knowi's answer
Verizon Telstra Everbridge ConvergeOne NJM Insurance Volante Systems intlx Solutions Alteas Health Hometime Tata
Knowi's answer
Knowi runs AI agents inside the data layer rather than on top of finished dashboards. The agents reach schemas and the query engine directly, which means they can build queries, widgets and dashboards, not just answer questions about ones that already exist.
That architecture comes from what Knowi was built to do: query data where it already lives. It speaks MongoDB and Elasticsearch in their own query language, including nested documents and arrays, and it joins across MongoDB, PostgreSQL, Snowflake, Databricks SQL, Trino, Salesforce and REST APIs in a single query without first moving anything into a warehouse.
Knowi also runs its own AI by default, with OpenAI and Claude available as optional integrations rather than requirements, and it deploys to cloud, your own VPC, on-premises via Docker or Kubernetes, or air-gapped.
Knowi's answer
Three practical reasons.
Your data does not have to be relational first. Most BI tools need a warehouse and an ETL pipeline before you see a chart, which means unstructured, nested and API data either gets flattened or gets left out. Knowi queries those sources natively, so the modelling work you would normally do up front becomes optional.
The AI is part of the query path, not a chat box on the side. Many platforms added a copilot that describes existing dashboards. Knowi's agents have access to the schema and the query engine, so they can create new datasets, widgets and dashboards from a question.
You control where it runs and which model touches your data. Knowi AI is the default, third-party models are optional, and deployment can be Knowi Cloud, your own VPC, on-premises, or air-gapped. Knowi is SOC 2 Type II certified, GDPR compliant, and offers a HIPAA BAA.
Knowi's answer
Data, engineering and product teams at mid-market and enterprise companies whose data is spread across more than one kind of system: NoSQL alongside SQL, warehouses alongside SaaS APIs and documents.
Two buying patterns show up most often. Internal analytics teams who need governed self-service across sources without building a pipeline for every question. And product teams who need customer-facing, multi-tenant analytics embedded inside their own application with row-level access control.
By industry, the customer base skews to telecom, healthcare, manufacturing, SaaS and adtech, proptech and e-commerce. Knowi is sold through a sales team and priced per deployment rather than by public self-serve tier.
Knowi's answer
Knowi was founded in 2014, at the point where a lot of production data had stopped being relational. Teams were running MongoDB and Elasticsearch, and the BI tools of the day all assumed a star schema in a warehouse. Getting a dashboard meant building a pipeline first, and anything nested or semi-structured got flattened or dropped along the way.
Knowi was built the other way round: connect to the source, query it in its own language, and join across sources at query time instead of moving the data. Cross-source joins, post-query transformation and a dataset layer that data teams could govern followed from that starting point, and embedded analytics came from customers who wanted to give the same views to their own users.
The AI work is a continuation rather than a pivot. Because Knowi already owned the query path across every connected source, AI agents could be placed inside the data layer with access to schemas and the query engine, instead of being bolted onto a finished dashboard.
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