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Connecty AI

Q: Who is Connecty AI for?

Data and analytics teams, RevOps and business teams, Mid market and enterprise SaaS companies, Companies with 5 million to 2 billion dollars ARR

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Data analyst agentprivateVerified 2026-07-09

Agentic analytics and decision intelligence platform built on an Autonomous Semantic Graph that answers questions and recommends actions over your data warehouse.

Connecty AI is an agentic analytics and decision intelligence platform powered by an Autonomous Semantic Graph, deep reasoning, and a context engine. It connects to data warehouses such as Snowflake, BigQuery, Databricks, PostgreSQL, and Athena and to CRM, marketing, and ERP tools through no code connectors, then auto generates a trusted semantic model on day zero by learning the schema, relationships, and KPIs with no manual YAML or rule building. Users ask questions in plain English and receive summaries, interactive outputs, or SQL, with step by step transparency showing how the AI interprets and queries the data, and dynamic Intent Graphs that map dependencies and execution order and adapt plans on the fly. Specialized agents collaborate across discovery, transformation, and analysis, and the platform continuously monitors data, detects changes, and proactively surfaces prioritized, goal aligned recommended actions rather than static dashboards. Its data in place architecture processes everything within the customer's own warehouse and never exposes sensitive data to the LLM, validating every query outside the LLM to prevent prompt injection and enforcing role based and row level access. Connecty AI is based in San Francisco and delivers via chat, Slack, email, or an API and MCP for agentic workflows.

Vendor details

Canonical URL

https://www.connectyai.com

Funding status

Pre seed. Raised 1.8 million dollars in a pre seed round led by Market One Capital, with Notion Capital and angels including Snowflake co founder Marcin Zukowski and Piwik PRO founder Maciej Zawadzinski. Emerged from stealth in November 2024.

Company status

private

Use cases & customers

Primary use cases

Natural language analytics over the warehouseAutomated semantic modelingDecision intelligence and recommended actionsGoverned self service data exploration

Target customers

Data and analytics teamsRevOps and business teamsMid market and enterprise SaaS companiesCompanies with 5 million to 2 billion dollars ARR

Integrations

Connects to data warehouses including Snowflake, BigQuery, Databricks, PostgreSQL, and Athena and to CRM, marketing, and ERP tools through no code connectors, reading the schema and inferring business context on day one.

Sources & related URLs

Related / legacy domains

https://www.linkedin.com/company/connectyai/ https://www.crunchbase.com/organization/connecty-ai

Research sources

https://www.connectyai.com/analyze https://www.connectyai.com/product/autonomous-semantic-graph https://www.connectyai.com/resources/security https://venturebeat.com/data-infrastructure/connecty-ai-brings-order-to-data-chaos-with-real-time-context-graphs

Capability coverage

9.0 / 14 capabilities · 64%

Integrations & Tool CallingNo code connectors to Snowflake, BigQuery, Databricks, PostgreSQL, and Athena plus CRM, marketing, and ERP tools, reading schema and inferring context on day one (getconnectyai.com, connectyai.com).	Full
Workflow OrchestrationSpecialized multi agents collaborate across discovery, transformation, and analysis, orchestrated with Intent Graphs that map execution order and adapt plans on the fly (connectyai.com/analyze).	Full
Knowledge Grounding & RAGAn Autonomous Semantic Graph and context engine auto generate a semantic model and continuously learn schema, relationships, metrics, and query intent (connectyai.com/product/autonomous-semantic-graph, venturebeat.com).	Full
Human Oversight & GuardrailsFull human control and transparency, with the ability to verify, un verify, or edit and human in the loop validation of changes to the semantic layer (connectyai.com/analyze, connectyai.com/product/autonomous-semantic-graph).	Full
Security, Identity & GovernanceData in place processing inside the customer's warehouse, no sensitive data exposed to the LLM, queries validated outside the LLM to block prompt injection, role based and row level access, and GDPR compliance, with ISO 27001 and SOC 2 Type II in progress (connectyai.com/resources/security).	Full
Observability & AuditabilityStep by step visibility into how the AI interprets and queries data and debugging of query generation, assumptions, and errors, with explainable, auditable outputs (connectyai.com/analyze).	Full
Memory & State PersistenceLearns and evolves from outcomes and feedback loops and the semantic graph persists context, though a per session agent memory is not detailed (connectyai.com/product/decision-intelligence-layer).	Partial
Deployment & Data ResidencyData in place architecture keeps all processing within the customer's own warehouse such as Snowflake, Databricks, or BigQuery; the application itself is cloud (connectyai.com/resources/security).	Partial
Prebuilt Agents / Templates / PacksDocumented use cases such as funnel diagnosis and lead scoring are illustrative rather than an installable pack library (connectyai.com/product/decision-intelligence-layer).	Unable to verify
Triggers & Channel CoverageRuns continuously in the background, monitors and detects changes, and delivers recommendations via Slack or email; a broad trigger framework is not detailed (getconnectyai.com, connectyai.com/top-ai-analytics-tools-in-2026).	Partial
Model Flexibility & RoutingNo user facing model choice or routing is documented (connectyai.com).	Unable to verify
APIs / SDKs / MCP ExtensibilityAn API and MCP embed the semantic graph into agents, BI, notebooks, and apps, with semantic APIs and governed metrics (connectyai.com/product/autonomous-semantic-graph, connectyai.com/top-ai-analytics-tools-in-2026).	Full
Testing, Debugging & OptimizationStep by step transparency and the ability to verify, un verify, or edit metrics support debugging and validation, though a general agent evaluation suite is not detailed (connectyai.com/analyze).	Partial
Browser / Computer-useNo browser or computer use is documented (connectyai.com).	Unable to verify

Recent platform changes

No recent material changes tracked yet.

View all changes for Connecty AI →

Pricing

Not public as fixed figures. Connecty AI has indicated a per seat or usage based model and is demo and waitlist gated; exact prices are not published.

Per seat or usage based; query volume and warehouse compute scale with use.

Public — partialMedium variable cost

Included quota

Not public.

Cost watchouts

Query volume and warehouse compute in the customer's own data warehouse scale with usage; per seat costs grow with team size.

Variable cost rationale

A usage based component means query volume and warehouse compute drive cost, and per seat pricing scales with team size, though the data in place model keeps heavy compute in the customer's own warehouse.

Overage / add-ons

Usage based components scale with query and compute volume.

Sales call required

Mixed (some tiers require a call)

Free / trial

Book a demo or join the waitlist.

Lowest paid plan

Not published as a fixed tier.

Key ambiguities

A per seat or usage based model is indicated, but exact figures are not published and access is demo and waitlist gated.

Verified 2026-07-09

Official pricing page

Data confidence: high