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CATEGORY

AI Analytics startups (2026)

Notebooks, text-to-SQL, predictive layers, and unstructured analytics — all racing the death of the dashboard.

73 ai analytics startups tracked, with the largest concentration in US. Total tracked funding: $6.5B.

Tracked
73
Total Raised
$6.5B
Countries
12
Active Deals
0

Editor's picks

6

Top by score

View all 73 →

Funding by year — AI Analytics

2020 → 2026
$9.2M
’20
$206.8M
’21
$129M
’22
$279.6M
’23
$66.1M
’24
$613.5M
’25
$608.8M
’26

Market overview

The dashboard is dying slowly. Snowflake Cortex and Databricks Genie shipped native text-to-SQL and natural-language semantic layers in 2025; the analyst seat that previously opened Tableau or Looker now opens a notebook with a chat input. The 19 companies in this category — $1.48B in cumulative disclosed capital — split between the ones building that new surface (Hex, Hebbia, Pecan AI) and the ones absorbing AI into existing BI (Domo, Anaplan). The moats live in workflows the warehouse cannot own.

The dashboard era is ending

  • Hex ($68M Series B). Collaborative SQL-plus-Python notebook with aggressive natural-language query and chart generation. The wedge is multiplayer analyst workflows that warehouse-native chatbots cannot replicate — Snowflake Cortex and Databricks Genie are features, Hex is a workspace.
  • Hebbia ($161M Series B, July 2024). Retrieval-grade analytics over unstructured private corpora — investment memos, legal review, regulatory filings. Warehouses index structured tables; Hebbia indexes the documents around them.
  • Pecan AI (Israel). Generates SQL-based predictive models directly inside the warehouse, removing the data-export step that killed earlier predictive vendors. Predictive analytics goes warehouse-resident or it dies.
  • Domo ($690M cumulative). The public-market BI veteran bolting generative features onto an established planning and dashboard surface. Anaplan plays the same incumbent role on the planning side.
  • Kensho ($550M cumulative, acquired by S&P Global). Vertical-finance specialization — jurisdictional or vertical depth as the alternative to horizontal BI.
  • Presight AI (Abu Dhabi). Sovereign-analytics archetype, extending government-grade analytics from the UAE the way HUMAIN extends sovereign-AI infrastructure from Saudi Arabia.

Geographically the category is US-anchored — 10 of 12 disclosed HQs — with Israel (Pecan) and the UAE (Presight) the non-US footholds. Capital is bimodal: legacy raises (Domo, Kensho, Anaplan-scale platforms) sit alongside capital-efficient newcomers like Hex and Schematic ($6.5M seed in April 2026) riding Snowflake and Databricks distribution.

What gets squeezed in 2026

The make-or-buy moment is here. Cortex and Genie have absorbed the easy text-to-SQL use cases. Standalone BI assistants now defend a higher-value workflow — collaboration (Hex), unstructured retrieval (Hebbia), warehouse-resident prediction (Pecan), or vertical depth (Kensho-pattern). Generic chatbot-on-top-of-warehouse plays get crushed between native warehouse features and deeper-workflow specialists.

Key trends 2026

  • Warehouse-native gravity is the dominant force. Snowflake Cortex and Databricks Genie absorb the easier text-to-SQL use cases, forcing standalone BI assistants to defend a higher-value workflow.
  • Notebook collaboration as the moat. Hex's $68M Series B bet — multiplayer SQL-plus-Python with generative chart workflows — pulls analyst seats away from Looker and Tableau where the team is data-fluent.
  • Predictive analytics goes warehouse-resident. Pecan AI pushes model training inside the warehouse, eliminating the data-export step that killed earlier predictive vendors.
  • Unstructured analytics is the next surface. Hebbia's $161M is targeting the retrieval workflow over private corpora that no warehouse currently owns.
  • Sovereign and vertical specialization. Presight AI in Abu Dhabi and finance-specialized Kensho show that jurisdictional or vertical depth is a viable alternative to horizontal BI.

Benchmarks vs global

Companies tracked
19
BI assistants, text-to-SQL, predictive, observability
Cumulative disclosed funding
$1.48B
capital-efficient, riding Snowflake and Databricks
Top single raiser
Domo $690M
public-market BI veteran adding generative features
US headquarter share
83%
10 of 12; Israel (Pecan) and UAE (Presight) the only non-US

Top countries

By startup count

Stage breakdown

Latest round type
  • Seed 27
  • Series A 11
  • Series B 9
  • Series C 8
  • Series F 3
  • Series E 1
  • Series D 1
  • IPO 1

Top investors backing AI Analytics

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FAQ

Frequently asked

What is AI Analytics versus a regular BI tool with a chatbot?
AI Analytics covers products where the primary surface is a query, dashboard, or predictive insight delivered with material AI assistance. A traditional BI tool that bolts on a thin chatbot does not qualify. Hex's notebook, Hebbia's retrieval workspace, and Pecan's warehouse-resident predictive models do — the AI is the workflow, not a sidebar.
How are these tools different from Snowflake Cortex or Databricks Genie?
Cortex and Genie are warehouse-native features that ship text-to-SQL and semantic-layer copilots directly inside the data platform. The independents in this category bet on workflows the warehouses do not own — multiplayer collaboration (Hex), unstructured retrieval (Hebbia), or vertical depth (Kensho).
Is text-to-SQL really a defensible category in 2026?
Pure text-to-SQL is now a feature, not a product. Defensible companies wrap it in a notebook (Hex), a workflow (Pecan), an unstructured corpus (Hebbia), or a vertical decision surface (Kensho). The standalone text-to-SQL pure-plays have been crushed between Cortex, Genie, and the deeper-workflow specialists.
Where does data observability fit?
Data observability tools that detect freshness, schema, and quality regressions on warehouse tables fall inside this category when ML is core to the detection. Pure rules-based monitoring sits in adjacent infrastructure tooling. The line matters because ML-driven anomaly detection is the part Snowflake and Databricks have not yet absorbed natively.
Why is the cumulative funding smaller than other AI categories?
$1.48B reflects disclosed cumulative across 19 tracked vendors. Many AI Analytics startups are capital-efficient and ride Snowflake or Databricks distribution rather than competing on raw GPU spend. Several entries also have undisclosed totals, so the headline figure understates true category capital.

Recent rounds in AI Analytics

All rounds →
Date Startup Round Amount
May 2026 Sigma Computing Series E $80M
May 2026 Nectar Series A $30M
Apr 2026 Schematic Seed $6.5M
Apr 2026 Omni Series C $120M
Apr 2026 PeakMetrics Series A $6M
Feb 2026 Optiml Seed $8.8M
Feb 2026 Fractal Analytics IPO $335M
Jan 2026 Constellation Space Seed $500K

All AI Analytics startups

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