{
 "sym": "Bi",
 "updated": "2026-08-06",
 "verdict": "Omni, if you want one tool that makes \"ask your data in English\" actually work \u2014 it is a modeled BI platform first and a chat box second, which is the only architecture that survives contact with real questions (Series C $120M at $1.5B, 4x YoY revenue, Apr 23, 2026). Hex is the pick when you have analysts and want the copilot to help them, not replace them \u2014 it scored highest of any real BI product on the only public head-to-head accuracy test. Lightdash when your metrics already live in dbt and you refuse per-seat pricing. Metabase when the budget is zero and the questions are simple. ThoughtSpot when a search box for 500 non-technical users is the actual requirement. And if you already pay for Databricks, Fabric, or Looker, use the copilot you own before you buy a fifth one \u2014 the differentiator in this category is never the LLM, it is whether someone did the semantic modeling.",
 "top5": [
  {
   "rank": 1,
   "name": "Omni",
   "maker": "Omni Analytics (Colin Zima, ex-Looker)",
   "url": "https://omni.co",
   "docs": "https://docs.omni.co",
   "pricing": "No public price list \u2014 sales-quoted annual contracts (verified Aug 2026). AI chat, modeling agent and MCP server included; LLM tokens not separately metered.",
   "best_for": "Startups that want Looker-grade governed metrics without Looker's cost or rebuild tax, with conversational access bolted to the same model.",
   "why": "Built by the people who built Looker, on the correct premise for this category: the chat is only as good as the semantic model, so Omni ships a modeling agent to build the model and an AI chat that queries through it, plus an MCP server so Claude, ChatGPT and Cursor hit the same governed logic. ICONIQ led a $120M Series C at $1.5B on Apr 23, 2026 against 4x YoY revenue \u2014 up from a $650M mark in March 2025 \u2014 with BambooHR (100,000+ end users), Checkr and Cribl as reference accounts.",
   "watch": "Zero published pricing means you cannot budget without a sales call, and the entry point is not seed-stage cheap. The modeling agent shortens the semantic-layer build but does not remove it \u2014 you are still doing the actual work, just faster. No independent accuracy benchmark for Omni's AI exists as of Aug 2026.",
   "evidence": [
    {
     "stat": "$120M Series C at $1.5B led by ICONIQ, plus $30M employee tender; 4x YoY revenue growth (Apr 23, 2026)",
     "src": "https://omni.co/blog/press-release-omni-series-c-funding"
    },
    {
     "stat": "MCP server exposes the governed semantic model to Claude, ChatGPT and Cursor; agent skills for model building, querying and content management (Aug 2026)",
     "src": "https://omni.co/"
    },
    {
     "stat": "Valuation up from $650M (Mar 2025) to $1.5B (Apr 2026)",
     "src": "https://omni.co/blog/press-release-omni-series-c-funding"
    }
   ],
   "tile_note": "modeled bi, then chat"
  },
  {
   "rank": 2,
   "name": "Hex",
   "maker": "Hex Technologies",
   "url": "https://hex.tech",
   "docs": "https://learn.hex.tech/docs/getting-started/ai-overview",
   "pricing": "Community free (5 projects) \u00b7 Professional $36/editor/mo \u00b7 Team $75/editor/mo \u00b7 Enterprise custom. Per-seat AI credit grants on paid plans; compute add-ons $0.32\u2013$4.06/hr. BYO API key Enterprise only.",
   "best_for": "Teams with at least one analyst \u2014 the Notebook Agent accelerates them, Threads gives everyone else a governed chat surface on the same models.",
   "why": "Hex is the rare vendor shipping both halves: a Notebook Agent that writes and debugs code with dependency awareness, and Threads (Oct 1, 2025) \u2014 a step-by-step reasoning chat for non-technical users, reachable from Slack, Claude and Cursor \u2014 plus a Modeling Agent for the semantic layer underneath. It scored 80.6% on BI Bench, third overall and the highest of any purpose-built BI product tested, behind only two general coding agents. Pricing is published, which in this category is itself a differentiator.",
   "watch": "Threads and the semantic-model agent require the $75/editor Team plan; the free and Professional tiers get the notebook agent only. Hex's centre of gravity is still the analyst notebook \u2014 element Wh territory \u2014 so buying it purely as a business-user chat box overpays. Last confirmed raise is the $70M Series C (May 2025); 2026 round listings on aggregator sites are unverified.",
   "evidence": [
    {
     "stat": "80.6% accuracy on BI Bench, 3rd of 11 and top-scoring BI product (vendor-run benchmark, published 2026)",
     "src": "https://www.basedash.com/bi-bench"
    },
    {
     "stat": "Threads launched Oct 1, 2025 \u2014 agentic chat for non-technical users, Team and Enterprise plans",
     "src": "https://hex.tech/blog/introducing-threads/"
    },
    {
     "stat": "Published pricing: Professional $36/editor/mo, Team $75/editor/mo (Aug 2026)",
     "src": "https://hex.tech/pricing/"
    }
   ],
   "tile_note": "analyst copilot plus threads"
  },
  {
   "rank": 3,
   "name": "Lightdash",
   "maker": "Lightdash (Apache-2.0)",
   "url": "https://www.lightdash.com",
   "docs": "https://docs.lightdash.com/guides/ai-overview",
   "pricing": "Open source free (self-hosted) \u00b7 Cloud Pro $3,000/mo flat, unlimited users, AI agents included \u00b7 Enterprise custom \u00b7 embedding $0.05/load after 1,000 free or $790/mo for 100k loads",
   "best_for": "Teams whose metrics already live in dbt YAML and who refuse to pay per seat to let the company read its own numbers.",
   "why": "The dbt-native path: metrics and dimensions are defined once in your dbt project and the AI agents query that, with Slack delivery, memory that learns from user corrections, and \u2014 uniquely among the top five \u2014 built-in evaluations to regression-test agent accuracy as your model changes. Apache-2.0 and self-hostable (5.9k GitHub stars, Aug 2026), so the escape hatch is real. Flat pricing means adding 40 viewers costs nothing.",
   "watch": "$3,000/mo flat is the wrong shape for a five-person startup \u2014 you self-host or you pay Looker money. AI agents are Cloud Pro or an Enterprise add-on, not in the open-source build. It scored 23.8% on BI Bench at default settings, second-lowest of eleven \u2014 a result that says more about ungroomed dbt models than about Lightdash, but the dependency is the whole point of the category.",
   "evidence": [
    {
     "stat": "Cloud Pro $3,000/mo flat with unlimited users and AI agents included (Aug 2026)",
     "src": "https://www.lightdash.com/pricing"
    },
    {
     "stat": "AI agents ship with built-in evaluations to validate accuracy; Data MCP available on all Cloud tiers",
     "src": "https://docs.lightdash.com/guides/ai-overview"
    },
    {
     "stat": "Apache-2.0, 5.9k GitHub stars, 729 forks, actively developed (Aug 2026)",
     "src": "https://github.com/lightdash/lightdash"
    }
   ],
   "tile_note": "dbt-native, no per-seat"
  },
  {
   "rank": 4,
   "name": "Metabase",
   "maker": "Metabase",
   "url": "https://www.metabase.com",
   "docs": "https://www.metabase.com/docs/latest/ai/settings",
   "pricing": "OSS free (self-host) \u00b7 Cloud Free $0 \u00b7 Starter $100/mo (5 users, +$6/user) \u00b7 Pro $575/mo (10 users, +$12/user) \u00b7 Enterprise from ~$20k/yr. AI on every tier; $3.75 per 1M tokens if you don't BYO key (1M free).",
   "best_for": "Pre-Series-A teams who need the answer today and cannot justify a four-figure monthly BI line item.",
   "why": "The only tool here where natural-language questions and SQL generation are on the free tier, and the 2026 releases are serious: Metabot, an MCP server, Slack integration, Data Studio for semantic-layer curation, and a dependency graph so you can verify model integrity before the AI leans on it. There is also a master kill switch for all AI \u2014 a governance feature the enterprise vendors mostly lack.",
   "watch": "It finished last on BI Bench at 12.4%, and Metabase's own docs are candid about why: accuracy depends on field descriptions, correct semantic types and a glossary you have to write. Out of the box, pointed at raw warehouse tables, this is the tool most likely to confidently answer the wrong question. Verified-content-only mode for Metabot is Pro/Enterprise.",
   "evidence": [
    {
     "stat": "AI question-asking and SQL generation available from the $0 tier upward; AI service $3.75/1M tokens or BYO key (Aug 2026)",
     "src": "https://www.metabase.com/pricing"
    },
    {
     "stat": "2026 shipping: Metabot, MCP server, Slack, Data Studio semantic layer, dependency graph and diagnostics (Jun 9, 2026)",
     "src": "https://www.metabase.com/blog/ai-for-everyone-with-confidence"
    },
    {
     "stat": "12.4% on BI Bench \u2014 lowest of 11 tools at default settings (vendor-run benchmark, 2026)",
     "src": "https://www.basedash.com/bi-bench"
    }
   ],
   "tile_note": "free tier, real ai"
  },
  {
   "rank": 5,
   "name": "ThoughtSpot Spotter",
   "maker": "ThoughtSpot",
   "url": "https://www.thoughtspot.com",
   "docs": "https://docs.thoughtspot.com",
   "pricing": "Essentials from $25/user/mo annual (5\u201350 users, 25M rows) \u00b7 Pro from $50/user/mo (to 1,000 users, 250M rows) \u00b7 Enterprise & Embedded custom \u00b7 Developer free 1 yr (10 users). LLM tokens explicitly not metered.",
   "best_for": "Rolling a search box out to hundreds of non-technical staff who will never open a BI tool, with governance and unmetered token cost.",
   "why": "ThoughtSpot invented search-first BI and is the only pure-play NLQ vendor named a Leader in the Jun 29, 2026 Gartner Analytics & BI Magic Quadrant, alongside Microsoft, Salesforce Tableau, Google Looker and Qlik. The 2026 agent line went beyond chat: SpotterViz builds dashboards from language, SpotterModel builds semantic models without code, SpotterCode generates embedding code \u2014 plus industry-tuned Spotter agents. Not metering LLM tokens matters when 400 people start asking questions.",
   "watch": "The $25 list price is the smallest part of the bill \u2014 this is an enterprise sales motion with modeling services attached, and analysts flag semantic modeling as the bottleneck the agents only partially remove. No independent accuracy data exists, no ARR is disclosed, and the persistent IPO-or-sale speculation means the strategic picture in 12 months is genuinely unknown.",
   "evidence": [
    {
     "stat": "Leader in the 2026 Gartner Magic Quadrant for Analytics and BI Platforms (published Jun 29, 2026)",
     "src": "https://www.martechcube.com/thoughtspot-leads-2026-gartner-magic-quadrant-for-analytics-bi-platforms/"
    },
    {
     "stat": "SpotterViz, SpotterModel and SpotterCode announced Dec 10, 2025, GA early 2026",
     "src": "https://www.techtarget.com/searchbusinessanalytics/news/366636078/ThoughtSpot-automates-full-platform-with-new-Spotter-agents"
    },
    {
     "stat": "Essentials from $25/user/mo; \"ThoughtSpot does not meter or charge for LLM tokens\" (Aug 2026)",
     "src": "https://www.thoughtspot.com/pricing"
    }
   ],
   "tile_note": "search box for everyone"
  }
 ],
 "matrix": {
  "cols": [
   "Semantic layer",
   "Ask surfaces",
   "MCP server",
   "Open source",
   "Pricing model",
   "Startup entry",
   "Built-in evals"
  ],
  "rows": [
   [
    "Omni",
    "Native + modeling agent",
    "App \u00b7 Slack \u00b7 Claude/ChatGPT/Cursor",
    "Yes",
    "No",
    "Sales-quoted annual",
    "Quote only",
    "Not published"
   ],
   [
    "Hex",
    "Native + modeling agent",
    "Notebook \u00b7 Threads \u00b7 Slack \u00b7 Claude",
    "Yes",
    "No",
    "Per editor + AI credits",
    "$36/editor/mo",
    "Not published"
   ],
   [
    "Lightdash",
    "dbt YAML (yours)",
    "App \u00b7 Slack \u00b7 MCP",
    "Yes",
    "Yes (Apache-2.0)",
    "Flat, unlimited users",
    "Free self-host / $3k/mo",
    "Yes"
   ],
   [
    "Metabase",
    "Data Studio (2026)",
    "App \u00b7 Slack \u00b7 MCP \u00b7 Agent API",
    "Yes",
    "Yes (AGPL core)",
    "Base + per user + tokens",
    "$0 / $100/mo",
    "No"
   ],
   [
    "ThoughtSpot Spotter",
    "Native + SpotterModel",
    "App \u00b7 embedded \u00b7 Spotter agents",
    "Yes",
    "No",
    "Per user, tokens free",
    "$25/user/mo",
    "No"
   ],
   [
    "Sigma",
    "Warehouse-native + models",
    "App \u00b7 spreadsheet \u00b7 Sigma Agents",
    "Yes",
    "No",
    "Sales-quoted",
    "Quote only",
    "Not published"
   ],
   [
    "Databricks Genie",
    "Unity Catalog + Genie Spaces",
    "Genie app \u00b7 dashboards \u00b7 mobile",
    "Yes",
    "No",
    "Included in DBU consumption",
    "Platform cost",
    "Yes (benchmarks + Ask Review)"
   ],
   [
    "Power BI Copilot",
    "Power BI semantic models",
    "Power BI \u00b7 Teams \u00b7 Copilot",
    "Limited",
    "No",
    "Fabric capacity",
    "F2 capacity \u2248 $262/mo",
    "No"
   ]
  ]
 },
 "rules": [
  {
   "if": "You are already paying for Databricks, Microsoft Fabric, Looker or Snowflake",
   "then": "Use the copilot you own before buying a fifth tool. 1.5M Genie Spaces were created in 2026 alone; Fabric Copilot dropped to F2 capacity (~$262/mo) with no per-user AI fee. Buy a standalone BI copilot only after the built-in one demonstrably fails."
  },
  {
   "if": "You have no semantic layer and no one to build one",
   "then": "Do not buy conversational BI yet \u2014 buy the modeling. Standalone semantic-layer adoption tripled from ~8% to ~28% among data teams in six months (Hex, Dec 2025) precisely because chat without a model produces confident wrong answers. Omni's or ThoughtSpot's modeling agent shortens that job; nothing skips it."
  },
  {
   "if": "Your metrics already live in dbt and you have more viewers than analysts",
   "then": "Lightdash \u2014 flat pricing, agents that query your existing dbt YAML, and built-in evals to catch regressions. Self-host free if $3,000/mo is out of range."
  },
  {
   "if": "You have analysts and the bottleneck is their throughput, not executive self-service",
   "then": "Hex at $36\u201375/editor. It out-scored every other BI product on the one public head-to-head accuracy test, and the notebook agent compounds with the people you already employ."
  },
  {
   "if": "An executive will make a real decision on the AI's answer",
   "then": "Require citation-to-source and verified-content-only modes before rollout. Only 51% of 114 data leaders trust AI-generated insights and 26% have already been burned by an inaccurate one (insightsoftware, Jun 2026); audit trails linking output to source data were the single most-requested fix (53%)."
  }
 ],
 "field": [
  {
   "name": "Sigma",
   "maker": "Sigma Computing",
   "note": "Spreadsheet-native warehouse BI; $200M ARR and 2,000+ customers (Apr 13, 2026), $80M Series E at $3B (May 18, 2026); Sigma Agents its fastest-adopted feature ever. Scored 35.2% on BI Bench.",
   "url": "https://www.sigmacomputing.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Databricks AI/BI Genie",
   "maker": "Databricks",
   "note": "1.5M Genie Spaces created in 2026; explicitly positioned as routing to vetted logic rather than 'another error-prone text-to-SQL interface'. Ships benchmark tests and Ask Review for accuracy tracking.",
   "url": "https://www.databricks.com/product/ai-bi",
   "oss": false,
   "entry": "included in DBU consumption",
   "status": "active"
  },
  {
   "name": "Databricks One",
   "maker": "Databricks",
   "note": "Renamed to Genie on Apr 26, 2026 to unify the business-user surface with the chat agent",
   "url": "https://www.databricks.com/blog/next-generation-databricks-genie",
   "oss": false,
   "entry": "\u2014",
   "status": "renamed"
  },
  {
   "name": "Snowflake Cortex Analyst / Cortex Agents",
   "maker": "Snowflake",
   "note": "Semantic-view-grounded text-to-SQL inside the warehouse; SQL generation improved Apr 13, 2026. Snowflake publishes no accuracy figures; scored 19.2% on BI Bench.",
   "url": "https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst",
   "oss": false,
   "entry": "consumption-based",
   "status": "active"
  },
  {
   "name": "Power BI Copilot",
   "maker": "Microsoft / Fabric",
   "note": "The volume leader by installed base; Fabric Copilot Capacity available from F2 (~$262/mo) with no per-user AI fee. Microsoft a 2026 Gartner MQ Leader.",
   "url": "https://www.microsoft.com/en-us/power-platform/products/power-bi",
   "oss": false,
   "entry": "F2 capacity \u2248 $262/mo",
   "status": "active"
  },
  {
   "name": "Looker Conversational Analytics",
   "maker": "Google Cloud",
   "note": "LookML as the anti-hallucination substrate; Next '26 (Apr 22, 2026) added dashboard agents, agentic workflows, a managed MCP server and a LookML AI agent for VS Code. Google a Leader for the 3rd year.",
   "url": "https://cloud.google.com/looker",
   "oss": false,
   "entry": "platform pricing",
   "status": "active"
  },
  {
   "name": "Tableau Next / Tableau Agent",
   "maker": "Salesforce",
   "note": "Agentic rebuild of Tableau on the Agentforce/Data Cloud stack; Salesforce Tableau a 2026 MQ Leader. Pricing entangled with Salesforce licensing.",
   "url": "https://www.salesforce.com/analytics/tableau-next/",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Amazon Q in QuickSight",
   "maker": "AWS",
   "note": "Generative BI inside QuickSight with reader/author Pro roles; the cheapest way to add NLQ if you are already AWS-native",
   "url": "https://aws.amazon.com/quicksight/",
   "oss": false,
   "entry": "per-user Pro roles",
   "status": "active"
  },
  {
   "name": "QuickSight Q",
   "maker": "AWS",
   "note": "The standalone NLQ add-on, superseded by and folded into Amazon Q in QuickSight",
   "url": "https://aws.amazon.com/quicksight/q/",
   "oss": false,
   "entry": "\u2014",
   "status": "renamed"
  },
  {
   "name": "Qlik (Answers / Insight Advisor)",
   "maker": "Qlik",
   "note": "Leader in the 2026 Gartner MQ for the 16th consecutive year; strong on exploratory associative analytics, weaker as a startup buy",
   "url": "https://www.qlik.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Zenlytic (Zo\u00eb)",
   "maker": "Zenlytic",
   "note": "$9M Series A (Sep 2024); Zo\u00eb Self-Learning (May 18, 2026) claims the agent builds its own semantic layer off the warehouse in under an hour \u2014 the boldest bet against the modeling-is-the-work thesis. No public pricing; small company, verify runway.",
   "url": "https://zenlytic.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Basedash",
   "maker": "Basedash",
   "note": "AI-native BI with a built-in semantic layer, flat $1,000/mo for 25 users; publishes BI Bench, the only public head-to-head accuracy comparison \u2014 and ranks itself #1 on it",
   "url": "https://www.basedash.com",
   "oss": false,
   "entry": "$1,000/mo + AI usage",
   "status": "active"
  },
  {
   "name": "TextQL (Ana)",
   "maker": "TextQL",
   "note": "Ontology-first agentic analytics for large enterprises (Blackstone, Dropbox, NBA); 64.7% on BI Bench. No public pricing.",
   "url": "https://textql.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Julius AI",
   "maker": "Julius",
   "note": "Consumer-grade AI data analyst (Python/stats over uploaded or connected data), $10M seed Jul 2025; 46.1% on BI Bench. Great for one-off analysis, not a governed BI layer.",
   "url": "https://julius.ai",
   "oss": false,
   "entry": "free tier + per-user",
   "status": "active"
  },
  {
   "name": "Fabi.ai",
   "maker": "Fabi",
   "note": "Lightweight AI analyst notebook aimed at small teams; pricing not published",
   "url": "https://www.fabi.ai",
   "oss": false,
   "entry": "unverified",
   "status": "active"
  },
  {
   "name": "Dot",
   "maker": "getdot.ai",
   "note": "Slack-first AI data analyst on top of your existing semantic layer; small European vendor",
   "url": "https://www.getdot.ai",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Querio",
   "maker": "Querio",
   "note": "AI-first BI for startups; 54.9% on BI Bench, mid-pack",
   "url": "https://querio.ai",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Veezoo",
   "maker": "Veezoo",
   "note": "Swiss knowledge-graph NLQ platform, $6M Series A to scale 'agentic analytics' globally; governance/trust positioning",
   "url": "https://www.veezoo.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Tellius",
   "maker": "Tellius",
   "note": "Conversational + automated-insight analytics; Visionary in the 2026 Gartner MQ",
   "url": "https://www.tellius.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "AnswerRocket",
   "maker": "AnswerRocket",
   "note": "Long-running NLQ vendor (Max agent), CPG/enterprise focus; predates the LLM wave and survived it",
   "url": "https://answerrocket.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "GoodData.AI",
   "maker": "GoodData",
   "note": "Headless/embedded analytics with an AI layer; Visionary in the 2026 Gartner MQ",
   "url": "https://www.gooddata.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Pyramid Analytics",
   "maker": "Pyramid Analytics",
   "note": "Decision-intelligence platform with a natural-language layer; Visionary in the 2026 Gartner MQ",
   "url": "https://www.pyramidanalytics.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Steep",
   "maker": "Steep",
   "note": "Metrics-first, semantic-layer-native AI analytics from a small Nordic team; argues the semantic layer is the product",
   "url": "https://steep.app",
   "oss": false,
   "entry": "free tier",
   "status": "active"
  },
  {
   "name": "Preset / Apache Superset",
   "maker": "Preset",
   "note": "Managed Superset \u2014 the OSS dashboard workhorse; AI/NLQ is the weakest part of the stack, treat as visualization not copilot",
   "url": "https://preset.io",
   "oss": true,
   "entry": "free tier \u00b7 self-host free",
   "status": "active"
  },
  {
   "name": "Rill Data",
   "maker": "Rill",
   "note": "DuckDB/ClickHouse-backed operational dashboards with a code-defined metrics layer; fast exploration, thin chat",
   "url": "https://www.rilldata.com",
   "oss": true,
   "entry": "free OSS",
   "status": "active"
  },
  {
   "name": "Evidence",
   "maker": "Evidence Dev",
   "note": "Markdown+SQL BI-as-code; the anti-chat position \u2014 reproducible reports over conversations",
   "url": "https://evidence.dev",
   "oss": true,
   "entry": "free OSS",
   "status": "active"
  },
  {
   "name": "Holistics",
   "maker": "Holistics",
   "note": "Code-modeled self-service BI (AML semantic layer) with an AI assistant; long-standing SMB/APAC alternative to Looker",
   "url": "https://www.holistics.io",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Domo AI",
   "maker": "Domo",
   "note": "Full-stack BI with agent features; public company, not a 2026 MQ Leader \u2014 sold top-down, rarely the startup answer",
   "url": "https://www.domo.com",
   "oss": false,
   "entry": "consumption credits",
   "status": "active"
  },
  {
   "name": "Sisense",
   "maker": "Sisense",
   "note": "Embedded-analytics veteran with NLQ features; absent from the 2026 Gartner MQ Leaders quadrant \u2014 diligence the roadmap before betting on it",
   "url": "https://www.sisense.com",
   "oss": false,
   "entry": "sales-quoted",
   "status": "active"
  },
  {
   "name": "Narrative Science",
   "maker": "Salesforce / Tableau",
   "note": "The original natural-language-generation BI company, acquired by Salesforce into Tableau (Dec 2021); brand retired, tech absorbed into Tableau's data-storytelling features \u2014 the category's first cautionary tale",
   "url": "https://www.tableau.com",
   "oss": false,
   "entry": "\u2014",
   "status": "acquired"
  }
 ],
 "signals": [
  {
   "fact": "BI market $41.16B (2026) \u2192 $62.38B by 2031, 8.67% CAGR; cloud segment 9.54% \u2014 Mordor frames agentic AI as 'widening BI scope from dashboarding into AI-assisted execution, creating whitespace around governed semantic layers'",
   "src": "https://www.mordorintelligence.com/industry-reports/global-business-intelligence-bi-vendors-market-industry"
  },
  {
   "fact": "Omni: $120M Series C at $1.5B led by ICONIQ, 4x YoY revenue (Apr 23, 2026) \u2014 up from a $650M valuation in Mar 2025",
   "src": "https://omni.co/blog/press-release-omni-series-c-funding"
  },
  {
   "fact": "Sigma: $200M ARR, 2,000+ customers, revenue doubled YoY and 1.1M new active users added (Apr 13, 2026); $80M Series E at $3B with Databricks, ServiceNow and Workday Ventures participating (May 18, 2026)",
   "src": "https://www.sigmacomputing.com/resources/announcements/series-e"
  },
  {
   "fact": "1.5 million Databricks Genie Spaces created in 2026 alone \u2014 the built-in copilots, not the startups, own the volume",
   "src": "https://www.databricks.com/blog/next-generation-databricks-genie"
  },
  {
   "fact": "Fivetran + dbt Labs merger completed Jun 1, 2026, spanning 100,000+ data teams, with 'Agents Schema' \u2014 an open standard putting metric definitions, semantic models and lineage in plain warehouse tables as shared agent context",
   "src": "https://www.getdbt.com/blog/fivetran-dbt-labs-complete-merger-to-create-the-data-infrastructure-for-trusted-ai-agents"
  },
  {
   "fact": "Trust gap: only 51% of 114 data & analytics leaders are confident in AI-generated insights, a third worry about hallucinations, and 26% have already suffered consequences from an inaccurate AI output (insightsoftware, Jun 9, 2026)",
   "src": "https://insightsoftware.com/blog/why-dont-data-leaders-trust-ai-and-other-insights-from-our-2026-ai-survey/"
  }
 ],
 "notes": "Ranking criteria, in order: (1) does the product treat the semantic model as a first-class artifact rather than an afterthought; (2) evidence of answer quality, with independent evidence weighted above vendor claims; (3) whether a sub-50-person company can actually buy it; (4) escape hatches \u2014 open source, MCP, published pricing; (5) commercial durability. No affiliate consideration; picks are editorial.\n\nBenchmark conflicts, resolved: BI Bench (basedash.com/bi-bench) is the only public head-to-head test of shipping conversational-BI products, covering 11 tools on one production-grade schema \u2014 but it is run and published by Basedash, which ranks itself #1 at 92.1%, and carries no publication date. We cite it because nothing else exists, and we flag every use. Its 'default settings' methodology systematically penalizes tools whose accuracy is meant to come from a curated semantic model (Lightdash 23.8%, Snowflake Cortex 19.2%, Metabase 12.4%), so read it as out-of-the-box behaviour, not ceiling. The academic alternative, Spider 2.0, measures research agents rather than products: GPT-4o scores 86.6% on Spider 1.0 and 10.1% on Spider 2.0's enterprise schemas (1,000+ columns), and the current Spider 2.0-Lite leader sits at 76.23% \u2014 the single best quantification of why demo-quality text-to-SQL collapses on a real warehouse.\n\nPricing caveats: Omni, Zenlytic, Sigma, TextQL and ThoughtSpot's real-world enterprise tiers are all sales-quoted; only Hex, Lightdash, Metabase, Basedash and ThoughtSpot's entry SKU publish numbers. Anyone quoting an Omni per-seat price in Aug 2026 is estimating.\n\nAdoption-stat caveat: the widely repeated '29% of employees actually use BI tools' figure traces to BARC's Nov\u2013Dec 2021 survey of 214 data leaders, which put it at 25% and noted no growth over seven years. 2026 restatements attribute it to Gartner without a public citation. The dashboard graveyard is real, but treat the exact number as directional.\n\nFunding caveat: Hex's last confirmed raise is the $70M Series C (May 2025). Aggregator profiles listing 2026 rounds could not be verified against a primary source.\n\nAdjacent elements: warehouses, DuckDB/MotherDuck and the notebook layer are element Wh \u2014 Hex straddles both and is ranked here for its conversational agents, not its notebook. Product and behavioural analytics (PostHog, Amplitude) are element An. Agent evals and LLM observability, including evaluating your BI agent's answers over time, are element Ev. Data movement and transformation (Fivetran, dbt) sit upstream of this element entirely.",
 "sources": [
  "https://omni.co/blog/press-release-omni-series-c-funding",
  "https://docs.omni.co",
  "https://hex.tech/pricing/",
  "https://hex.tech/blog/introducing-threads/",
  "https://learn.hex.tech/docs/getting-started/ai-overview",
  "https://www.lightdash.com/pricing",
  "https://docs.lightdash.com/guides/ai-overview",
  "https://github.com/lightdash/lightdash",
  "https://www.metabase.com/pricing",
  "https://www.metabase.com/docs/latest/ai/settings",
  "https://www.metabase.com/blog/ai-for-everyone-with-confidence",
  "https://www.thoughtspot.com/pricing",
  "https://www.techtarget.com/searchbusinessanalytics/news/366636078/ThoughtSpot-automates-full-platform-with-new-Spotter-agents",
  "https://www.martechcube.com/thoughtspot-leads-2026-gartner-magic-quadrant-for-analytics-bi-platforms/",
  "https://cloud.google.com/blog/products/business-intelligence/looker-updates-for-agentic-bi-at-next26",
  "https://cloud.google.com/blog/products/business-intelligence/looker-in-2026-gartner-analytics-and-bi-platforms-mq",
  "https://www.databricks.com/blog/next-generation-databricks-genie",
  "https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst",
  "https://www.sigmacomputing.com/resources/announcements/series-e",
  "https://www.sigmacomputing.com/resources/announcements/200m-arr",
  "https://www.basedash.com/bi-bench",
  "https://www.basedash.com/pricing",
  "https://spider2-sql.github.io/",
  "https://insightsoftware.com/blog/why-dont-data-leaders-trust-ai-and-other-insights-from-our-2026-ai-survey/",
  "https://hex.tech/state-of-data-teams/",
  "https://barc.com/infographic-bi-analytics-adoption-strategies/",
  "https://www.getdbt.com/blog/fivetran-dbt-labs-complete-merger-to-create-the-data-infrastructure-for-trusted-ai-agents",
  "https://www.mordorintelligence.com/industry-reports/global-business-intelligence-bi-vendors-market-industry",
  "https://www.prweb.com/releases/zenlytic-launches-zoe-self-learning-the-ai-data-analyst-that-onboards-itself-302773223.html",
  "https://blog.bismart.com/en/fabric-copilot-capacity-available-from-f2",
  "https://joulyan.com/en/blog/gartner-releases-2026-magic-quadrant-for-analytics-and-bi"
 ],
 "element": {
  "number": 46,
  "name": "BI Copilot",
  "group": "Data & Analytics",
  "essential": false,
  "edition": "v2026.Q3",
  "revision": "r7",
  "license": "CC BY 4.0 \u2014 cite elems.ai",
  "url": "https://elems.ai/e/bi.html"
 }
}