Data context aware AI

  • Curated data ensures trust, consistency, and explainability

  • Metadata and business rules provide guiding context

  • Answers grounded in data - test and compare competing hypotheses

High-fidelity data discourse

  • Uncover data-backed answers that move business KPIs forward

  • Explore ‘why’, ‘what if’, ‘how do I’ questions, without any wait.

  • Use natural language to brainstorm with your data freely

Day-zero intelligence

Day-zero intelligence

  • Feed full data context to AI without plumbing hassles
  • Connect your warehouse and start conversing instantly
  • High-quality, data-backed answers — from the first question
  • Iterate faster — improve response quality with small changes
  • Ship in days, not months — production-ready on day zero

Data-local by design

  • Runs natively on top of your existing data estate

  • Supports data warehouses, databases, federated SQL engines

  • Supports single or multiple data sources - on-premises or on cloud

  • No rewrites, glue code, or re-platforming needed

Supported data stack

Built for builders Trusted by leaders

For developers

  • Instantly feed AI with complete data context
  • Focus on use case development — not plumbing
  • Test, compare, and swap models — use best-fit AI
  • Maintain full control — build and deploy on your stack
  • Ship higher-quality analytics products in days, not months
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For data analysts

  • AI-powered ‘digital twin’ to co-pilot analysis
  • Skip catalog & query grunt work — focus on insights
  • Tunable first drafts — iterate faster with REPL-style interface
  • Significant lift in answer quality with minor changes
  • Deliver strategic, story-driven analyses that win buy-in
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For business executives

  • Bridge analytics quality gap with data context aware AI
  • Empower users to dig deeper into data using plain language
  • Reduce ad-hoc requests and maximize team efficiency
  • Decision-grade intelligence — production-ready from day zero
  • Runs where your data lives — maximize value of current stack
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For analytics executives

  • Brainstorm with AI that feels like a trusted, data-savvy expert
  • Get data backed answers to ‘why’, ‘what-if’, and ‘how do I’
  • Self-serve data exploration — no delays, no analyst wait time
  • Explore freely in natural language — no slicers & dimensions
  • Actively shape KPIs and drive the analysis behind them
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Unlike any other

Supported data sources

Data warehouse intelligence

  • Runs natively on existing data estate ​
  • Supports leading warehouses, databases, and federated SQL engines​
  • Supports single or multi-estate, across cloud or on-premises

DIY tools

  • Each source must be wired and maintained manually, effort grows with estate size ​

Native DW Copilots / Assistants

  • Limited to the vendor’s own data warehouse

Data context for AI

Data warehouse intelligence

  • AI shaped by full data context — curated data, metadata, schema, business rules

DIY tools

  • Context modeling performed manually, updates demand ongoing effort

Native DW Copilots / Assistants

  • Sees only table metadata; little or no business semantics

Data discourse

Data warehouse intelligence

  • High-fidelity data discourse
  • Explore “why,” “what-if,” “how do I” questions
  • Hypothesis testing, reasoning, validation, recommendations
  • Low-effort improvement loops

DIY tools

  • Depth and quality depends on custom code
  • Improving result quality (even marginally) requires significant additional cycles

Native DW Copilots / Assistants

  • Basic NLQ; limited causal reasoning or validation depth

Time to market

Data warehouse intelligence

  • Day-zero intelligence
  • Production-grade from the start

DIY tools

  • Assembly, tuning, and QA can take weeks to months
  • New/addendum use cases have equally high turnaround times

Native DW Copilots / Assistants

  • Hours for basic Q&A; months for deeper use cases

Control

Data warehouse intelligence

  • Accelerate with low-code, code when needed
  • Reuse existing IP, inject proprietary logic, models, and custom functions

DIY tools

  • Full control, but with high build and maintenance burden

Native DW Copilots / Assistants

  • Limited extension; mostly UI-level settings

Outbound integrations

Data warehouse intelligence

  • MCP server (coming soon)
  • Seamless third-party integrations via API
  • Efficient starter kits for large AI projects

DIY tools

  • Each integration is hand-coded and orchestrated
  • More time and effort spent on software assembly than use case development

Native DW Copilots / Assistants

  • Limited, with few controls

Data warehouse intelligence

  • Runs natively on existing data estate ​
  • Supports leading warehouses, databases, and federated SQL engines​
  • Supports single or multi-estate, across cloud or on-premises

DIY tools

  • Each source must be wired and maintained manually, effort grows with estate size ​

Native DW Copilots / Assistants

  • Limited to the vendor’s own data warehouse

Data warehouse intelligence

  • AI shaped by full data context — curated data, metadata, schema, business rules

DIY tools

  • Context modeling performed manually, updates demand ongoing effort

Native DW Copilots / Assistants

  • Sees only table metadata; little or no business semantics

Data warehouse intelligence

  • High-fidelity data discourse
  • Explore “why,” “what-if,” “how do I” questions
  • Hypothesis testing, reasoning, validation, recommendations
  • Low-effort improvement loops

DIY tools

  • Depth and quality depends on custom code
  • Improving result quality (even marginally) requires significant additional cycles

Native DW Copilots / Assistants

  • Basic NLQ; limited causal reasoning or validation depth

Data warehouse intelligence

  • Day-zero intelligence
  • Production-grade from the start

DIY tools

  • Assembly, tuning, and QA can take weeks to months
  • New/addendum use cases have equally high turnaround times

Native DW Copilots / Assistants

  • Hours for basic Q&A; months for deeper use cases

Data warehouse intelligence

  • Accelerate with low-code, code when needed
  • Reuse existing IP, inject proprietary logic, models, and custom functions

DIY tools

  • Full control, but with high build and maintenance burden

Native DW Copilots / Assistants

  • Limited extension; mostly UI-level settings

Data warehouse intelligence

  • MCP server (coming soon)
  • Seamless third-party integrations via API
  • Efficient starter kits for large AI projects

DIY tools

  • Each integration is hand-coded and orchestrated
  • More time and effort spent on software assembly than use case development

Native DW Copilots / Assistants

  • Limited, with few controls
Demo Data Warehouse Intelligence

Data context aware AI
on Snowflake

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