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.

  • Perform hypothesis testing, reasoning, and validation on-demand

Day-zero intelligence

Day-zero intelligence

  • Feed full data context to AI without plumbing hassles
  • Connect your warehouse and start conversing instantly
  • Get high-quality, context-rich answers from the very first query
  • Iterate faster — better answers with small changes
  • Ship in days, not months — production-ready from the start

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

Demos

See it in action

inforgraphic inforgraphic
Build RAG systems and knowledge graphs Build RAG systems and knowledge graphs
Discover data assets using natural language Discover data assets using natural language
Transform data using natural language Transform data using natural language
Auto-insights and instant visualizations using natural language Auto-insights and instant visualizations using natural language
All demos

Built for builders Trusted by leaders

For developers

  • Instantly feed AI with full data context and deliver high-impact AI and analytics use cases
  • Build, test, and ship AI-driven analytics in weeks, instead of months
  • Get more value from your current stack — no new clusters or orchestration needed
  • Stay in control. Code when needed and embed your own IP — models, logic, or functions — without rewrites
  • Accelerate outcomes with Gathr.ai’s efficient starter kits, Data+AI Copilot, and more

For data analysts

  • Instantly feed AI with full data context and deliver high-impact AI and analytics use cases
  • Build, test, and ship AI-driven analytics in weeks, instead of months
  • Get more value from your current stack — no new clusters or orchestration needed
  • Stay in control. Code when needed and embed your own IP — models, logic, or functions — without rewrites
  • Accelerate outcomes with Gathr.ai’s efficient starter kits, Data+AI Copilot, and more

For business executives

  • Instantly feed AI with full data context and deliver high-impact AI and analytics use cases
  • Build, test, and ship AI-driven analytics in weeks, instead of months
  • Get more value from your current stack — no new clusters or orchestration needed
  • Stay in control. Code when needed and embed your own IP — models, logic, or functions — without rewrites
  • Accelerate outcomes with Gathr.ai’s efficient starter kits, Data+AI Copilot, and more

For analytics executives

  • Instantly feed AI with full data context and deliver high-impact AI and analytics use cases
  • Build, test, and ship AI-driven analytics in weeks, instead of months
  • Get more value from your current stack — no new clusters or orchestration needed
  • Stay in control. Code when needed and embed your own IP — models, logic, or functions — without rewrites
  • Accelerate outcomes with Gathr.ai’s efficient starter kits, Data+AI Copilot, and more

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