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Evidence-bound summary — expand sections for movement, risks, and signals.
Memo snapshot · May 20, 2026, 6:11 PM
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TL;DR
Seed (YC)About | Kaelio
Raised $500K across 1 funding round. Latest: $500K Pre-seed (Jan 2025). Investors: Y Combinator. (High).
Funding
Raised $500K across 1 funding round. Latest: $500K Pre-seed (Jan 2025). Investors: Y Combinator. (High).
Product / news
24 product/news‑styled row(s); headline risk without filings (High).
Verified facts
+5 more in Recent movement below
Spring 2025 batch; governed context layer for agentic analytics across the data stack.
Weak funding evidence: mention does not include a parsed raise amount/round and may refer to non-funding context.
No open roles indexed yet.
The index price and activity score are algorithmic estimates based on observed public company-level signals. They may be incomplete, stale, or inaccurate and are not investment, legal, tax, or business advice.
Kaelio:reddit_credentials_not_configured
Source types found
Strongest / recent news-style rows
Pricing · Thu, Jul 2, 03:27 PM · confidence 90%high quality
About page · Thu, Jul 2, 03:27 PM · confidence 90%high quality
Wed, May 20, 06:11 PM · confidence 88%high quality
Newest first · 35 event(s)
Source: Pricing
Choose how to use Kaelio: free open-source ktx, hosted ktx Cloud, or a managed Data Agent built on your context layer.
Source: Blog
Insights on context layers, data agents, and the infrastructure behind governed, trusted analytics from Kaelio.
Source: About page
Kaelio builds ktx, ktx Cloud, and managed Data Agents so teams can give AI systems governed business context and approved metrics.
Source: Homepage
ktx teaches agents how to query your warehouse accurately. It learns your metric definitions, joinable columns, and business knowledge, then writes them as reviewable YAML and Markdown in git. Apache 2.0. Run locally.
Source: hackernews
Spring 2025 batch; governed context layer for agentic analytics across the data stack.
Source: Blog / news
How data teams are deploying AI data agents to clear BI backlogs, reduce ad-hoc request volume, and shift from reactive reporting to proactive analytics.
Source: Blog / news
A practical implementation guide written for data engineers and analytics engineers. Covers the connect-govern-activate workflow, schema and lineage ingestion, dbt and BI integration, MCP, CI/CD patterns, and how to keep a context layer accurate as your stack…
Source: Blog / news
GTM analytics consolidates acquisition, retention, and expansion metrics into one governed view, enabling founders to identify which growth motions actually...
Source: Blog / news
Most revenue teams achieve forecast accuracy within 10% only 43% of the time, primarily due to inconsistent data definitions and ungoverned metrics. See how a governed context layer fixes that.
Source: Blog / news
See how CMOs get real-time pipeline visibility by layering a governed context layer under their existing data stack, so any agent can deliver trusted, sourced answers.
Source: Blog / news
Learn how analytics leaders can shift from reactive report-pulling to proactive intelligence that surfaces insights before anyone asks. A practical playbook for escaping the report factory.
Source: Blog / news
A practical guide to data quality gates for AI analytics agents, covering freshness, completeness, uniqueness, relationships, schema changes, semantic checks, and review routing.
Source: Blog / news
A practical guide to data lineage for AI analytics, covering source traceability, transformation paths, semantic definitions, audit evidence, and context-layer design for trusted agent answers.
Source: Blog / news
A practical guide to data contracts for AI analytics, covering where contracts help with interface stability and ownership, and where you still need metric governance, security controls, and a context layer.
Source: Blog / news
See how conversational analytics helps SaaS teams get trusted, sourced answers faster, with a governed context layer sitting underneath existing BI tools.
Source: Blog / news
Explore how conversational analytics optimizes data warehouse operations, enhancing accessibility and trust for SaaS teams.
Source: Blog / news
Learn how conversational analytics enhances dbt and BI workflows by grounding natural-language answers in governed context, lineage, and existing metric definitions.
Source: Blog / news
Explore how conversational analytics enhances customer-facing applications by pairing natural language access with governed context, metric consistency, and existing permissions.
Source: Blog / news
Kaelio leads the embedded analytics market for natural-language querying, followed by ThoughtSpot Sage, Snowflake Cortex Analyst, Holistics AI, and Querio....
Source: Blog / news
Explore top conversational analytics tools for RevOps teams and discover how Kaelio enhances data governance and insights.
Source: Blog / news
Compare leading AI-powered GTM analytics tools for sales leaders and see how a governed context layer helps teams get trusted, sourced answers across revenue data.
Source: Blog / news
Compare the best AI data analyst tools for Amazon Redshift, including native Amazon Q in QuickSight, ThoughtSpot, Tableau Pulse, and Hex. Learn how a governed context layer like Kaelio makes every tool more accurate by grounding answers in your Redshift schem…
Source: Blog / news
Compare the best AI data analyst tools for Databricks, including native Databricks AI features and third-party options. Learn how a governed context layer like Kaelio makes every tool more accurate. Covers governance, accuracy, Unity Catalog integration, and…
Source: Blog / news
Compare the best AI analytics tools for product teams, covering feature adoption tracking, user behavior analysis, and cross-functional data access without SQL or analyst dependencies.
Source: Blog / news
Learn how AI data query tools let business teams ask data questions in plain English instead of SQL, eliminating the analytics backlog and democratizing data access across your organization.
Source: Blog / news
A reference architecture for AI analytics covering warehouses, semantic layers, context layers, agent interfaces, access controls, and evaluation loops for production-grade deployments.
Source: Blog / news
A practical AI analytics readiness checklist for data leaders covering metrics, context, permissions, evaluation, and rollout controls before production deployment.
Source: Blog / news
A practical observability framework for AI analytics covering answer quality, context coverage, policy enforcement, lineage, latency, cost, and feedback loops after launch.
Source: npm_registry
Placeholder — name reserved by Kaelio.
Source: npm_registry
Standalone ktx context layer for data agents
Source: hackernews
1 row(s)
The company's own site — the authoritative description of what they sell and to whom. Marketing-controlled, so treat claims as positioning rather than verified traction.
ktx teaches agents how to query your warehouse accurately. It learns your metric definitions, joinable columns, and business knowledge, then writes them as reviewable YAML and Markdown in git. Apache 2.0. Run locally.
Why it matters: Primary source — the company's own positioning; best read for what they sell and to whom, not for traction claims.
Open source ↗4 row(s)
Third-party press coverage. Independent reporting corroborates company claims; repeated coverage across outlets is a momentum signal.
Choose how to use Kaelio: free open-source ktx, hosted ktx Cloud, or a managed Data Agent built on your context layer.
Why it matters: Independent coverage — third-party corroboration of company claims; recurring coverage indicates rising visibility.
Open source ↗Kaelio builds ktx, ktx Cloud, and managed Data Agents so teams can give AI systems governed business context and approved metrics.
Why it matters: Independent coverage — third-party corroboration of company claims; recurring coverage indicates rising visibility.
Open source ↗Why it matters: Independent coverage — third-party corroboration of company claims; recurring coverage indicates rising visibility.
Open source ↗Why it matters: Independent coverage — third-party corroboration of company claims; recurring coverage indicates rising visibility.
Open source ↗1 row(s)
Funding announcements and investor-database records. The strongest public signal of capitalization: round, amount, and syndicate quality when disclosed.
Spring 2025 batch; governed context layer for agentic analytics across the data stack.
Why it matters: Funding signal — Pre-Seed per this source; verify against the linked original before relying on it.
Open source ↗4 row(s)
Public engineering activity. Sustained commits, releases, and stars indicate real product development and, for dev tools, developer adoption.
kaelio context manager\
Why it matters: Engineering signal — public repo activity evidences active development and possible developer adoption.
Open source ↗Placeholder — name reserved by Kaelio.
Why it matters: Engineering signal — public repo activity evidences active development and possible developer adoption.
Open source ↗Standalone ktx context layer for data agents
Why it matters: Engineering signal — public repo activity evidences active development and possible developer adoption.
Open source ↗Why it matters: Engineering signal — public repo activity evidences active development and possible developer adoption.
Open source ↗1 row(s)
Social presence and mentions. A soft signal — useful for gauging attention and launch reception, not a substitute for verified traction.
Why it matters: Attention signal — social mention or presence; soft signal of market interest.
Open source ↗24 row(s)
Company blog and newsletters. Shipping cadence and technical depth of posts hint at product velocity and team quality.
Insights on context layers, data agents, and the infrastructure behind governed, trusted analytics from Kaelio.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗How data teams are deploying AI data agents to clear BI backlogs, reduce ad-hoc request volume, and shift from reactive reporting to proactive analytics.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical implementation guide written for data engineers and analytics engineers. Covers the connect-govern-activate workflow, schema and lineage ingestion, dbt and BI integration, MCP, CI/CD patterns, and how to keep a context layer accurate as your stack…
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗GTM analytics consolidates acquisition, retention, and expansion metrics into one governed view, enabling founders to identify which growth motions actually...
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Most revenue teams achieve forecast accuracy within 10% only 43% of the time, primarily due to inconsistent data definitions and ungoverned metrics. See how a governed context layer fixes that.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗See how CMOs get real-time pipeline visibility by layering a governed context layer under their existing data stack, so any agent can deliver trusted, sourced answers.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Learn how analytics leaders can shift from reactive report-pulling to proactive intelligence that surfaces insights before anyone asks. A practical playbook for escaping the report factory.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical guide to data quality gates for AI analytics agents, covering freshness, completeness, uniqueness, relationships, schema changes, semantic checks, and review routing.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical guide to data lineage for AI analytics, covering source traceability, transformation paths, semantic definitions, audit evidence, and context-layer design for trusted agent answers.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical guide to data contracts for AI analytics, covering where contracts help with interface stability and ownership, and where you still need metric governance, security controls, and a context layer.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗See how conversational analytics helps SaaS teams get trusted, sourced answers faster, with a governed context layer sitting underneath existing BI tools.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Explore how conversational analytics optimizes data warehouse operations, enhancing accessibility and trust for SaaS teams.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Learn how conversational analytics enhances dbt and BI workflows by grounding natural-language answers in governed context, lineage, and existing metric definitions.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Explore how conversational analytics enhances customer-facing applications by pairing natural language access with governed context, metric consistency, and existing permissions.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Kaelio leads the embedded analytics market for natural-language querying, followed by ThoughtSpot Sage, Snowflake Cortex Analyst, Holistics AI, and Querio....
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Explore top conversational analytics tools for RevOps teams and discover how Kaelio enhances data governance and insights.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Compare leading AI-powered GTM analytics tools for sales leaders and see how a governed context layer helps teams get trusted, sourced answers across revenue data.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Compare the best AI data analyst tools for Amazon Redshift, including native Amazon Q in QuickSight, ThoughtSpot, Tableau Pulse, and Hex. Learn how a governed context layer like Kaelio makes every tool more accurate by grounding answers in your Redshift schem…
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Compare the best AI data analyst tools for Databricks, including native Databricks AI features and third-party options. Learn how a governed context layer like Kaelio makes every tool more accurate. Covers governance, accuracy, Unity Catalog integration, and…
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Compare the best AI analytics tools for product teams, covering feature adoption tracking, user behavior analysis, and cross-functional data access without SQL or analyst dependencies.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗Learn how AI data query tools let business teams ask data questions in plain English instead of SQL, eliminating the analytics backlog and democratizing data access across your organization.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A reference architecture for AI analytics covering warehouses, semantic layers, context layers, agent interfaces, access controls, and evaluation loops for production-grade deployments.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical AI analytics readiness checklist for data leaders covering metrics, context, permissions, evaluation, and rollout controls before production deployment.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
Open source ↗A practical observability framework for AI analytics covering answer quality, context coverage, policy enforcement, lineage, latency, cost, and feedback loops after launch.
Why it matters: Company publishing — post cadence and depth hint at product velocity.
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