Querio
An AI-native analytics platform that stores metrics, dashboards, and analysis in customer-owned GitHub repositories.
Website: https://querio.ai/
Cover Block
Public sources
| Field | Value |
|---|---|
| Company | Querio |
| Tagline | An AI-native analytics platform that stores metrics, dashboards, and analysis in customer-owned GitHub repositories [querio.ai/launch, October 2026] |
| Headquarters | London, UK [Tech.eu, October 2026] |
| Founded | 2024 [Crunchbase, retrieved 2026] |
| Stage | Seed [FinSMEs, October 2026] |
| Business model | SaaS |
| Industry | Other |
| Technology | AI / Machine Learning |
| Geography | Western Europe |
| Growth profile | Venture Scale |
| Founding team | Co-Founders (2), Rami Abi Habib and Javier Bonilla [Crunchbase, retrieved 2026] |
| Funding label | Seed |
| Total disclosed funding | ~$5,000,000 [FinSMEs, October 2026] |
Links
Public sources
- Website: https://querio.ai/
- LinkedIn: https://www.linkedin.com/company/querioai
- GitHub: https://github.com/Querio-App/unshackle
Executive Summary
PUBLIC Querio is a London-based analytics startup that wants to move business intelligence out of proprietary BI layers and into customer-owned GitHub repositories, a design choice that stands out because it treats metrics, dashboards, definitions, permissions, and analysis as version-controlled assets that can also be used by AI tools such as Claude, ChatGPT, and Slack [querio.ai/launch, October 2026] [AP/EIN Presswire, October 2026]. The timing matters because the company launched publicly in October 2026 alongside a disclosed $5 million seed round, giving investors an early read on a product thesis that is tightly aligned with the shift toward AI-assisted data work [FinSMEs, October 2026] [Tech.eu, October 2026].
The founding story is still only lightly documented in public sources, but the record does establish that Querio was founded in 2024 by Rami Abi Habib and Javier Bonilla, with Abi Habib serving as CEO and Bonilla identified as co-founder and CTO on the launch materials [Crunchbase, retrieved 2026] [querio.ai/launch, October 2026]. Public reporting and company materials describe the product as an AI-native analytics platform with a data copilot, notebooks, embedded analytics, and direct warehouse connectivity, while the more differentiated claim is that the analytics context itself lives in the customer repository rather than inside Querio's own application layer [Crunchbase, retrieved 2026] [Querio, retrieved 2026] [querio.ai/launch, October 2026].
On team quality, the clearest relevant datapoint is Abi Habib's reported prior work across Amazon warehouse operations and Alexa product management, which suggests some combination of operational and product exposure before founding the company [Prompt and Circumstance, October 2026]. Bonilla's public footprint in the provided sources is thinner, but the launch page and LinkedIn-based references consistently place him as co-founder, and the public GitHub release history for Querio's open-source migration engine gives at least some external evidence of technical execution in the open [querio.ai/launch, October 2026] [GitHub, retrieved 2026].
The business model is presented as SaaS, and the financing base includes BMW i Ventures, LAUNCH, Haatch Ventures, Araya, Forum Ventures, and Secways, with additional angel participation from operators associated with OpenAI, Slack, Robinhood, and Google according to funding coverage [FinSMEs, October 2026] [Crunchbase, retrieved 2026]. A notable product-adoption wedge is "unshackle," Querio's free open-source migration engine for legacy analytics environments, which could reduce switching friction if it works as advertised across real-world dashboard estates [FinSMEs, October 2026] [GitHub, retrieved 2026].
Over the next 12 to 18 months, the key question is less whether the story is legible and more whether the company can turn a persuasive architectural argument into repeatable production deployments. Public evidence today supports the product concept and the seed financing, but named customers, broader usage metrics, and independent proof that GitHub-native analytics governance materially improves buyer outcomes remain limited in the current source set [Tech.eu, October 2026] [AP/EIN Presswire, October 2026] [Skywork.ai].
Lightly corroborated -- Core company identity, founding, launch timing, and funding are corroborated by FinSMEs, Tech.eu, Crunchbase, and company launch materials, but several product-capability details remain company-sourced or single-source.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Stage | Seed |
| Business Model | SaaS |
| Industry / Vertical | Other |
| Technology Type | AI / Machine Learning |
| Geography | Western Europe |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding | Seed, total disclosed approximately $5,000,000 |
How the Company Got Here
PUBLIC
Querio presents as a young analytics software company built around a specific architectural bet: analytics logic should live in assets the customer owns, rather than inside a closed BI application. The company describes itself as an AI-native analytics platform, and its launch materials say metrics, dashboards, definitions, permissions, and analysis are stored as version-controlled files in a customer-owned GitHub repository [Querio, retrieved 2026]. Crunchbase lists Querio as a London, UK company founded in 2024, with Rami Abi Habib and Javier Bonilla named as co-founders [Crunchbase, retrieved 2026].
The public milestone record is short but clear. Querio says it launched publicly on October 27, 2026, alongside a disclosed $5 million financing and a product position centered on making analytics context available to tools such as Claude, ChatGPT, and Slack [Querio, retrieved 2026]. Crunchbase separately categorizes the company as seed stage and lists data copilot, data notebooks, and embedded analytics among the product surfaces associated with the business [Crunchbase, retrieved 2026].
Within that timeline, the main operating signal is product breadth rather than commercial scale. Querio's website frames the platform as a data system for teams and their customers, while the same launch materials introduce "unshackle," an open-source migration engine intended to move analytics assets from legacy systems into Querio's model [Querio, retrieved 2026]. No legal entity details appear in the cited company website or Crunchbase materials reviewed for this section, so the public record here is largely limited to founding, location, launch, and product framing [Crunchbase, retrieved 2026] [Querio, retrieved 2026].
Lightly corroborated -- Grounded in Crunchbase and company website materials; founding, headquarters, and launch framing are corroborated, but legal entity detail is not established in the cited sources.
Product and Technology
Sources and analysis
Querio is making a specific architectural argument, not simply adding a chat layer to business intelligence. The company says its platform stores metrics, dashboards, definitions, permissions, and analysis as version-controlled files inside a customer-owned GitHub repository, with the aim of making analytics logic portable, reviewable, and usable across multiple AI tools [querio.ai/launch, October 2026]. On its homepage, Querio describes the product as a data platform that lets teams and customers explore data through agentic notebooks for instant insights [Querio, retrieved 2026]. Press coverage and directory listings also describe adjacent product surfaces including a data copilot, data notebooks, and embedded analytics [Crunchbase, retrieved 2026].
The functional pitch rests on connecting governed analytics context to both databases and AI interfaces. Querio says it connects directly to major warehouses and databases including Snowflake, BigQuery, Postgres, Databricks, Redshift, ClickHouse, MySQL, SQL Server, MongoDB Atlas, and SAP HANA, and that it does so through read-only encrypted connections rather than duplicating data [Supported integrations | Querio Documentation, retrieved 2026] [Querio Brings Natural-Language Chat to Every Major Data Platform - Querio, retrieved 2026]. Public launch materials say that context can then be surfaced to tools including Claude, ChatGPT, and Slack [querio.ai/launch, October 2026] [AP/EIN Presswire, October 2026]. Tech.eu reports that Querio's agents can identify inconsistencies, update dependent definitions and reports, and route proposed changes back to the data team for approval, which suggests a supervised workflow rather than fully autonomous edits [Tech.eu, October 2026].
Querio has also attached an open-source migration wedge to the core platform. FinSMEs reported the launch of "unshackle," a free migration engine for moving analytics assets from legacy systems [FinSMEs, October 2026]. The public GitHub release describes a workflow that checks credentials, inventories assets, extracts and imports content, executes notebooks, verifies migrated numbers against the source system, and generates dashboard-level reports on what translated cleanly and what remains blocked, with untranslated elements marked for follow-up as TODO(unshackle) [GitHub, retrieved 2026]. That matters because migration friction is one of the main reasons incumbent BI systems persist; the evidence here does not yet show adoption at scale, but it does show Querio is trying to reduce switching cost in a concrete way.
Lightly corroborated -- Draws on company materials, one independent press report, FinSMEs, Crunchbase, and Querio's public GitHub release; several core product claims remain company-sourced.
Where the Demand Sits
Public sources The market matters now because generative AI is changing how employees query data, but the control layer for metrics, definitions, and permissions still sits largely inside older BI systems, creating an opening for vendors that repackage analytics context for both humans and software agents [Tech.eu, October 2026] [AP/EIN Presswire, October 2026].
The constraint here is evidentiary rather than conceptual. No third-party TAM, SAM, or SOM figures for Querio’s exact category were provided in the source set, so market sizing has to be framed through analogous public markets rather than direct category estimates. Public materials place Querio at the intersection of business intelligence, embedded analytics, data notebooks, and AI assistants for enterprise data workflows, with a product that stores analytics logic in customer-owned GitHub repositories and exposes that context to tools such as Claude, ChatGPT, and Slack [Querio, retrieved 2026] [Crunchbase, retrieved 2026] [querio.ai/launch, October 2026]. That positioning suggests its practical budget line is less a net-new software category than a reallocation of existing BI, analytics engineering, and data enablement spend.
Demand signals in the reviewed coverage are specific enough to outline the buying motion, even if they do not yet quantify category size. Querio’s public launch materials describe the buyer as the data team, with business users consuming answers through AI assistants and collaboration tools [AP/EIN Presswire, October 2026]. Tech.eu’s description of agents that can identify inconsistencies, update dependent definitions and reports, and route proposed changes for approval points to a second tailwind: enterprises want AI-assisted self-serve analytics, but still need reviewability and governance before metric changes propagate across the business [Tech.eu, October 2026]. In plain terms, the product is trying to benefit from AI adoption without asking data leaders to surrender change control.
Adjacent markets matter because they define both expansion paths and substitution risk. Querio’s open-source "unshackle" migration engine is aimed at moving analytics assets from legacy environments, which indicates direct overlap with BI migration budgets and modernization projects already attached to incumbents such as Tableau, Power BI, and Looker [FinSMEs, October 2026] [GitHub, retrieved 2026]. Its warehouse connectivity across systems including Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, and SAP HANA also places it near data catalog, semantic layer, notebook, and embedded analytics workflows, where buyers often prefer to consolidate vendors rather than add one more interface layer [Supported integrations | Querio Documentation, retrieved 2026]. That broad adjacency is useful for wedge expansion, but it also means Querio is entering crowded purchasing committees rather than a greenfield budget.
Regulatory and macro forces cut both ways. On the supportive side, customer-owned repositories, version control, and read-only warehouse connections align with a broader enterprise preference for auditability, least-privilege access, and lower data movement when AI tools are introduced into analytical workflows [querio.ai/launch, October 2026] [Querio Brings Natural-Language Chat to Every Major Data Platform - Querio, retrieved 2026]. On the limiting side, the current public record is still largely company-authored or launch-coverage based, and there is no cited third-party market report in the source pack establishing how quickly enterprises will standardize on Git-based analytics context as opposed to extending existing BI estates. That leaves the market case credible at the level of direction, but still early at the level of proven category formation.
| Analogous market framing | Public evidence | Relevance to Querio |
|---|---|---|
| Business intelligence and analytics platforms | Querio is described as an analytics platform with dashboards, notebooks, embedded analytics, and plain-English querying [Crunchbase, retrieved 2026] [Best Embedded Analytics Reviews 2026 | Gartner Peer Insights - Querio, retrieved 2026] |
| Analytics migration and modernization | Querio launched "unshackle," a free open-source migration engine for legacy analytics assets [FinSMEs, October 2026] [GitHub, retrieved 2026] | Suggests wedge into replacement projects |
| AI assistants for enterprise data access | Querio says analytics context is made available to Claude, ChatGPT, and Slack [querio.ai/launch, October 2026] [AP/EIN Presswire, October 2026] | Ties product to agent-enabled workflow spending |
| Data governance and semantic context management | Querio stores metrics, definitions, permissions, and analysis as version-controlled files in customer-owned repositories [querio.ai/launch, October 2026] | Frames differentiation around control and portability |
The table points to a market thesis built from converging budgets rather than one cleanly reported category. That can be attractive if the product truly compresses several workflows, but it also means adoption will depend on whether buyers view Git-native analytics control as a necessary layer or an added abstraction.
Lightly corroborated -- Section relies on named public sources for product positioning and demand drivers, but no third-party market sizing report for Querio’s exact category was available in the provided evidence.
Competitive Landscape
MIXED Querio is positioning itself less as another dashboard layer and more as a control plane for analytics logic, which places it in partial competition with BI incumbents such as Tableau, Power BI, and Looker while also pulling against repository-native and AI-assistant workflows that sit adjacent to classic business intelligence [querio.ai/launch, October 2026] [Crunchbase, retrieved 2026].
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Querio | AI-native analytics platform for data teams and their AI agents | Seed, $5M disclosed | Stores metrics, dashboards, definitions, permissions, and analysis as version-controlled files in a customer-owned GitHub repository | [querio.ai/launch, October 2026] [FinSMEs, October 2026] |
| Tableau | Analytics and dashboarding platform | Competitor named in structured facts | Named incumbent competitor in analytics | [Structured facts] |
| Power BI | Analytics and business intelligence platform | Competitor named in structured facts | Named incumbent competitor in analytics | [Structured facts] |
| Looker | Analytics and business intelligence platform | Competitor named in structured facts | Named incumbent competitor in analytics | [Structured facts] |
The clearest way to read the field is by workflow. Querio is trying to relocate that center of gravity into a customer-owned GitHub repository, then make the same context available to tools such as Claude, ChatGPT, and Slack, which suggests a wedge aimed at teams that already treat analytics as a software development problem rather than a reporting problem [querio.ai/launch, October 2026] [AP/EIN Presswire, October 2026].
That creates a second competitive set made up of adjacent substitutes rather than only named BI vendors. A data team can keep existing dashboards in place and still shift parts of documentation, version control, and AI-assisted analysis into notebooks, repos, and collaboration tools. Querio's open-source migration engine, unshackle, matters in that context because it reduces the cost of testing a move away from legacy systems, at least for teams willing to translate BI assets into a code-centered workflow [FinSMEs, October 2026] [GitHub, retrieved 2026].
Querio's edge today appears to be product architecture rather than scale. The company says analytics logic is stored as version-controlled files in a repository the customer owns, and Tech.eu reports that its agents can identify inconsistencies, update dependent definitions and reports, and send proposed changes to the data team for approval [querio.ai/launch, October 2026] [Tech.eu, October 2026]. If that workflow works in production, the benefit is governance with portability: teams keep reviewability and change history in systems they already trust. The durability of that edge is less certain. GitHub-centered analytics is differentiated now, but it is also perishable if larger BI platforms add stronger repository sync, notebook workflows, or native AI context layers on top of their installed base.
The main exposure is distribution. Tableau, Power BI, and Looker enter accounts with broad analyst familiarity, existing procurement paths, and entrenched dashboard estates, while Querio is a 2024-founded company that publicly launched in October 2026 with a $5 million seed round [FinSMEs, October 2026] [Crunchbase, retrieved 2026]. Even if the product is technically cleaner for modern data teams, the company does not yet appear, from the reviewed public sources, to own a channel comparable to Microsoft's, Salesforce's, or Google's ecosystem reach. That makes Looker the most specific benchmark on product philosophy, because teams that want governed self-serve on live warehouse data are already in its orbit, and Querio will need to prove that repository ownership and AI-tool interoperability justify switching costs [The Best AI Platforms for Data Driven Decisions - Querio, retrieved 2026] [Best Looker Alternatives for Self-Serve Analytics (2026) - Querio, retrieved 2026].
The most plausible 18-month scenario is a bifurcated market. Winner if repository-native analytics becomes a real buying criterion for data teams is Querio, because its positioning is already aligned to that shift and unshackle gives it a practical migration story [querio.ai/launch, October 2026] [GitHub, retrieved 2026]. Loser if buyers continue to prefer analytics bundled inside a broader software estate is Looker or Tableau only at the margin, in the specific slice of technical teams that want customer-owned logic and AI-readable context; in the broader market, the smaller and more exposed player is still Querio if incumbent platforms absorb enough of this functionality before it builds referenceable deployments [Tech.eu, October 2026] [AP/EIN Presswire, October 2026].
Opportunity
PUBLIC
The prize here is not a better dashboard tool, it is a chance to become the control layer for how analytics logic is created, versioned, migrated, and consumed as AI becomes a first-class interface for business data [Querio, retrieved 2026] [AP/EIN Presswire, October 2026] [Tech.eu, October 2026].
The headline opportunity is straightforward. If Querio can persuade data teams to keep metrics, dashboards, definitions, permissions, and analysis in customer-owned GitHub repositories, it has a plausible path to becoming foundational infrastructure for AI-native analytics workflows rather than a thin interface on top of existing BI stacks [querio.ai/launch, October 2026] [AP/EIN Presswire, October 2026]. That matters because the company is not only pitching natural-language querying, a crowded surface, but a different system of record for analytics context, one that is portable, reviewable, and available to tools including Claude, ChatGPT, and Slack [querio.ai/launch, October 2026] [Querio, retrieved 2026]. The launch of the open-source migration engine, unshackle, also gives the thesis a practical wedge: if migration from legacy BI is easier, the company can meet buyers at the moment they reconsider their analytics stack rather than waiting for a greenfield decision [FinSMEs, October 2026] [GitHub, retrieved 2026].
The upside path is easier to see when separated into concrete scenarios rather than a single all-or-nothing bet.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| GitHub becomes the analytics control plane | Querio becomes the default system for storing governed metrics and dashboard logic as code, with AI tools reading from the same source of truth | Data teams adopt the GitHub-centered architecture as they standardize AI access to analytics context | Querio already positions the repository, not the application layer, as the durable home for metrics, definitions, permissions, and analysis [querio.ai/launch, October 2026] [AP/EIN Presswire, October 2026] |
| Open-source migration unlocks enterprise replacement cycles | unshackle lowers switching friction from legacy BI products and gives Querio a repeatable wedge into entrenched accounts | The free migration engine gains adoption among teams evaluating Tableau, Power BI, or Looker alternatives | Querio publicly launched unshackle as a free open-source migration engine, and its GitHub release materials describe inventory, extraction, verification, and reporting workflows rather than a simple file converter [FinSMEs, October 2026] [GitHub, retrieved 2026] |
| AI analytics distribution through existing work tools | Querio wins because analytics context becomes available inside collaboration and assistant tools employees already use daily | Usage through Claude, ChatGPT, and Slack drives broader internal adoption than conventional BI seat expansion | Querio's launch materials explicitly frame Claude, ChatGPT, and Slack as surfaces for consuming analytics context, which suggests a distribution path beyond the standalone dashboard login [querio.ai/launch, October 2026] [AP/EIN Presswire, October 2026] |
The compounding mechanism would come from control, migration, and workflow depth reinforcing one another. A team that stores analytics definitions in a repository it owns is more likely to treat Querio as part of the operating system for data work, especially if agents can identify inconsistencies, update dependent definitions and reports, and route proposed changes back to the data team for approval [Tech.eu, October 2026]. Add a migration layer that inventories existing dashboards, flags translation gaps, verifies migrated numbers against the source, and produces implementation reports, and the product starts to look less like a single-purpose assistant and more like infrastructure that becomes harder to unwind as usage spreads [GitHub, retrieved 2026]. If that motion works, each migration creates more governed analytics assets in the repository, which in turn makes the AI surfaces more useful and raises the cost of returning to a proprietary logic store.
The size of the win is best framed as a scenario, not a forecast. The public source set here does not provide a named third-party market size figure, so the cleanest comparable is the scale of the incumbents Querio is explicitly challenging, including Tableau, Power BI, and Looker, which sit inside large and durable analytics budgets [Crunchbase, retrieved 2026]. If Querio were to become a category-level control layer for AI-native analytics across enterprise data teams, the outcome could support multi-billion-dollar enterprise value on mature software infrastructure economics (scenario, not a forecast). The evidence on hand is still early and largely launch-stage, but the architecture choice, the open-source migration wedge, and the distribution thesis through existing AI and collaboration tools make the upside legible rather than merely conceptual [querio.ai/launch, October 2026] [FinSMEs, October 2026] [AP/EIN Presswire, October 2026].
Lightly corroborated -- This section relies on one independent funding report, one independent launch report, public GitHub materials, and company launch materials; the strategic upside case is grounded, but several product-scope claims remain partly company-sourced.
Sources
Public sources
[querio.ai/launch, October 2026] Querio Launch | https://querio.ai/launch
[Tech.eu, October 2026] UK analytics platform Querio launches, with $5M raise | https://tech.eu/2026/10/27/uk-analytics-platform-querio-launches-with-5m-raise/
[Crunchbase, retrieved 2026] Querio - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/querio
[FinSMEs, October 2026] Querio Raises $5M in Seed Funding | https://www.finsmes.com/2026/10/querio-raises-5m-in-seed-funding.html
[AP/EIN Presswire, October 2026] Querio Launches with $5 Million to Give Data Teams Control of Company Analytics as Work Moves to AI | https://apnews.com/press-release/ein-presswire-newsmatics/press-release-43c31463cbc55090316b7a2648eaea94
[Querio, retrieved 2026] Querio: The AI Data Analytics Platform | https://querio.ai/
[Prompt and Circumstance, October 2026] Interview with Rami Abi Habib | Querio | https://pandcpodcast.com/15/transcript
[GitHub, retrieved 2026] Release unshackle 0.1.0 · Querio-App/unshackle | https://github.com/Querio-App/unshackle/releases/tag/v0.1.0
Articles about Querio
- Querio Is Storing Company Dashboards in Customer GitHub Repos — The London-based startup, backed by BMW i Ventures and OpenAI angels, treats dashboards and definitions as version-controlled files.