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One source of truth for your entire business, from raw data to company-wide agentic workflows. So every team works with the same numbers, every time.
Trusted by
Our open source Semantic Nexus architecture gives your organisation complete visibility into its data — from raw sources to AI-powered querying.
Three purpose-built reporting domains aligned to how companies are structured: Commerce Atlas (go-to-market, unit economics), Finance Atlas (margins, financial operations), and Operations Atlas (utilisation, capacity). Each delivers periodic dashboards and trend analysis tailored to the domain's owners and targets.
Details →A unified, navigable interface that maps your entire data architecture. Source tables are shown post-transformation with aggregations for fast analysis. Each critical metric gets its own dedicated page with a scorecard, numerator/denominator breakdown, and business context provided by the client.
Details →Automated, client-specific tests that continuously validate data entries and business workflows. Each test is assigned an owner, a deeplink for direct resolution at the source, a context description, and a unique test ID — keeping data quality accountable and easy to communicate across teams.
Details →A live flowchart of your full data pipeline from extraction through transformation to consumption. Every stakeholder can see how data moves across your architecture — which systems are involved, what the current status is, and where bottlenecks occur. Invaluable when planning refactors or expansions.
Details →We configure multiple Model Context Protocol servers tailored to specific use cases and agentic workflows — each one exposing the right slice of your semantic layer to the right AI system. Whether it's a customer-facing agent, an internal analyst assistant, or an automated reporting workflow, every MCP server is purpose-built for how that agent actually needs to query your data.
Details →We work with the leading data stack tools, keeping clients in full control of their own infrastructure. We have a tooling agnostic approach when implementing the Semantic Nexus, with a strong preference for open source and self hosted applications.
Sources · Product database
MySQL 
PostgreSQLOS Sources · Bookkeeping
Exact Online 
Yuki Sources · CRM
HubSpot Sources · ERP
Float 
Infor Sources · Customer care
Freshdesk 
Zendesk Sources · Spreadsheets
Google Sheets 
Excel Extraction · streaming (product database)
change data captureOS Extraction · batch (all other sources)
open-source connectorsOS 
python pipelines · codeOS 
no-code · 150+ sources Warehouse · real-time (from the stream)
real-time analyticsOS Warehouse · batch
serverless · Google Cloud 
storage and compute apart 
data warehouse Transformation
models · tests · docs in gitOS Semantic layer
metrics defined once · APIOS semantic models Consumers · AI models
Claude 
OpenAI 
Gemini Consumers · MCPs
nao analyticsOS Consumers · AI agents
Quality Guardian 
LangChain agents Consumers · Reports
Data Studio 
Power BI Consumers · Your own apps
Next.js 
Django The Semantic Nexus is underpinned by three frameworks that cover architecture, performance tracking, and security.
Best practices for building a data pipeline and data model that is robust, flexible, and low cost — refined over years of real client work.
Details →A systematised way of presenting the data model that strikes the right balance between simplicity and depth for all stakeholders.
Details →A structured set of security checks designed for client-side and open-core architectures, where the security environment is inherently more complex.
Details →How one of our clients, Freeday, runs its reporting and operations on the Semantic Nexus we built and maintain for them.
Freeday builds AI digital employees for customer service and finance teams. Maxq designs, runs and maintains their complete analytics stack: Airbyte, dbt and Cube on Snowflake, with the Quality Guardian on top.
Visit Freeday →Ahead of a growth-investor round, Freeday needed one set of metric definitions that finance, sales, operations and the board could all trust: ARR, NRR, CAC payback and the performance of every digital employee. The data sat in six systems: the Freeday platform itself, its ClickHouse event logs, HubSpot, Airtable, Exact Online and Google Sheets.
In every episode a Maxq engineer demonstrates one part of the stack live. Episode 001 explains how an analytics agent answers business questions from your warehouse in a chat, in Slack and in Claude.
Analytics agents, explained: an introduction to nao
Episode 001 · 46 min · Sarah Thiebault and Philip Boontje
Schedule half an hour with Philip. Together you will explore what one source of truth would change for your team, and whether it's worth the investment.
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