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One source of truth for your entire business, from raw data to boardroom KPIs. So every team works from the same numbers, every time.
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It's Monday morning. You open your dashboard. Then you check the spreadsheet. The numbers don't match. You ask your finance lead. Different number again. Here's what that actually costs you:
Sales says revenue is up. Finance says margins are down. Operations doesn't have the data yet. Every meeting starts with 20 minutes of arguing about whose spreadsheet is right.
Cost: every strategic decision starts on shaky ground.Every board meeting is preceded by days of pulling data, reconciling numbers, and formatting slides. Your finance lead spends 3–5 days per month on reporting that should take 15 minutes.
Cost: senior talent buried in manual data wrangling.You built them. Or someone did, once. Now they're stale, half-broken, and everyone's gone back to exporting CSVs. The data underneath was never reliable enough to build on.
Cost: investment wasted, trust in data eroded further.We build the data foundation underneath your business. Not another dashboard, but the layer that makes every dashboard, report, and KPI trustworthy.
Every KPI gets its own scorecard with the formula, the source, and the business context. No more debates about what "revenue" means.
Problems get caught before they reach a decision-maker. Each test has an owner, a direct link to fix the issue at source, and a clear description.
A live view of your full data pipeline: extraction, transformation, delivery. Every stakeholder can see how data flows, which systems are involved, what's current, and where bottlenecks sit.
Finance sees margins and cash flow. Sales sees pipeline and unit economics. Operations sees utilisation and capacity. Same data, purpose-built views for the people who need them.
An AI layer connected directly to your data model. Query live data through Claude and other AI systems. No SQL, no waiting for a developer. Your single source of truth, accessible through conversation.
Monthly company KPIs shared directly with shareholders through an automated metrics exchange. No manual exports, no slide decks cobbled together at midnight. Always current, always consistent.
Five components that together give 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.
View in docs →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.
View in docs →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.
View in docs →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.
View in docs →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.
View in docs →Optional modules that plug directly into the Semantic Nexus. Each one solves a specific high-value problem — built on top of the same single source of truth.
Revenue intelligence
A spreadsheet calculator linked directly to the Semantic Nexus that runs pricing-impact calculations based on historic client usage metrics. Model different pricing scenarios against real usage data — and walk into every upsell conversation with numbers that are already grounded in what the client actually does.
Explore add-on →Account expansion
An AI agent built for customer success teams that surfaces upsell opportunities by analysing multiple leading indicators against benchmarks and client-specific trailing averages. When a metric deviates from its expected pattern, the agent flags it as an opening — giving CS a data-backed prompt to act before the moment passes.
Explore add-on →Data integrity
An AI automation that continuously scans your data for anomalies and surfaces them to the relevant data entry test owners with proposed corrections. Issues get to the right person with enough context to act — before a bad number reaches a report or a decision.
Explore add-on →Risk intelligence
An AI agent that continuously monitors client behaviour patterns and surfaces high-risk signals before they escalate. When risk is detected, Firefighter proposes concrete remediation actions — giving your team a clear playbook with prioritised next steps instead of a vague warning to act on.
Explore add-on →Goal management
A structured OKR template that standardises how clients set, track, and review objectives across teams. Weekly check-ins surface progress and blockers early — with future iterations adding automations, escalation workflows, and call-to-action triggers wired directly into your live metrics.
Explore add-on →Open metrics exchange
Maxq's open metrics exchange — an inter-entity protocol that enables organisations to share standardised performance metrics across company boundaries. Built for investor reporting, board-level benchmarking, and cross-portfolio analytics where consistent metric definitions matter most.
Explore add-on →Business valuation
Generates data-driven business valuations directly from the metrics flowing through Metricsrouter. Applies recognised valuation methodologies to live data — reducing the manual effort of valuation exercises and giving founders, CFOs, and investors a repeatable, defensible number at any point in time.
Explore add-on →Need something specific? We build bespoke add-ons for custom workflows. Let's talk →
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.
View in docs →A systematised way of presenting the data model that strikes the right balance between simplicity and depth for all stakeholders.
View in docs →A structured set of security checks designed for client-side and open-core architectures, where the security environment is inherently more complex.
View in docs →The Semantic Nexus has been deployed across financial services, maritime engineering, safety certification, and AI. Each with their own complexity, each now running on one source of truth.
Moved from a rigid legacy BI tool to a modern, scalable pipeline, unlocking cohort analysis, CAC/ARR tracking, and automated shareholder reporting.
Hour-tracking, planning, bookkeeping, and sales were all in separate systems with conflicting numbers. Now they run on a single model with consistent reporting across departments.
ERP, CRM and bookkeeping data was disconnected. We built a complete stack including 30+ custom HR reports, a REST API for marketplace sync, and embedded analytics.
Ahead of an investment round, they needed consistent metric definitions across all teams. We built a unified stack with embedded analytics for their client portal.
Our team combines backgrounds in data engineering, financial operations, and software development. We work across the full data stack — from extraction to semantic layer — and stay involved from design till operations.
Designed the Semantic Nexus while serving as CFO at ClubCollect. Combines data architecture depth with executive financial experience.
LinkedIn
Specialises in Python automation and cloud-native data applications. Turns complex pipeline requirements into clean, maintainable code.
LinkedIn
Seven years across media, gaming, and retail — from King to Looker Consulting. Brings enterprise-scale data modelling to every engagement.
LinkedIn
Bridges data engineering and financial operations. Keeps client reporting sharp and the company's own numbers in order.
LinkedIn
Studying Information Sciences at Utrecht University. Contributes to pipeline development and data quality across client projects.
LinkedIn
Maintains and develops the Quality Guardian. Drives ongoing improvements and leads the roadmap for Slack-native data correction workflows.
Contributes to the development of the Deal Expander, working alongside the team on implementation and ongoing improvements.
Contributes to the data engineering work behind the Quality Guardian — supporting check configuration and client-side implementation.
A select group of platforms, investors, and specialists who share our standards for data quality and metric driven governance.
We integrate directly into their product to give their clients custom dashboards, agentic workflows, and reports — without needing an in-house data team.
As the data & analytics partner for ISH's portfolio, we implement the Semantic Nexus across their holdings — turning fragmented data into a shared standard.
Visit ISH →
Domain specialists who deepen our work in financial modelling and reporting, extending our capabilities where deep expertise matters most.
Visit Sparck Finance →No licensing fees. No per-seat pricing. No vendor lock-in. The Semantic Nexus is an open architecture: you own everything we build. You only pay our standard rate for the time it takes to set up and maintain.
The full architecture framework, all five product components, and the three core frameworks. Freely available. We believe architecture should never be a barrier.
Ongoing implementation, maintenance, and data engineering. Scale up or down as needed. No long-term contracts required.
Each sprint is 40 hours of a medior data engineer. Scheduled monthly and stackable to accelerate delivery. Work pauses when the budget is reached, unless a follow-up sprint is agreed.
Underlying tool costs (warehouse, extraction, BI) billed directly by vendors
No. Dashboards visualise data. The Semantic Nexus is the layer underneath: the foundation that makes every dashboard reliable. We don't replace your BI tool. We make it trustworthy.
That's exactly why MAXQ exists. Internal builds fail because they lose priority to product work. We've deployed this architecture across different industries. The patterns are proven. The pitfalls are known.
A senior data engineer in the Netherlands costs €115–140K total per year including employer costs. You're looking at 3–6 months to hire and onboard. With MAXQ, you get a full data team's worth of experience starting at €3K/month, delivering within weeks. And if they leave, you don't lose your architecture.
Everything we build is yours. Every repository is version-controlled and client-owned. No proprietary platform, no black-box dependencies. You can switch tools or engineers without losing a thing.
If you have more than 20 people and your CEO can't answer basic questions about the business without asking three people, you already do. The cost of bad decisions on bad data is always higher than the cost of fixing the data.
We work with the leading modern data stack tools, keeping clients in full control of their own infrastructure.
No pitch deck, no discovery workshop, no obligations. Just a direct conversation about your data and what it should be doing for you.
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