One set of numbers everyone agrees on.
For leadership teams who spend the first half of every meeting debating whose numbers are right. We agree the definitions first, then build the data pipelines and dashboards that bring finance, sales, marketing and operations into one view, with AI-drafted commentary that explains what changed. Decisions get made on evidence, and sooner.
Signs you need this
If two or more of these sound familiar, there's likely time and money to recover.
Month-end takes a week of spreadsheets
Someone exports from Xero, the CRM and the ad platforms, then stitches it together by hand. By the time the report is ready, the month is half over.
Two reports, two answers
Marketing counts leads one way, sales counts them another and finance sees different revenue again. Meetings debate the numbers instead of the decisions.
Margin by customer or channel is invisible
Revenue is visible, but the cost to win and serve each customer, product or channel isn't. Growth can look healthy while profit erodes.
Reports describe, nobody explains
Dashboards show that a number moved but not why. Leaders still have to dig, or wait for an analyst, before they can act.
What changes for the business
Definitions agreed before anything is built
We write down what each measure means, where it comes from and who owns it, and get sign-off from finance, sales and operations. Arguments about the number are settled once, not every month.
Automated data pipelines
Data flows from Xero, the CRM, ad platforms, e-commerce and job systems on a schedule, with checks for missing or unusual values. Reporting stops depending on one person's spreadsheet.
Margin, not just revenue
Joining cost, revenue and acquisition data shows profit by customer, product, channel or site. You can put money and effort where they actually return.
Commentary that explains the change
AI drafts a short weekly or monthly narrative on what moved, the likely drivers and what to watch, citing the figures it used. A person reviews it before it reaches the leadership team.
Built around decisions
Each dashboard answers specific questions, such as where next month's budget should go or which accounts need attention. Fewer charts, more action.
A faster management pack
With extraction and reconciliation automated, the monthly pack no longer waits on manual exports. Leaders act on this month's numbers while they still matter.
What we build
Metric dictionary
Every measure defined, with its formula, source system, owner, refresh frequency and known limitations.
Data pipelines and central database
Scheduled extraction from source systems into a central PostgreSQL database, with validation checks and alerts when data is late or wrong.
Leadership dashboard
One view of the measures that run the business, with drill-down by site, team, channel or customer.
Team dashboards
Focused views for sales, marketing, operations and finance, each tied to the decisions that team actually makes.
AI commentary and alerts
Drafted weekly or monthly commentary, plus alerts when a measure moves outside its expected range, each reviewed by a person.
Management reporting pack
An automated board or management pack drawn from the same data, so the meeting and the dashboard agree.
Technology we work with
- PostgreSQL
- TypeScript and Node.js
- Cloudflare
- Xero
- HubSpot
- Salesforce
- Shopify
- Google Analytics 4
- Microsoft Power BI
- Looker Studio
How it runs
Timings are indicative and depend on your systems, data and approvals.
Decisions and definitions
We start with the decisions leaders need to make, then agree the measures and definitions that support them.
1 to 3 weeks (indicative)
Data audit
We check each source system for completeness, consistency and access, and flag what must be fixed before it can be trusted.
1 to 2 weeks (indicative)
Pipelines and data model
We build the extraction, cleaning and modelling layer, with tests that catch broken or missing data.
3 to 8 weeks (indicative)
Dashboards and commentary
Dashboards are built, reviewed with each team and refined, and AI commentary is tuned and checked against the figures.
2 to 4 weeks (indicative)
Operate and refine
We monitor data quality, add measures as the business changes and retire charts nobody uses.
Ongoing
Worked example
Online retail: reporting on margin, not platform ROAS
An illustrative $8M online homewares brand on Shopify and Klaviyo is paying more for paid social every quarter, while three agencies report three versions of success. One contribution-margin model, rebuilt retention flows and a fortnightly creative testing cadence show which spend pays for itself and bring more revenue from customers the brand already has.
- Shopify, Klaviyo and ad platforms
- 3PL, payment and Xero costs
- Data connector
- Contribution-margin model
- One dashboard
- AI-drafted weekly commentary
- Human review and decisions
- Budget, creative and flow changes
Guardrails, fit and measurement
Guardrails we apply
- Every measure has a written definition and an owner, and any change to a definition needs sign-off before it reaches a dashboard.
- AI commentary works only from your data model, cites the figures it used, flags uncertainty and is reviewed by a person before circulation.
- Access is role-based, so salary, margin and customer-level data are visible only to the people who need them.
- Pipelines include data-quality tests and alerts, so a broken feed is caught before anyone makes a decision from it.
- Pipelines are scheduled within each platform's API limits. Xero, for example, allows 60 calls per minute and 5,000 calls per day for each connected organisation.
Not the right fit if
- Businesses whose source data is unreliable and nobody owns fixing it. A dashboard on bad data spreads bad decisions faster, so we would start with the data.
- Teams that need a single answer once. For a one-off question, an analysis is cheaper than a pipeline.
- Organisations already well served by the reporting inside their accounting or CRM platform. Improving what you have may be enough.
What we measure
- Working days from month-end to management pack
- Hours spent preparing reports each month
- Share of leadership measures with an agreed definition and owner
- Data freshness and pipeline failure rate
- Dashboard use by role
- Actions agreed and followed up from the weekly review
Pricing
Reporting and dashboard work is scoped individually, usually after an Altitude Diagnostic or a fixed-scope discovery, once we know how many systems and measures are involved.
Questions, answered.
Why not just use Power BI or Looker Studio?
Often you should. They are capable front ends, and if your team already uses one we can build the definitions and pipelines underneath it. The hard part is rarely the chart: it is agreed definitions and reliable data, which is where most of the work goes.
How much does a reporting system cost, and how is it scoped?
We scope reporting individually, usually after a Diagnostic or a fixed-scope discovery. Cost depends on the number of source systems, the condition of their data, how many measures and dashboards you need and how often they refresh. A sensible first release covers the leadership view, with team dashboards to follow.
How do you make sure the AI commentary is accurate?
The AI works only from the figures in your data model, cites the numbers it used and is instructed to flag uncertainty rather than guess. A person reviews each commentary before it is circulated, and we test it against past months where the drivers are already known.
How long until we have a working dashboard?
As an indication, agreeing definitions and auditing the data takes 2 to 5 weeks, and a first leadership dashboard follows 5 to 12 weeks later, depending mostly on data quality. We would rather show you a smaller dashboard you trust than a larger one you don't.
Who owns the data, pipelines and dashboards, and what if Altitude Creative disappears?
You do. Data stays in databases and accounts in your organisation's name, the pipeline code is yours and every measure is documented in the metric dictionary. Another analyst or developer could pick up from that documentation, and source-code escrow can be added to the agreement if you want further protection.
Related services
- System integrationsConnect your CRM, Xero, job system and email so nobody re-keys data. APIs, webhooks and sync rules, plus monitoring that alerts you to failures first.
- Custom CRM developmentA CRM built around your pipeline, accounts, quoting and service history, with AI that drafts follow-ups and does the data entry. Owned by you, not rented.
- Paid mediaGoogle, Meta, LinkedIn and YouTube ads managed to booked revenue, with CRM sales sent back to the platforms. Spend paid direct to them, never marked up.
- AI assistants & knowledge systemsRole-specific AI assistants that answer from your own documents, cite the source and respect permissions. Built on Microsoft 365, Google or a custom stack.
Where this matters most
Agree the numbers, then build the dashboard
Start with a discovery that defines your measures and tests whether your data can support them.
Sources
Third-party facts on this page, checked 14 October 2026.
- API limits (developer FAQ), Xero Developer. developer.xero.com/faq/limits