Reporting & analytics
See what changed without rebuilding the view.
Bring recurring measures into a clear reporting rhythm, so the time goes into understanding the numbers rather than collecting them.
From scattered data to clear decisions.
We engineer dependable data architecture, executive analytics, and practical AI systems for businesses that need trusted numbers and automated operational flow.
01 Practice
Data systems, decision tools and AI that fit the way your company already runs.
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Data engineering & architecture
We design and build the pipelines, warehouse, and data models that bring your operational systems into a dependable shape, using the cloud and tools that fit your needs.
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Analytics, visual BI & reporting
We turn business questions into clear dashboards, visual analysis, and recurring reports so teams can see what is changing and decide what to do next.
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Data science & commercial forecasting
We use statistical analysis and forecasting to investigate demand, retention, margins, and other questions where better evidence can improve a decision.
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Practical AI engineering & automation
We build and deploy models, embeddings, search, and AI workflows around a defined task, with evaluation, access controls, and human review where the work calls for it.
How we work We work from assessment and design through implementation and handover. Senior practitioners stay close to the work, and the systems are built for your team to own and operate. How an engagement works →
Across the operating stack
The data is already there. The system is what makes it useful.
Payments, commerce, CRM, support, finance, analytics, and cloud tools each hold part of the answer. We bring the useful signals together, define the measures, and make them ready for a decision.
02 Demonstrations
Explore a few working examples.
One page your leadership actually reads.
Live dashboards, plus a written executive brief delivered every Monday at 07:00, generated from verified warehouse models, not assembled by hand in spreadsheets.
Measure
Period
Automated brief / Monday 07:00
Bring separate sources into a useful model.
See how operational sources can feed a shared data model, with checks that help teams spot inconsistencies before they reach a report.
View
A workflow that prepares work for review.
This example shows how an AI workflow can read a request, check it against available information, and prepare a next step for a person to approve.
Workflow
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- Idle
03 Outcomes
Good systems reduce friction around decisions.
Data and AI should make the next step easier to see, not create another layer of reporting to manage.
Data engineering
Work from definitions people can stand behind.
Connect important sources and model the measures that matter, so teams are working from the same basis for a decision.
AI & integrations
Move routine work forward, with judgment intact.
Use integrations and AI to prepare repetitive work for review, while people stay responsible for the calls that need context.
A self-serve product
Explore your data stack on your own terms.
For teams who want a direct view of their data stack, our self-serve product helps surface data health, schema changes, and shifts in key metrics. Get started on your own, then bring in our team when you need help making sense of the findings or building what comes next.
Use the product independently, or pair it with a custom engagement across data engineering, analytics, and AI.
05 Contact
Tell us where the numbers hurt.
Tell us what you are trying to understand or improve. We’ll reply with a useful next step and an honest view of where we can help.
Prefer direct email? hello@16-62.co
Start with the situation
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