Pipelines
Scheduled feeds pull data from your store, ad platforms, email and support tools, and alert someone when a feed fails.
Customer Data & Intelligence
We connect your customer data so you can trust the numbers.
Data audits, pipelines, customer matching, warehouses, metric definitions and dashboards, for teams that need one reliable view of their customers.
Trusted numbers
Your systems report different numbers because they count different things.
Before anyone can trust a report, someone has to decide what each number means. We trace every key metric back to its source, explain the gaps and write one definition your team agrees to use.
You get a metric dictionary
One definition, one source and one owner for every number your team relies on.
Revenue, last month
One customer
You can’t see one customer because each system keeps its own record.
Your store knows an email address. Your support desk knows a phone number. Your ad platform knows a device. We build the matching rules that join those records into one customer table, and we show how sure each match is.
- Confirmed
- Same email or customer ID. We join the records.
- Likely
- Same name and address, different email. We flag it for review.
- Unproven
- Not enough evidence. The records stay separate.
Reporting
Stop rebuilding the same report by hand.
If someone exports spreadsheets every week, the problem starts before the report. The fix has three parts, built in this order.
Warehouse and models
The data lands in one warehouse, where it’s cleaned, tested and modeled into customers, orders and campaigns.
Dashboards
Reports read from those models using the agreed definitions. Each one is built once and refreshes itself.
Limits
What your data can’t tell you.
- Exactly which ad caused a sale.
- Each platform claims credit, and people switch devices along the way. We build an attribution model, write down its assumptions and test big budget decisions.
- Who every customer is.
- Some records will never match. We show how much of your customer base is matched and keep the rest separate.
- Why someone left.
- Data shows that a customer stopped buying. Finding out why takes research and conversation.
- What will happen next.
- A forecast is an estimate. We show what it assumes and what would change it.
AI-ready data
Can your data support useful AI? Four things have to be true first.
We check these four, fix what’s missing and connect the data your AI tools need. That can grow into an AI-first customer data platform, built on your systems. If you’re still working out where AI fits, start with Strategic AI Consulting.
The source is known
The tool can show where a fact came from and how current it is.
The definitions are agreed
It uses the same metric definitions as your reports.
The permissions are clear
Customer consent and access rules decide what it can read and use.
A person can step in
Its output is tested on real cases, and you decide when a person takes over.
What we deliver
What you get at each stage.
Start wherever you are. You don’t need all three.
Find out what you have
Audit
- A map of your systems, what each one holds and who owns it
- Data quality findings, worst problems first
- A review of consent and permissions
- A ranked list of what to fix and what it would take
Start here if nobody is sure which numbers to trust.
Connect it
Foundation
- Integrations and pipelines with failure alerts
- A data warehouse with tested models
- Customer matching rules and one customer table
- A metric dictionary with an owner for each metric
- Automated data quality checks
Start here if you already know what’s broken.
Put it to use
Activation
- Dashboards built around your team’s decisions
- An attribution model with its assumptions written down
- Segments that reach your email, ad and site tools
- Documented, permissioned context for AI tools
Start here if the data is sound but nobody is using it.
Bring us
the question.
Where could AI help? What should we build? Let’s work through it together.