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What Analytics Teams teams need most
WHAT WE BUILD
Data platforms & applied AI
Metric Layer
One definition per metric, used by every dashboard and query.
Modeled Datasets
Tables shaped around the questions teams actually ask.
Self-Serve Dashboards
Filterable views on modeled data, with the metric definitions attached.
Cohort & Funnel Analysis
Groups tracked over time to show where people drop out.
Anomaly Alerts
Unexpected moves flagged with the segment that caused them.
Narrative Reporting
Recurring summaries written from the numbers, not by hand.
Where Analytics Teams teams lose time
An analytics team spends its week answering questions that arrive in chat. One asks for a churn number, another wants last quarter split by region, and each request begins with a search for the right table. The dashboard built in March is already stale and nobody trusts it.
We build the definitions and the data once, then let people read from them. A metric layer holds the shared definitions, modeled datasets carry the fields that matter, and alerts point at changes before someone asks why a chart moved.
01
Numbers disagree
Two dashboards report different active user counts because each one filters churn, trials and internal accounts in its own way, and nobody agrees.
02
Requests bypass the model
An urgent question gets a fresh query on raw tables, and that number is quoted in a meeting the next day.
03
Reports nobody opens
Dashboards are built to answer one question, then kept alive for years without an owner, a refresh schedule or a reader.
HOW THE BUILD RUNS
OUR PROCESS
Step 01
Walk the workflow
We sit with the people who do the work and follow one job from first contact to final invoice. The gaps show up quickly once nobody is describing them from memory.
Step 02
Model the data
Bookings, orders, patients or jobs become one set of records with clear owners. We agree on the fields before any screen gets built.
Step 03
Build in stages
The costliest step ships first, usually the one where revenue or time leaks. Each stage goes live while the next one is being built.
Step 04
Hand over and support
Your team gets training, written runbooks and a support window. We stay close through the first month of real use, then step back.
SYSTEMS WE CONNECT TO
INTEGRATIONS
We build around what you already run. These are the systems we most often read from, write to, or replace one part of, without asking your team to start over.
Data warehouses and lakes
Identity and access systems
Help desk and CRM tools
Document storage
Monitoring and evaluation tools
THE TARGET
6-8 wk
Pilot to Production
Typical timeline for a scoped, evaluated AI pilot.
THE EXPERIENCE
100%
Evaluation Coverage
Every AI output path tested before release.
THE COVER
24/7
Guardrails
Monitoring, fallbacks, and cost controls running continuously.
THE HANDOVER
1
Source of Truth
A single governed warehouse behind reporting and AI.
OTHER SPECIALTIES
AI & Data Intelligence
QUESTIONS & ANSWERS
Questions about Analytics Teams
The questions that come up before a ai & data intelligence engagement starts.
Do we need a semantic layer?
When more than one team reports the same metric, yes. Definitions in one place end the email chain about whose number is right.
Can business users build their own reports?
On modeled data with documented fields, they can. That is the point of the model, and it frees the analytics team from one-off requests.
How do you handle ad hoc questions?
We build the notebook and query access so the team can go straight to the data, with the metric definitions still applied.
Can we keep our current BI tool?
Yes. The metric layer sits under whatever tool you use, so the definitions travel with it. Changing tools later becomes a smaller job because the logic is not trapped in dashboards.
How do you handle a question the model cannot answer?
It goes back to the analytics team as a documented request, and the answer is added to the model if it will be asked again. The point is to stop the same question being solved from scratch.
What does the first month look like?
We pick the metrics that cause arguments, define them with the people who own them, and rebuild the top reports on those definitions. That usually settles more confusion than a new dashboard would.
HOW WE HELP
The wider team behind this work
Most specialty builds draw on more than one service line. One team, one scope, no handoffs between vendors.
Talk to us about analytics teams.
Tell us what your team runs today and where it breaks. A real person replies, usually the same day.
Start a conversation
Start a conversation
contact us
Let's Talk About Your Project
Scope the problem and its constraints
Review your current product and stack
Agree on timeline, team, and budget
Cover security and compliance needs
Plan integrations with your tools
Leave with a clear first milestone
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