Services Data and business intelligence
Decide on numbers your team has stopped arguing about.
We build the data foundation and reporting your team stops arguing about, so decisions run on numbers rather than a week of spreadsheet work.
25+ years in enterprise IT, including Fortune 500 regulated-data environments.
No preparation needed. You leave with next steps in writing.
Assess, build, embed
- 25+ yrs enterprise IT
- Fortune 500 regulated-data environments
- Founder-led
Decide on numbers your team has stopped arguing about, and stop paying for a week of spreadsheet work ahead of every decision.
The problem
You pay for the same number twice.
Once in the analyst days spent assembling it, and again in the decision made late because it was not ready. The cause is ordinary: data in disconnected systems, reporting that is slow and manual, and leadership making calls on instinct in the gap. AI makes this worse rather than better when it runs on data nobody trusts, because it produces confident wrong answers faster than a person could.
What we do
What comes back: the days, and the argument.
- The weekly assembly work disappears, because the data that drives your decisions is consolidated and modeled once rather than gathered by hand each cycle.
- Reporting arrives in time to act on, built on sources that reconcile with each other instead of on a spreadsheet somebody stitched together overnight.
- AI runs on inputs you can defend, which is what keeps a wrong answer from propagating at machine speed.
- A disputed figure has an answer rather than an argument, because ownership of where every number comes from is written down.
In practice
From scattered sources to a foundation you trust.
- Sources we consolidate
- The systems your decisions actually depend on, such as your ERP, CRM, finance, and operational databases, brought together instead of reconciled by hand every month. The warehouse is usually the one you already run and already pay for, commonly Snowflake, BigQuery, Databricks, or Microsoft Fabric.
- Data-quality work
- A broken assumption fails loudly on the day it breaks rather than in a quarterly clean-up that costs a week. Deduplication, reconciliation across systems, and clear definitions mean a number means the same thing everywhere it appears, and the tests run inside the pipeline in the shape dbt tests or Great Expectations describe.
- Semantic layer
- Removes the recurring cost of three teams calculating the same metric three ways. A governed set of shared definitions means terms like active customer or revenue are defined once and reused, not rebuilt in every report.
- Lineage you can follow
- Turns "the dashboard says" into "here is where it came from", which is the difference between a report and evidence, and the difference between a five-minute answer and a day of tracing. For each number: the path back to the source system, the transformation that produced it, and the version of the job that ran, captured in the shape OpenLineage describes rather than in tribal knowledge.
- Dashboards and telemetry
- Reporting on the decisions that matter, alongside operational telemetry from the agents we deploy, so you can see what the automation did, what it changed, and what capacity it actually freed.
What you get
What you get, and the decision it unlocks.
- Consolidated, modeled data for your priority decisions, so the assembly work stops recurring
- A data-quality pass with cross-system reconciliation and shared definitions
- A semantic layer of governed metric definitions, so the same question stops producing three answers
- Dashboards in one place, so a leader can see which segments are actually profitable and act on it this week, instead of waiting for a hand-built spreadsheet
How we deliver it
One definition per number.
Every agent we deploy reports what it did as it does it, so what is actually happening is a record you can read rather than a reconstruction that costs somebody a day. The same discipline runs through the reporting: each metric is defined once, in one place, and every dashboard reads that definition instead of recalculating it its own way, which is where contradictory numbers come from.
How a governed agent runs
Where this shows up
See the work in context.
Is this you
- Who this is for
- Best for leadership making calls on instinct because the reporting is slow, manual, or contradictory, and paying analyst days for the privilege.
- Who it is not for
- Not for teams that already have a trusted, governed data foundation and only need one more dashboard. If the data is sound and the real problem is that two systems never talk to each other, that is systems integration.
Questions
Questions leaders ask.
- Do we have to replace our data stack?
- No, and replacing it is usually the most expensive way to solve this. We build on the warehouse and BI tools you already run and already pay for, whether that is Snowflake, BigQuery, Power BI, or Looker, rather than moving you onto something new to suit us.
- Why does this matter before AI?
- AI amplifies whatever it runs on. On data nobody trusts, it produces confident wrong answers faster than anyone can catch them, which turns a data problem into a rework problem. A clean, defined foundation is what makes AI outputs defensible.
- What is a semantic layer, and why do we need one?
- It is one governed place where each metric is defined, so a term like active customer means the same thing in every report and every agent. Without one, the same question gets answered three ways and somebody spends the afternoon working out which is right.
If this is on your plate, let's talk.
A twenty minute intro call is the simplest next step: we work out which step fits, and you leave with one specific thing to act on. If you want the senior read on your business rather than a routing conversation, that is the AI Leverage Briefing.
Within one business dayYour scope is settled, in writing.
What you walk away with Prioritized 90-Day Roadmap · Risk Register · Governance and Compliance Gap Assessment · Safe-to-Deploy Read
25+ years in enterprise IT, including Fortune 500 regulated-data environments.
If AI is not the right tool for your problem, you will hear that from us.