We help teams restore trust in their data
We help teams restore trust in their data
We help teams restore trust in their data
We help teams restore trust in their data
...and keep it that way.
...and keep it that way.
...and keep it that way.
FIG 1.0: METRIC DRIFT
Trust doesn't fail loudly
Trust doesn't fail loudly
Trust doesn't fail loudly
Over time, manual work creeps back in. Numbers that once aligned start producing different answers.
Over time, manual work creeps back in. Numbers that once aligned start producing different answers.
Over time, manual work creeps back in. Numbers that once aligned start producing different answers.
Teams spend more time explaining how results were produced than acting on them.
Teams spend more time explaining how results were produced than acting on them.
Teams spend more time explaining how results were produced than acting on them.
We help teams restore trust in their data...
As the business evolves,
what used to be a simple metric now spans more teams, systems, and edge cases than it was designed for.
As the business evolves,
what used to be a simple metric now spans more teams, systems, and edge cases than it was designed for.
As complexity increases,
logic that once lived in one place is now split across pipelines, tools, and ad-hoc workarounds.
As complexity increases, logic that once lived in one place is now split across pipelines, tools, and ad-hoc workarounds.
When reality drifts faster than systems can keep up, definitions change in practice but systems continue reporting based on old assumptions
When reality drifts faster than systems can keep up, definitions change in practice but systems continue reporting based on old assumptions
When customer truth lives in too many places, different teams rely on different sources to answer the same question, depending on context.
When customer truth lives in too many places, different teams rely on different sources to answer the same question, depending on context.
After systems go live, real usage begins, and reality sets in, gaps appear that weren't visible during implementation or testing
After systems go live, real usage begins, and reality sets in, gaps appear that weren't visible during implementation or testing
We help teams restore trust in their data...
As the business evolves,
what used to be a simple metric now spans more teams, systems, and edge cases than it was designed for.
As complexity increases, logic that once lived in one place is now split across pipelines, tools, and ad-hoc workarounds.
When reality drifts faster than systems can keep up, definitions change in practice but systems continue reporting based on old assumptions
When customer truth lives in too many places, different teams rely on different sources to answer the same question, depending on context.
After systems go live, real usage begins, and reality sets in, gaps appear that weren't visible during implementation or testing
/// CONSEQUENCES
→
Manual work becomes the system of record.
→
Decisions slow while numbers are reconciled.
→
Trust erodes quietly, then suddenly.
/// THE REALITY
This isn't a data problem.
It's a reconciliation problem.
It shows up when spreadsheets appear before executive meetings and decisions stall while someone "double-checks."
None of this is accidental. It's what happens when systems change faster than ownership models.
/// CONSEQUENCES
→
Manual work becomes the system of record.
→
Decisions slow while numbers are reconciled.
→
Trust erodes quietly, then suddenly.
/// THE REALITY
This isn't a data problem.
It's a reconciliation problem.
It shows up when spreadsheets appear before executive meetings and decisions stall while someone "double-checks."
None of this is accidental. It's what happens when systems change faster than ownership models.
We don’t compete on volume.
We don’t compete on volume.
We don't start by building. We start by making friction visible enough to decide.
We don't start by building. We start by making friction visible enough to decide.
We don't absorb ambiguity. We force clarity on what the numbers mean and who owns them.
We don't absorb ambiguity. We force clarity on what the numbers mean and who owns them.
We don't staff projects. We get involved only when the work can land.
We don't staff projects. We get involved only when the work can land.
When not to bring us in
When not to bring us in
We're not a fit when:
We're not a fit when:
You need extra capacity to burn down a backlog.
You need extra capacity to burn down a backlog.
You want a vendor to quietly implement without challenging definitions.
You want a vendor to quietly implement without challenging definitions.
If this feels familiar...
If this feels familiar...
The right next step is simply to make it visible without committing to fixing anything yet.
The right next step is simply to make it visible without committing to fixing anything yet.
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