Reporting
One source of truth, current to this morning
We put every number your business produces into one modeled layer, define each metric exactly once, and build the reporting your leadership reads to make the call. The dashboard stays current because the platform underneath it is the system your operation already runs on. Nothing gets stitched together the night before a decision.
The problem
What this costs you today
- 01
Two people pull the same metric from two different tools and get two different numbers, so every meeting starts by arguing about whose figure is right.
- 02
Reporting is assembled by hand at month end, which means it lands days late and carries copy-paste errors nobody catches until a decision has already been made on it.
- 03
Your revenue, your margin, and your delivery numbers live in the CRM, the accounting system, and a folder of spreadsheets that never quite agree with each other.
- 04
By the time the board deck is finished, the moment to act on what it shows has already passed.
- 05
Leadership steers on lagging indicators because assembling anything close to real time is too much manual work to attempt more than once a month.
What we build
Data and dashboards, built around you
One definition per metric
Every number your business runs on gets defined once, in a single modeled layer, so revenue means the same thing in the sales review that it means in the board deck. When a definition changes, it changes in one place and every report follows it.
Pipelines that refresh on their own
We pull from your CRM, your accounting system, your operational tools, and the spreadsheets nobody wants to touch, on a schedule instead of by hand. The numbers reconcile against figures you already trust before anyone reads them.
Reporting built around your decisions
The dashboards are shaped around the calls you actually make, not a template's idea of a KPI, with a drill path from the top-line number down to the individual record behind it. Someone can act on what they see the same day it appears.
Numbers that reconcile, or say why not
Bad and missing records get flagged instead of quietly averaged into a total that looks fine and is wrong. The reporting tells you when a source has gone stale or a figure will not tie out, rather than hiding it.
History deep enough to see the trend
We bring your historical records into the same model, so comparisons and trends reach back as far as your data allows, not just as far as the current tool happens to remember.
The intelligence layer
Agents that know how your business runs
A chat window bolted to the side of your software knows nothing about your operation. We build the platform and the model context underneath it together, so the agents work inside your data rather than guessing at it from the outside.
An MCP server over your real metric layer
We expose your modeled data and its definitions to AI agents as first-class tools through a custom MCP server, so an agent queries the same governed numbers your dashboards read rather than guessing from a text box. It reads the actual warehouse, not a screenshot of it.
Agents that explain the number, not just show it
When margin moves or a metric crosses a threshold, an agent traces the variance back through the underlying records, drafts the explanation in plain language, and posts it before your team goes looking. It works inside the data and follows your reporting rules, so the answer is grounded in the same figures leadership already trusts.
Every reading is auditable after the fact
Each agent logs which records it read, which rule it applied, and how it arrived at the number, so a figure in the board deck can be traced back and defended weeks later. The reasoning stays on the record instead of vanishing into a chat window.
How it goes
From the first call to the platform
- 01
We map how you measure
We sit inside how each number is calculated today, where it lives, and the decision it is meant to drive, before we model anything.
- 02
We model the data and build the layer
We design the metric layer, stand up the warehouse and pipelines, and consolidate your sources into one queryable place that reconciles against your known figures.
- 03
We build the reporting and prove it ties out
We build the dashboards and agents around your KPIs, set role-based access, and validate every headline number against a figure you already trust before anyone relies on it.
- 04
We run the data layer with you
We keep the pipelines healthy, fix them fast when a source tool changes its format, and add metrics and views as your questions evolve.
After launch
It does not end at go-live
Once the reporting is live, your leadership stops assembling numbers by hand and starts reading a single screen that is current to this morning. Meetings begin with the decision instead of an argument about whose figure is right. We keep the pipelines running, watch the agents, and extend the model as the business asks new questions. Because we own and run the platform this reporting sits on, keeping the data layer trustworthy as you grow is our job, not a handoff you inherit.
Questions
The questions worth asking early
- We already run Power BI or Looker. Does that get thrown out?
- No. The dashboard tool is rarely the problem. We build the modeled data and pipelines underneath it so the tool your team already knows finally reads from one governed source. What we fix is the mess feeding it, not the chart on top.
- How is this different from the reporting our other systems already show?
- Those reports each measure inside one tool and stop at its edge, which is exactly why they disagree with each other. This is one layer that sits across all of them and defines each metric once, so the number reads the same everywhere it appears. It is the reporting face of the single platform your operation runs on, not a seventh dashboard to reconcile.
- Who keeps the numbers accurate when a source tool changes?
- We do. We build the platform, own it, and run it, so when a source system changes its export or you add a new tool, adjusting the pipeline is our responsibility rather than yours. The reporting flags a source that has gone stale instead of showing you a confident wrong number.
- How do the AI agents know they are reading the right figure?
- They query the same governed metric layer your dashboards read, through an MCP server that exposes your definitions as tools, so an agent cannot invent its own version of revenue. Every reading is logged with the records it touched and the rule it applied, so you can audit any number it reports.
- How fast can leadership actually act on what they see?
- The promise is the plain one: one source of truth, and reporting leadership can act on the same day. The pipelines refresh on a schedule and the metric layer reconciles before anyone reads it, so the screen is current rather than a snapshot from last month's close.
The first conversation costs an hour.
We take on a small number of partnerships, because we carry the engineering risk on every one. The first call is where we both find out whether this is one of them.