No new software for the team. No dashboards to learn. BModelr listens to the conversations the team already has, turns them into a structured operating model, and surfaces the friction that quietly costs them money.
A single bot registered inside the company's own Microsoft 365 tenant — their data never leaves their walls. Most of the three hours was just waiting on Microsoft to propagate settings.
Remaining time: Microsoft policy propagation (hands-off). Net hands-on effort ≈ 90 minutes.
BModelr's Model Agent turned a plain-English description of the corporate-travel operation into a causal value chain — every process, the drivers that move it, and how they connect.
12 corporate drivers + 5 friction drivers = 17 tracked. Regenerated in seconds whenever the business changes.
In its first week the bot says nothing. It reads the team's natural chatter and attributes it to people and drivers — so within days we can see whether the model's focus areas and owners actually match how the team works, and tune them before anyone is asked to do a thing.
messages observed
operational signals classified
team members contributing
major cost pattern surfaced
Phase 0 is one week: align on the right drivers and owners from real interactions — calibration, not measurement.
A single booking error consumed most of a day. The cause wasn't carelessness — it was tooling friction and manual rework, exactly the headwind the friction layer was built to see.
The takeaway: the error was fixable in minutes — the tool made it a day. A systemic tax on every booking, invisible to a normal dashboard. BModelr now measures it.
Once the model is calibrated, BModelr sends each driver-owner one short check-in a day — answered in plain English, in the chat they already live in. Structured signal, near-zero effort.
driver-owners on a daily cadence
structured signals captured
projected check-in response rate
per person, per day
Every signal attributed to a named owner and a specific driver — so the weekly brief writes itself.
It picks the highest-cost driver, proposes a specific countermeasure, names an owner, and gets the CEO's go — all in a few lines of chat.
Every touch is framed around the one active experiment — light, specific, and pulled to the right person at the right moment.
The recap focuses the standup; the recorded standup, dropped back into Teams, sharpens the model. Each loop is tighter than the last.
Brief generation and transcript ingestion are live capabilities today — this is the recommended cadence to run them in.