SAMPLE: Locust Walk × BI Consulting Services

Transactions closed since 2008

80+

5 offices · 3 countries

Published figure — locustwalk.com¹

Active mandates

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Pipeline value (aggregate deal size)

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Median months to close

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Win rate (closed ÷ decided)

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Marketing-sourced mandates (in flight)

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Signature · Data architecture

Four lines, one interchange: how the sources reach this board

Drawn as a Boston transit map. Every line is one of your systems; every station a table the model reads; the interchange is the shared semantic model this board (and every future board) sits on. Station counts recalculate with the filters.

CRM line Marketing-platform line ERP line Other-sources line (deal databases, spreadsheets, calendar)

Pipeline

Mandate funnel

Every mandate in flight during the period and how far it has travelled. Stages are cumulative — a closed mandate counts at every stage.

Trend · 24 months

Mandates won per month, Sep 2024 – Aug 2026

Bars are mandates closed; the line is the trailing 3-month average. Months outside the selected period are dimmed.

Ranking

Therapeutic area × deal type

Mandates closed in the period, split by deal type, with the aggregate deal size and median months to close.

LicensingM&AFinancing

Marketing → BD attribution

Where mandates came from

First-touch source of every mandate in flight during the period, joined from the marketing platform to the CRM deal record — with the share that closed in the period.

What this board answers

Three questions a BD lead asks every Monday

Each answer below is read straight off the board for the current filter.

Ask the data · mock

A question typed in plain English

What a natural-language layer over the same semantic model would return. The answer is computed from the board's figures for the current filter.

Source: semantic model (Fact Mandate, Fact Touch, Dim Therapeutic Area). Illustrative — no live model is attached to this page.

Private AI on your own server

The "ask the data" panel above is built to run on a GPU server inside your own network: an open-weight model over your own documents, with every answer citing the file it came from. No per-seat licence, works offline, and nothing leaves the building.

How it sits in your environment

One appliance-class server on your rack or in a secured closet. Your files are indexed where they already live; the model runs locally; answers come back with the source page attached.

What that means in practice

1
Your data only, on your hardwareAn open model over your own documents and exports. No third-party AI processor, no data-transfer agreement to review.
2
Every answer cites its sourceReplies point to the file and page they were drawn from, so people check the answer instead of trusting it blind.
3
No per-seat licenceThe whole team can ask questions. Cost is the server and our build, not a monthly number that grows with headcount.
4
Works offlineIf the internet connection goes down, the answers still come. Nothing depends on a vendor's uptime or pricing changes.