Utilization and pipeline in one warehouse
for consulting firms, answered in Slack, not a spreadsheet
A consulting firm usually has time tracking in one tool, billing in another, and pipeline in a CRM, so nobody can say which practice area is actually profitable without a week of manual joining. We build the warehouse that joins them and an analyst who answers in minutes.
Why management consulting firms lose money today
A consulting firm’s profitability question, which practice area, which engagement type, which partner’s book actually makes money, sounds simple but usually requires manually joining time tracking, billing and pipeline data that live in three separate systems, each chosen for a different job. By the time someone finishes that join for a quarterly review, the data is already a quarter old.
A second cost is utilization blindness at the individual level. A firm can look fully booked in total revenue while several senior consultants are quietly underutilized, spending hours on proposal writing and internal work that never gets flagged until a review finds the gap months later.
A third is pipeline visibility disconnected from actual profitability. A practice area with a strong close rate is not automatically the most profitable one if its typical engagement runs at lower margin, and without a dashboard joining pipeline data to realized profitability, a firm can keep investing business development effort into the wrong practice area.
What we build
A warehouse joining your time-tracking tool, billing system and CRM, so utilization, profitability and pipeline all answer from the same consistent data. The utilization dashboard breaks out billable versus non-billable time by practice area and by consultant, surfacing underutilization before it shows up as a quarter-end surprise.
Engagement profitability joins actual hours logged and billed against revenue, by practice area and engagement type, so you can see which segment of the business actually produces margin, not just top-line revenue. Pipeline dashboards track close rate and average deal size by practice area, joined against the same profitability data so business development effort can be directed toward what is actually worth winning.
An AI analyst answers business questions directly in Slack or Telegram with real SQL against the warehouse, so a partner asking “what was our utilization on the operations practice last quarter” gets an answer in minutes, not after someone manually reconstructs it.
What stays with humans
The actual engagement delivery, client relationships and business development strategy stay entirely with your partners and consultants. We build the warehouse and the dashboards that make the numbers visible; we do not decide where to invest business development effort, we just make that decision informed.
Price and timeline
| Package | Price | Timeline |
|---|---|---|
| Agency runs it | from $2,500 | 3 to 6 weeks |
| Full control, handover-ready | from $4,250 | 4 to 7 weeks |
Related
Pair this with the AI agent for management consulting firms so qualified leads flow straight into a pipeline you can actually report on, or Google Ads for management consulting firms to see which campaign actually produces signed, profitable engagements. See the full package breakdown on the analytics service page, or get a written plan with a fixed price for your firm.
FAQ
How much does an analytics warehouse for a consulting firm cost?
A tracking audit starts from $800 if you want to see the gaps first. A full warehouse with utilization, profitability and pipeline dashboards starts from $2,500, and adding the AI analyst is included from that tier.
How long until we have working dashboards?
3 to 6 weeks for the warehouse and the core dashboards, depending on how many time-tracking, billing and CRM fields need to be joined.
Which tools do you connect?
Harvest, Toggl or a similar time-tracking tool, your billing or invoicing system, and HubSpot, Pipedrive or a CRM for pipeline data.
Can it show which practice area is actually most profitable?
Yes, that is the core of the dashboard: engagement profitability joined against actual hours logged and billed, by practice area and engagement type, rather than a top-line revenue number that hides where the margin actually comes from.
Can the AI analyst get our numbers wrong?
It runs read-only SQL against a prepared data mart, one query at a time, with forced limits, and shows the query it ran. We built and audited this exact guard before trusting it on live data, closing eight bypasses in one internal review.