A custom-trained AI teammate for bid collection — so your estimators can bid 2x more work with the same headcount.
A GC they’ve never heard of. 500 pages of drawings. Twenty competitors on the trade, ten days to price it. Invites got free to send — so they stopped meaning anything.
Thirty minutes a sub, twenty-five trades, seven bidders a trade — the average across the precon teams we’ve sat with. All before anyone reads a drawing.
The platform assumes subs log in. Most don’t — bids, questions, and scope sheets come back by plain email, and the workflow falls apart right there.
Your personal intern, plugged into all of your systems — she preps personalized outreach until every trade is covered, then files and starts leveling the bids as they come back. You approve every move; she does the legwork.
No new platform to log into. Through APIs and MCPs, Margo syncs two-way with Outlook, SharePoint, Excel, and your bid solicitation tool — one consistent set of data, and nobody re-typing it.
Margo reads the board and hands you the move: “send this morning’s follow-ups?” One yes — she drafts every email in your voice, no two alike. One send — they queue from your own inbox and land in your sent items.
Every bid and scope sheet that comes back gets filed, statused, and lined up against the scope you defined — lump sums, exclusions, and plugs flagged — so bid day starts from a filled-in sheet, not a pile of PDFs.
Stop chasing subs.
Review drawings earlier.
Bid more work.
The loop runs itself. Your estimators do the part that needs an estimator.
The opportunity of bringing margo onto the team.
Unlocks 0 more projects a year — about $0 of revenue capacity. the whole calculationthe chase25 trades × 7 bidders × 30 min = 87.5 hrs a jobyour year87.5 hrs × 25 solicitations = 2,188 hrs on the chasemargo takes2,188 hrs × 40% = 875 hrs handed backworth875 hrs × $56/hr = $49,000 of estimator timecapacity875 hrs ÷ 87.5 hrs a job = ≈ 10 more projects bidrevenue10 × 30% win × $5M avg job = $15M of capacitytrades, bidders, minutes, rate, and win are the averages measured across the precon teams we’ve sat with.