A private equity-backed vertical SaaS company ($100–150M revenue, ~40% EBITDA margin) had named AI as a core lever in its value creation plan, and the board expected AI to contribute meaningfully to margin improvement in the current fiscal year.
Leadership believed the heavy lifting was already done: developer tooling was rolled out, the CTO was engaged, a chatbot was live, and there was real enthusiasm. ' But none of the AI work was connected to the P&L. There were explicit cost-takeout targets in the value creation plan and a finite PE hold clock, yet no financial model behind the AI program and no operator accountable for capturing value.
AMP diagnosed the client against its P&L, then ran its first pilot on the client's existing Azure/Microsoft environment (Microsoft 365, Power Automate, SharePoint) with a model-agnostic agent layer, currently Azure OpenAI (GPT-4.1), routing to Claude via Amazon Bedrock where it outperforms. The pilot automated professional-services documentation drafting, BRDs, SOWs, requirements analyses, meeting summaries, pulling from call transcripts, discovery notes, and the historical document library to produce review-ready first drafts with citations and house style.
Realized and measured on live documents: senior-consultant drafting of BRDs, SOWs, and requirements analyses fell from 1–2 weeks to about 2 hours per engagement.
Underwritten over three years from this one workflow, plus a projected 5–6% Year-1 EBITDA margin improvement, modeled and underwritten, not yet banked.
The drafting workflow runs in daily production today; SOWs are the next workflow, with the rest of the professional-services suite behind them.
- How a PE Roll-Up Surfaced 40% of Redundant WorkMarble · Financial Services
- How Reynolds Lifted Its Innovation Pipeline 40% in 8 WeeksStarday Foods · Consumer Goods & CPG
- How a $20M Services Firm Unlocked $500KSY Partners · Professional Services
- How One Automaker Cut Venture Discovery to 2 HoursFifthRow · Automotive