CloudSAFE: an AI agent that turns pricing into proposals customers sign

CloudSAFE sells complex hosting and managed services through a focused sales team. Salesforce CPQ holds more than 200 products and does its job on price. What it does not do is produce the polished, narrative document a customer wants to sign, so reps were building proposals from the company's contract library by hand.

That library was a real asset. CloudSAFE had invested in 2024 in a solid set of standards: the RACI, the recurring and non-recurring service terms, the time-and-materials format, the backup and disaster-recovery language. The gap was that those standards lived in documents rather than in a system, so each rep reassembled them by hand for every deal. Proposals varied more than the team wanted, and reps were spending time on assembly that they would rather spend selling. Doug had recently taken sales under his own direction and saw a chance to make the proposal a stronger part of how CloudSAFE wins.

Strong Tide's view was that buying a tool before defining the standard would mostly speed up the inconsistency. The recommendation was to define the standard first, build an agent to run it, and treat a tool purchase as a later decision rather than the first one.

Strong Tide worked through the problem with the people closest to it: the rep closest to the customer, the solutions lead who had written the requirements, and the sales-ops lead who owned the systems. The raw material to define the standard was already on the table in their own notes. The advice to Doug was straightforward: hold off on the tool evaluation, lock the proposal content standard, then let an agent do the assembly the reps had been doing by hand.

An AI agent that takes what CloudSAFE already has, the CPQ pricing, the deal's meeting notes, and the company's own contract library, and produces a finished proposal. It works the way a good sales engineer would, in two stages:

Stage one, the briefing document. A clean narrative proposal the customer reviews and signs off on, built from the deal's meeting notes and the standard service language, with pricing pulled from CPQ.

Stage two, the statement of work. On approval, the agent drops that pricing into a locked, on-brand template, with the right terms and the standard RACI attached.

The judgment is built into the agent, not left to each rep. Pricing comes only from the CPQ quote, terms come only from the approved contract library, and the agent flags anything missing rather than filling it in on its own. It was tested end to end on real CloudSAFE deals across both of the company's main hosting lines, producing the proposal, the statement of work, and a change log that shows exactly what changed and why.

The design principle: every system keeps its job. The agent orchestrates; it does not become a new source of truth.

• Salesforce CPQ is the only source of pricing, SKUs, quantities, and licensed products.
• SharePoint is the only source of contract terms and SOW templates, read live through the Microsoft 365 connector, so a template edit propagates to every rep's next run without a rebuild or a redeploy.
• Teams and rep notes supply deal context and narrative. Meeting recordings and transcripts are optional inputs.
• Claude (Anthropic) handles orchestration and assembly, running as two Cowork skills on Microsoft 365 Copilot, shipped as one plugin.

The guardrails are hard constraints, proven on real deals. Pricing and line items come only from the CPQ quote. Missing data is flagged, never filled in: a quote with no quote number refuses to render. No invented totals, and no line-item math the quote does not state. Every run ships a change log showing exactly how the paper differs from the template and the quote, with anything needing a decision highlighted for review.

The agent was validated against a baseline on three real deals: a full IBM Power hosting proposal, an x86 capacity add, and an IBM i storage-add SOW, each run with the skills and without. With the skills, every check passed, the correct template was chosen by platform, and each document took minutes. Without them, one run invented a contract total, two produced no editable deck, and rebuilding a template by hand took about an hour per document.

Deployment is a model IT already knows. The skills ship as a Microsoft 365 app package, the same mechanism as Teams apps, deployed through the M365 Admin Center and governed like any other app in the tenant. The agent runs under Entra identity, so SharePoint permissions, Purview DLP, and sensitivity labels apply exactly as they do everywhere else. Updates are a version bump and a re-upload. Uninstall is removing the app.

The numbers: proposal turnaround dropped from about a week of two-person manual work to minutes on validated runs, at roughly $5,800 in estimated manual labor per proposal cycle against about $6 in Claude consumption for the same proposal and SOW package, a roughly 960x lower cost per cycle. The team went from demo to governed production in two weeks.

CloudSAFE walked into its national sales meeting with a working agent rather than a plan to buy one. The team held off on a software purchase until the standard was defined, and now has a clear picture of what a finished CloudSAFE proposal should be, which is the thing a tool decision should be measured against.

For a partner-driven business where the proposal is a sales tool and not just paperwork, the business case is direct. Proposals that match the quality of the work behind them. Fewer revision cycles before signature. Assembly hours that used to eat into selling time now back with the reps. And a standard the company can carry into the next acquisition rather than rebuilding it each time.