Choreography: the tailor-made proposal in minutes
The proposal that used to take half a day to assemble becomes minutes: you pass along what you learned about the account, AI builds it on your template that closes, and you decide the offer and check it before sending.
You just had a good discovery meeting. The account has a clear pain, the budget exists, the decision-maker is engaged. Then reality hits: now comes building the proposal. Opening the template, finding a similar case, adjusting the language to their problem, pasting in the proof. Half a day of work. And while you build it, the lead cools off. This lesson's choreography shrinks that half day to minutes, without you giving up anything that matters.
You finished discovery with a client who wants to cut the cost of capital and gain cash-flow predictability. You have it all in your head. Now you need to turn it into a proposal with your own rigor: conservative numbers, a source always cited, comparable cases. Before, half a morning of assembly. This lesson's choreography gets AI to build the draft in your pattern in minutes, and leaves for you what's yours: the offer, the price, and checking the number.
You just left a meeting with a client who has a specific contractual risk. You need to turn that into a fee proposal with the firm's own face: conservative tone, clear scope, analogous cases with no promise of outcome. Before, building this by hand took an afternoon. This lesson's choreography gets AI to build the draft on your template in minutes, and leaves for you deciding the fee and checking every name and fact before sending.
You found out the account values positioning and is afraid of looking like a commodity. Now you need to turn that into a proposal that connects that pain to your solution, with the right cases and the social proof that closes. Before, building this by hand took a morning. This lesson's choreography gets AI to build it on your template in minutes, connecting their pain to your offer, and leaves for you the price and the final check.
You finished the interviews with a client who needs to build a custom hiring and onboarding program for their team. You have in your head the profile they're looking for, the company's climate, and what that manager values in a candidate. Now you need to turn it into a proposal with your HR consultancy's own face: clear stages, cases from similar companies, your way of closing. Before, building this by hand took an afternoon. This lesson's choreography gets AI to build the draft on your template in minutes, and leaves for you deciding the package and the price and checking the company's name and every data point before sending.
You just left a discovery meeting with a client who wants to structure the product area: roadmap, prioritization, and metrics they don't yet have. You understood the pain, the business's moment, and what the decision-maker values. Now you need to turn it into a consulting proposal on your pattern: clear scope, analogous cases, the way you connect their problem to your solution. Before, building this proposal took half a morning. This lesson's choreography gets AI to build the draft on your template in minutes, and leaves for you what's yours: the package, the price, and checking every name and number before sending.
You just had a good discovery meeting. The pipeline moved forward, the account has a clear pain, and the decision-maker is engaged. Then reality hits: now comes building the proposal to move the deal to the next stage. Opening the template, finding a similar case, adjusting the language to their pain, positioning the proof. Half a day of work while the lead cools off and the forecast slips. This lesson's choreography gets AI to build the draft in your pattern in minutes, and leaves for you the offer, the price, and the final check, so you can send while the account is still hot.
You finished discovery with a client who wants to improve operational efficiency: cut process waste, tighten the SLA, and gain predictability in the supply chain. You understood the bottleneck, the operation's context, and what that decision-maker values. Now you need to turn it into a proposal on your pattern: clear scope, cases from similar operations, the way you connect the problem to your solution. Before, building this by hand took an afternoon. This lesson's choreography gets AI to build the draft on your template in minutes, and leaves for you deciding the package and the price and checking every number and name before sending.
You just left a meeting with a client who has a specific risk exposure: a poorly resolved LGPD point, a fragile internal control, an audit coming up. You need to turn that into a proposal with the area's rigor: conservative tone, well-delimited scope, analogous cases with no promise of outcome. Before, building this by hand took an afternoon. This lesson's choreography gets AI to build the draft on your template in minutes, and leaves for you deciding the fee and checking every name, number, and fact before sending.
You just left a technical meeting with a client who needs a custom project: modernizing the architecture, stabilizing the deploy, stopping firefighting every incident. You understood the pain, the current stack, and what the decision-maker values. Now you need to turn it into a proposal on your pattern: clear scope, cases from similar projects, the way you connect the problem to your solution. Before, building this by hand took a morning. This lesson's choreography gets AI to build the draft on your template in minutes, and leaves for you the package, the price, and checking every name and number before sending.
You just left a discovery session with a client whose flow traps the user: confusing journey, poor usability, friction that kills conversion. You understood the pain, the product context, and what the decision-maker values. Now you need to turn it into a design proposal on your pattern: clear scope, cases from similar projects, the way you connect their problem to your solution. Before, building this proposal took a morning. This lesson's choreography gets AI to build the draft on your template in minutes, and leaves for you deciding the package and the price and checking every name and data point before sending.
You just wrapped a good discovery session with a business unit that wants to enter a new market. The pain is clear, the budget exists, the sponsor is engaged. Now comes building the business case: opening the template, finding the comparable, adjusting the scenario's assumptions, pasting in the evidence. Half a day of work. This lesson's choreography gets AI to build the business case draft in your pattern in minutes, and leaves for you what's yours: the assumptions, the final call, and checking every number before taking it to the board.
Let me guess: the part of the sale that trips you up most isn't the meeting, it's what comes after it. You leave with the account in hand, hot, and disappear for half a day building the proposal. Believe me, I've lost deals that way: the lead cooled off while I was formatting a slide. Think with me: what if assembly stopped being the bottleneck? I'm not talking about AI selling for you. I'm talking about it carrying the weight of assembly while you keep what's really yours: the offer and the sharp eye. That's what we choreograph here.
The core idea of this lesson. Turning an account's discovery into a tailor-made proposal is a four-step choreography, and each step has a clear owner. You pass along what you discovered. AI builds it on your template that closes, connecting the account's pain to your solution. You decide the offer and the price, because that's sales judgment and AI doesn't decide that. And you check the number, the name, and the fact before sending, because sending a proposal with the company's name wrong kills the sale instantly. AI speeds up the assembly and personalization. The commercial strategy remains yours. The proposal that used to take half a day becomes minutes, and you send it while the lead is still hot.
You already saw, back in lesson 2.1, that context is the lever: AI only knows what you show it. This lesson is that lever applied to the exact point where the sale stalls. The context here is what you discovered about the account. The template is the shape. The offer is yours. Let's choreograph.
01The whole choreography at once
Before breaking it down step by step, look at the complete flow. There are four moves, in order, and what changes everything is who owns each one. AI doesn't step in to replace you. It steps in the middle, in the assembly, which is where the manual labor lives.
From discovery to sending, with the owner of each step:
Notice something: of four steps, three are yours. AI takes the middle one, the assembly, which is exactly the step that used to eat half a day. It's not in charge of the sale. It's the mechanical piece of work that remains yours from start to finish.
02Step 1: you pass along what you learned about the account
The whole choreography is born here, and this step is yours because nobody but you was in the meeting. AI has no way of knowing the pain the account voiced, their sector's context, what they value and what they fear. That's lesson 2.1's context applied to sales: AI only builds a correct proposal if you show it what you saw.
Pass along three things, in plain running text, no fluff: the pain (the problem the account wants to solve), the context (size, sector, timing, who decides), and what they value (price, speed, security, status, whatever weighed in the conversation). The more specific, the more the step-2 assembly comes out tailor-made. Vague here means generic proposal down the line.
Where this trips people up: you'll be tempted to skip this part and ask "build a proposal for a logistics company". AI will build it, but it builds the generic version, because that's all you showed it. The difference between a proposal that looks handwritten for the account and one that looks like a pasted template lives entirely in what you passed along here. Five minutes describing the account is worth more than any tweak afterward.
03Step 2: AI assembles on YOUR template that closes
Here's where AI takes over. And the secret isn't letting it invent a proposal from scratch, it's handing it your own template that already closes deals. You have a pattern that works: a structure, cases you usually cite, social proof in the right order, a way of connecting problem and solution. That template is your asset. AI assembles inside it.
AI's job is to fit what you passed in step 1 into that shape: taking the account's pain and tying it to your solution, choosing the closest-matching case from your repertoire, adjusting the language for their sector, positioning the proof. It's assembly and personalization, in the pattern you already know converts. Think of it as an experienced builder working from your blueprint: the blueprint is yours, the manual labor is theirs.
What goes in, what AI does, what comes out:
Notice the caption at the bottom of the drawing: what comes out is a draft. Good, on your pattern, tailor-made, but a draft. It's still missing precisely the two things that are yours and that come in the next two steps.
04Step 3: you decide the offer and the price
This is the step AI doesn't touch, and it's deliberate. Deciding the offer (exactly what you're going to propose, in what package, under what terms) and the price is sales judgment. It depends on things that live only in your head and in your reading of the account: how much this account can afford, how much it's worth to you to close, where there's room to give, what your floor is. AI has no way of weighing that, and you don't want it to.
Step 2's draft comes with a space for the offer, and it's you who fills it in. Here your commercial strategy comes into play: anchoring high or entering lean, offering a pilot or the full package, giving a deadline or not. The economic frame, the way it matters: AI cut the cost of assembly, but the decision that moves the contract's number stays yours. Outsourcing the assembly is smart. Outsourcing the offer is handing over the wheel.
Where this trips people up: AI assembles so well that you're tempted to ask it to "suggest the price". It'll suggest something plausible, and plausible isn't the same as right for this account. Price is an owner's decision, with information only you have. Use AI to build the offer's shape if you want, but the number is yours.
05Step 4: you check the number, name, and fact before sending
Last step, and the cheapest to skip and the costliest to get wrong. Before any proposal goes out, you check three things with your own eyes: the number (price, timeline, scope match what you decided), the name (the right company, the right person, spelled correctly) and the fact (the case cited is real, the pain described is theirs). This module's rule is simple and non-negotiable: check before sending it to the customer.
Why is this non-negotiable? Because sending a proposal with the wrong company name kills the sale instantly. The decision-maker reads "we prepared this specially for [competitor company]" and that's it, you just became the careless vendor who sent a pasted template. All the previous steps' work evaporates over one unswapped field. AI is fast, but it's your check that protects the sale. Thirty seconds of reading are worth the whole deal.
Learn more: why this choreography becomes an asset, not a trick
The first time you run these four steps, it feels like you saved a morning, and you did. But the real gain shows up the tenth time. Every proposal you build with AI on your template refines the template: you notice which case converts more, which way of tying pain and solution closes, which proof carries weight. You're turning, without noticing, your closing pattern into something explicit, written, reusable. It's the same move as lesson 2.1: what used to live only in your head (the way you build a winning proposal) becomes explicit context AI carries. Later in this module, in lesson N.vnd.8, this stops being a choreography you run by hand for every account and becomes an OS, a system where these steps run on their own and you only step in for the offer and the check. Today's choreography is tomorrow's system's rehearsal.
Do it now
Take your real task, a real account you need to turn into a proposal, and run the whole four-step choreography now, once, with your AI:
- Step 1 (yours): write, in plain running text, the account's pain, its context (sector, size, who decides, timing), and what it values (price, speed, security, status). Five minutes, specific.
- Step 2 (AI): paste in your template that closes (structure, a case you usually cite, the proof) and ask AI to build the proposal within it, tying the account's pain to your solution. Out comes a draft.
- Step 3 (yours): decide the offer and the price in the draft. What you propose, in what package, for how much. This is yours, don't ask AI.
- Step 4 (yours): check the number, name, and fact with your own eyes. Right company? Right person? Real case? Only then send it.
Time yourself. Compare it to the time this used to take before. That delta is the lead you get to send while it's still hot.
Practice
1. In the tailor-made proposal choreography, which of the four steps is the only one AI takes over?
2. You ask AI to build the proposal saying only 'it's a logistics company that wants to cut costs'. The result comes out generic, feeling like a pasted template. What was missing?
3. The proposal is assembled and looks great. Before sending, why does the module's rule tell you to check name, number, and fact?
For the board
On the bottleneckthe lead cools down while you format slides. The assembly is what blocks you, not the meeting.
On the inputvague about what you found becomes a generic proposal on the way out. The AI builds with what it received.
On the checkthe decision-maker reads a competitor's name and you became the careless one. The check is cheap and protects the whole deal.
Thanks for the feedback. It helps sharpen the next lesson.