AllyWise and ChatGPT don't do the same job.
This is the comparison worth making, because it is the real one: most salespeople preparing for a first meeting today paste a LinkedIn profile into a chat and ask for an opening line. It is worth saying precisely how far that gets you, and where it stops.
What a general-purpose model does well
Starting from a caricature would help nobody, and the person reading this has already tried the alternative.
A general-purpose model is excellent at summarising a page, rewriting an email, shifting a tone, producing ten subject lines in twenty seconds. On those tasks it is fast, effectively free, and needs nobody to teach it anything.
The point is not the model's power: AllyWise itself runs on general-purpose models underneath. The point is that between a model and a repeatable sales decision, three things are missing — a unit of analysis, a method and a perimeter — and those are exactly the three things a prompt rewritten by hand each time cannot supply.
The differences you feel by the third meeting
Not on the first try. On the first try a good prompt looks sufficient: it is when the team is five people and the meetings are forty a month that the differences become operational.
| Dimension | General chat, used by hand | AllyWise |
|---|---|---|
| Unit of analysis | Whatever ends up in the prompt: usually the buyer alone. The seller is not in the frame. | The pair. Your persistent profile crossed with your buyer's ephemeral one. The same customer produces different prescriptions for two different salespeople. |
| Method | Whatever you know to ask for. It varies from person to person, and from yesterday's prompt to today's. | TANDEM: four archetypes and three Regulators anchored in the Big Five, declared and readable. The same one across the whole team. |
| Shape of the output | Prose. Pleasant to read, hard to compare from one meeting to the next. | A document in nine sections, always the same ones: pair, openings, queen proof, language, format, topics, timing, signals, sources. |
| Uncertainty | The answer sounds the same whether the evidence is solid or thin. Nothing marks the difference. | Every element carries its source and a confidence level; dimensions where the public evidence is poor are flagged as such rather than filled in. |
| Your own profile | Has to be re-explained every time, or never enters the request at all. | Filled in once, in five minutes, then it is the starting point of every brief. |
| After the meeting | The conversation closes. Nothing comes back. | You say in a few seconds whether the brief anticipated what you found, and the next reading sharpens. |
| Data perimeter | You decide it, prompt by prompt: what to paste, what not to, where it ends up. With real people's data, in Europe, that is a decision falling on the company every single time. | A product constraint: special categories filtered across four layers, an ephemeral buyer profile, a written balancing test, no emotion recognition. |
When ChatGPT is enough, and AllyWise is a pointless cost
Set out as plainly as the rest, because selling you a tool that moves nothing in your context is a loss for both of us.
- You only need to rewrite an email you have already decided in substance: a general-purpose model does that better and immediately.
- You sell transactionally, with a short cycle and a single decision-maker: there is no relationship to read.
- You prepare alone and have nobody to align: the value of a shared method grows with the number of people, and at one person it is close to zero.
- Your problem is finding the contacts, not talking to them: that needs a database, not a brief.
The difference your legal team notices first
Pasting a real person's profile into a general-purpose chat is processing of personal data, carried out by an employee, for a purpose decided on the spot, with no documented legal basis. It is not automatically unlawful, but the burden of showing it is lawful falls on the company — and nobody inside the company knows how many times it has happened.
AllyWise does not remove that burden: it moves it onto a declared and documentable perimeter. The buyer's profile is ephemeral, the special categories under Art. 9 GDPR are excluded across four layers of control, the balancing test for legitimate interest is written down, and there are no emotion-recognition features, which Art. 5 of the AI Act prohibits in the workplace. That is the difference between a widespread practice and a defensible one.
The right way to decide is to compare two outputs.
Take the sample brief, take the prompt you use today, and look at what each one is missing. It is the comparison we run in discovery calls too.