Software companies and large service agencies pay well for algorithms that keep their brand from embarrassing itself and losing clients to nagging.
Every AI agent on the market has the same defect. It doesn’t know when to stop. It insists. It repeats corporate lines nobody believes. It pushes at a client who has already turned away, and the brand that hired it pays twice: first the sale, then the reputation.
Restraint is not a nicety bolted onto a sales AI. It is infrastructure. And it is the one thing no vendor ships, because an agent that backs off makes the engagement number on their dashboard smaller.
Augusta is the one that was built to back off. It conducts a four-figure sale on WhatsApp end to end, pain, value, thirteen families of objection, proposal, payment, and then stops, in code, at the moment stopping is the sale.
Exhibit A · the same request, three conversations
Two real WhatsApp exchanges from Brazil, 2026, from two shops with no connection to each other. Beside each, Augusta receiving the same request. Then a whole sale, move by move. And at the end, the thesis: the same Augusta handling two clients inside one hour, once direct, once not.
Read the two shops against each other. The shop changes. The script does not. Both customers left. Both posted the screenshots. One of the posts drew a hundred comments, the top one asking: “so this is the humanised service?”
Comparison 1 · a phone shop
He asked for the price twice. On the second, she stopped asking and went to get the data: the city, the floor area, 120 m², about 1,300 sq ft, and a name. The shop on the left was asked four times and never stopped.
Comparison 2 · a dress shop
“If price is really the only factor that matters to you, I won’t be able to help.” Then the same closing move in both: what’s good was never cheap.
That is not an agent exercising judgment badly. It is an agent with no way to exercise judgment at all. It is a script, running, and a script cannot notice that it has stopped working.
A value speech delivered to someone who didn’t ask for one is not persuasion. It’s obstruction.
Augusta’s doctrine opens with the opposite instruction, and it is where the methodology begins rather than a footnote in it: reason, never script. If it catches itself reaching for a template, the doctrine says it is already wrong.
Translated from the Portuguese. None of this reached the shop. It never does. The shop’s dashboard recorded an engaged conversation with fourteen messages and a customer who simply didn’t convert.
“I’d rather you had insulted me and just told me the price.”
Her words, softened for print, which tells you something about the original. That sentence is what sits underneath every opt-out statistic on this page, and it is the reason a brand never gets told why it lost the sale.
Augusta doesn’t sell phones or dresses. It sells four-figure consulting, where the price genuinely is calculated per case, the hardest version of this problem, because there is a real reason not to answer instantly. It answers anyway, and then it stops asking. What follows is what it does when the client didn’t only want the price.
The full demonstration · 12 September 2026
The owner’s second test of the same day, translated from the Portuguese. On the left, the conversation as the client saw it. On the right, the move count.
Seven touches, seven different angles, and not one of them says “just checking in” or “last chance”. The market’s agent in the eight-day table below has four messages and no angle at all.
The thesis
It knows when to be direct, and it knows when not to be. The two conversations below are from the same day and the same system, nine minutes apart between the first message of one and the first message of the other. The opening is the same question. What happens next has nothing in common.
When to be direct
She explains that the figure depends on the floor area and the city, and asks one pain question.
He repeated it. She dropped the question and went straight to what was missing:
No second attempt at building value. No speech about the method. She heard “just the price” and stopped pressing.
When not to be
Same opening, different reply. She names what the insomnia costs, explains what a reading of the bedroom can find, brings in a documented case and asks how long it has been going on.
From there: proposal, six objections from six different families, seven follow-up touches with seven angles.
What separates the two conversations is not a rule in the code. It is the hardest thing to build into a sales AI: knowing when to stop.
The proof
One reply looks like a small difference. The difference is what happens over the following week, and to the brand that hired the agent.
The empty column is what zero pestering looks like.No path through this system produces the next message.
Every AI agent on the market is measured by engagement, because engagement is the number that appears on the vendor’s dashboard. An agent that backs off makes that number smaller. No vendor optimises against its own metric.
Augusta was built by someone selling her own services, who paid for the insistence out of her own pocket. That is the only condition under which anyone builds this.
Why it exists
Not an opinion. The category’s own record, a tribunal ruling, an incident database, and Gartner’s own newsroom. Every figure below was read from the primary source before it was printed.
A tribunal made Air Canada honour a bereavement-fare policy its chatbot had invented, and wrote that it made no difference whether the information came from a static page or a chatbot. A Chevrolet dealership’s bot was talked into agreeing to sell a Tahoe, a $76,000 vehicle, for one dollar, “no takesies backsies”. Cursor’s support bot invented a login policy that never existed, signed its replies as a person, and customers cancelled over it.
Moffatt v. Air Canada, 2024 BCCRT 149 · AI Incident Database #622 · The Register, April 2025Message frequency is the number one reason consumers opt out of business messaging, cited by 40%, ahead of irrelevant content, ahead of bad timing. And WhatsApp itself caps how many marketing messages one person receives, with the cap tightening automatically as that person stops reading.
EZ Texting, 2026 Consumer Texting Behavior Report, n=959, US consumers, SMS · Meta, WhatsApp Business per-user marketing limits64% of customers say they would prefer companies did not use AI in customer service at all. And 53% would consider switching to a competitor if a company used AI for customer service. Not annoyed. Gone.
Gartner, July 2024, n=5,728 customersZoomInfo piloted the AI sales agent built by 11x, one of the most heavily funded in the category, backed by a16z and Benchmark, and said so publicly: it “performed significantly worse than our SDR employees.” And two years on, Gartner finds customers are three times more likely to use a third-party AI assistant than the company’s own chatbot. In Gartner’s words, the GenAI boom “has not translated into growth in the use of company-provided customer service chatbots.”
TechCrunch, 24 March 2025 · Gartner, July 2026, n=3,566 B2B and B2C customersOnly 24% of service leaders report positive financial returns from their AI investments. Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027: costs, unclear business value, inadequate risk controls. And of the thousands of vendors claiming agentic capability, Gartner counts roughly 130 with the real thing.
Gartner, July 2026 · Gartner, 25 June 2025That advice is rational, for every bot built like a support-ticket machine. Augusta was built by the owner of a premium consultancy, for her own revenue, because nothing on the market could be trusted with a four-figure sale. That is the origin story, and it is also the entire point.
You are about to compare this price to an engineering estimate. Compare it to the other number instead. Last year, how many accounts did you lose, or fail to win, because someone’s client said your bot was annoying? Take your average contract value. Multiply by the years that client would have stayed. Then note that a majority of those clients never told you why.
Your dashboard calls it engagement. Your churn report calls it something else.
From the founder
I have worked in services and in sales for many years. When AI arrived, I tested the platforms on the market and kept none of them. I found them limited, dumb, with nothing human about them. What customer-facing AI is missing most is a human being serving another human being. People serving people.
Their worst habit is insistence. They push value at a client who is visibly irritated by the pushing, and that is how you lose a sale that was already yours.
So I built, from zero, the AI I couldn’t buy. One that finds the client’s pain, connects with it, validates it, builds value, and shows point by point that your work is the solution. With real sales technique inside, and the good sense to know when to stop. If the client only wants the price, the conversation changes shape.
That good sense is the product.
Diana Féo · founder, in her own words, translated
Augusta ran her business before it was ever for sale.
The obvious question
Half of it is. That half is the half nobody can copy.
Writing “don’t be pushy” into a system prompt takes an afternoon. Knowing which thirteen situations call for which reaction, the objection under the objection, the client who only wants a number, the polite refusal that is really a budget, took months of conducting four-figure sales, and losing some of them, to find where the line was.
Follow-up is a counter, not an intention. Finite by design, and the last touch closes the lead. There is no path through this system that produces the touch after the last one. Discount pressure and sensitive ground work the same way. The model recognises them; the system pauses the conversation and calls the owner.
The judgment is the model’s. The consequence is architecture.
A sentiment score tells you a message was negative. It does not tell you that “I’ll think about it” from a firm with three partners means something different from the same four words typed by a founder who has already decided and is being kind about it.
One of those is a decision still in motion. The other is a door closing gently, and an agent that pushes on it earns the only reply that matters: silence, and a brand the client now associates with being pestered.
That distinction is not a classification problem. It is what the doctrine is for.
Enforcement
The owner’s approval isn’t a request made to the model. It’s a gate in the code.
Price, proposal, PDF and payment link do not reach a client until the owner releases them. That is not an instruction asking the model to wait, which a model can disobey on a bad day. It is guaranteed before the AI enters the story.
Once the owner approves, only the exact figures calculated for that specific proposal can be said. An invented number never reaches the client.
And when a reply cannot pass, the client receives nothing and the owner is told. The system goes silent rather than guessing.
A tribunal made Air Canada honour a bereavement-fare policy its chatbot had invented, and wrote that it made no difference whether the information came from a static page or a chatbot. This is the architecture that makes that class of accident structurally uninteresting.
Moffatt v. Air Canada, 2024 BCCRT 149The validator
Not schema validation. Brand policy, applied by machine, over every reply the model generates. A reply carrying a price the owner has not released never leaves. That is one of the sixty-three.
What fails, doesn’t send.
The rules themselves are read under NDA, in the supervised walkthrough.
The arc
Not a funnel diagram. A consultative sale conducted end to end, from the first message to the payment: it listens before it asks, it deepens the pain, it builds value, it brings a real case in, it handles the objection the client stated and the one they meant, and it follows up a finite number of times. The proposal is drafted and held, and no number reaches the client until the owner releases it.
Each step has rules that hold it in place. You watch the steps in the live demo. The rules transfer on completion.
The client who only wants the price has a path of their own, and it is not the loop of a bot that insists until the client gives up. What the phone shop in Exhibit A does for an hour and fifty-one minutes, this system is structurally unable to do.
For high-ticket deals it offers a video call, cross-checks the client’s availability against the owner’s calendar, and books it. The Google Calendar event with the Zoom link is created by the AI itself.
After payment it runs the intake: the questionnaire, the floor plan, the videos. Without ever nagging for the material, and without ever claiming to have seen a file that didn’t arrive.
Feeding all of it: a pain taxonomy, for consumers and for businesses, that decides which arguments load into which conversation.
Take two entries. A child who won’t sleep, and an adult who can’t. One problem to a marketer and two problems to anyone who has sold to both: different argument, different case, different emotional register. That granularity is the transferable asset. The labels belong to one consulting vertical and stay behind; the structure that holds them, and the engine that routes on it, is what you are buying.
All of it in human language, with a limit, and with judgment. The last one is what the market doesn’t ship.
Under the hood
This runs on every single message. Most of the steps exist to stop something.
The client’s fragments are read as one thought, and the answer arrives the way a person writes: short messages, in human rhythm, never in the same second. Before anything is sent the reply is checked, and one that fails is rewritten before the client ever sees it. A reply too close to what it has already said doesn’t go out. Nobody gets sold to at 3am.
And if it fails: silence. The client receives nothing and the system reprocesses. There is no canned fallback reply anywhere in the codebase. It isn’t that it avoids stock phrases. There are no stock phrases to fall back to.
The order of the steps, and what each one checks, is in the code walkthrough, under NDA.
It does not offer a proposal to a client who hasn’t been listened to yet, and it does not create one before it holds what pricing needs.
One of those is enforced in code. The other the model obeys because it was taught to, not because a counter forces it. We could have called both of them gates and nobody reading a sales page would have checked. The distinction is the product, so we draw it: judgment where judgment belongs, enforcement where enforcement belongs, and no dressing one as the other.
Either way it is the same discipline pointing the other direction. It will not chase a client who has gone, and it will not pitch one who hasn’t been listened to yet.
And it never opens with an intake form. What pricing needs is asked once.
There are situations in which the AI writes nothing at all. Not a deflection, not a holding phrase, not “let me check on that for you.” Silence, the conversation paused, and the owner told.
And there are situations in which it ends the conversation without being silent. A client who says plainly they can’t afford the work gets a proper goodbye, written for them, with the door left open. The cadence stops, the lead closes, the owner decides.
Which situations fall on which side is doctrine, and it transfers on completion.
The same honesty runs through the small things: it never claims to have seen a photo, a floor plan or a video that didn’t actually arrive. And a card payment is confirmed by the processor itself, matched to the exact proposal, with no model involved. The state change that matters most doesn’t depend on a generation going well.
The method
This is the part that did not come from engineering, and it is larger than every feature on this page put together.
A training in persuasion of its own, which reasons through each case instead of reciting a script. Principles of conduct that forbid the memorised line. Objection handling with its own reasoning for each kind of objection, including the ones the client never says out loud. And the lead’s own stated pain always takes priority over the one the system would prefer to talk about.
It knows the difference between the objection the client stated and the one they meant.
There is a written ethical constraint on persuasion, inside a sales AI, put there by the person whose own clients were on the receiving end of it. The system applies it to its own arguments before it uses one. Ask the vendor of any other agent to show you theirs.
This page names what the structure is. What is inside it transfers on completion, which tells you how literal the asset is.
What’s inside
Everything below is in the codebase. This is the breadth of what you would have to build if you decided to build it instead of buying it. The function-by-function inventory, and the code itself, come under NDA.
Most sales engines count clicks. This one knows who clicked. A suspicious visitor is blocked in the advertiser’s own account, automatically, and the ledger of every block, its reason and its expiry, lives on an admin screen with an unblock button.
The safety net is the part that took a real incident to build. A genuine client was blocked once, minutes before he filled in the contact form. Your real customers are never blocked.
The first click is always paid for. What the system prevents is the next one.
Full function-by-function inventory, and the code itself, under NDA.
Not a prototype
Read those numbers as what they are. Thirty times, this system was put in front of an actual human being with actual money in an actual buying decision, and carried the conversation to the point where a number was on the table. That is the test no specification and no benchmark performs.
Across all of them, it has never sent a price, a discount or a payment link without the owner’s approval. Figures read from the production data store on 31 August 2026, excluding test records.
The test suite guards the layer that enforces, the layer your team touches first in a refactor. It tells you the moment you break the gate. Dozens of the tests are named after specific real conversations, because that is where the rule came from.
The buyer
You have distribution and infrastructure. You are missing the consultative sales layer premium clients trust. And a compliance story your legal team will actually like.
You serve high-ticket niches: clinics, law firms, studios, consultancies. Your clients don’t need a ticket bot. They need a closer with manners, and you need to stop hearing that your bot annoyed someone’s client.
Anywhere one sale lost to a dumb reply costs more than the whole engine.
The deal
| Included | Complete source (Node.js / TypeScript / Express), admin panel, commercial CRM, pricing engine, proposal PDF generation, the full commercial methodology and objection library, the 5,128-test suite, technical documentation, deployment guide, and 30 days of handover support: asynchronous questions answered within two business days, plus two live walkthrough sessions of up to ninety minutes. Scoped to the system as delivered: deployment, configuration and how the code works. New features, adaptation to your vertical and debugging of your own modifications are quoted separately, at a day rate. |
|---|---|
| Not included | The founder’s own field knowledge base (her consulting vertical’s content, client cases and testimonials) and all client data, conversations and credentials. The methodology, the thirteen objection families, the validator rules and every structure that holds domain content transfer complete. Only the vertical-specific content stays behind, and it wouldn’t serve your market anyway. |
| License | Sold once, to one buyer. The Asset is never sold or licensed to anyone else, now or in the future. Seller retains a perpetual license to run her own single install inside her own consultancy, in her own niche and language, which she can never sell, license, or transfer. |
| Process | Twenty-minute live demo, screen-shared, you playing the hardest client you know → NDA, full function inventory and supervised code walkthrough → offer → escrow → handover, with the 30 days of support starting from the day of transfer. |
Buyer questions
Node.js + TypeScript + Express, around 73,000 lines. Anthropic’s Claude for generation, model configurable by environment variable, with a deterministic validator on top. Runs on a single small cloud instance. No exotic dependencies.
The built-in channel speaks the WhatsApp Web protocol, with no per-message fees. The asset also ships with a driver for Meta’s official Cloud API. Moving to it only needs to be set up in your company’s Meta account.
It stops the repeat, and assembles the evidence to claim the first click back. Your real customers are never blocked.
Generation is isolated behind one service boundary with a structured contract, and the commercial guarantees live outside the model on purpose. Swapping providers is a bounded refactor.
Measured on the production system, 1 September 2026. A message costs about US$ 0.15 once a conversation is running, and ten messages come to roughly US$ 2. At twenty conversations per closed sale of a US$ 2,900 package, the model bill for that sale is about US$ 44. One and a half per cent.
Augusta was built to sell the founder’s own services, not subscriptions. You are buying an engine that ran, an enforcement architecture, and a commercial methodology, priced as an asset, not as a multiple of a revenue line that doesn’t exist.
The live demo. You bring your hardest client persona; it conducts. Under NDA you get the full function inventory and a supervised walkthrough of the code, the validator rules and the objection library. The methodology transfers on completion. This page names its shape; the text itself is the asset, which is why it stays behind the NDA.
One install per business today. Multi-tenant is a known refactor.
Portuguese and English. The asset ships with both: a complete configuration in each language, same architecture, same gate, same validator, same test suite. You are not inheriting the localisation work.
A premium consultant who needed it to exist, in daily production since June. Solo-built, clean IP chain, no contractors, and documented for handover.
Augusta can’t. See it conduct a sale, live, in twenty minutes.
What the twenty minutes actually are. A screen-share. A real WhatsApp number. And you, typing as the most difficult client you have ever had to handle: the one who only wants a price, the one who says they’ll think about it, the one who pushes for a discount.
You watch it deepen instead of pitch. You watch it back off when you go cold. And you watch the gate refuse to let a number reach you until the owner releases it.
No files change hands, no code is shown, nothing is sent. That comes after an NDA. The demo exists to prove the methodology without giving the methodology away, which is the same problem you would have selling it on.
Asking US$ 50,000 · hello@augustasales.com
Full function inventory, validator rules, objection library and supervised code walkthrough under NDA. Write and we’ll find a time this week.