Sally is one system that runs a company's entire go-to-market — finds the buyers, runs the ads, writes the outreach, takes the live sales calls, follows up, and closes. She is in production today. Her first customer is us: the sales call on our own site is Sally, selling Sally.
Not our pipeline — a customer's. They sell resident retention to property operators. Their prospect finally got a quiet minute, opened his email, and tapped a link. No calendar dance. He was in a live, spoken sales call in under a second.
11:31 PM — a committed next step, every objection captured on the deal, nobody on their team awake.
These run 24/7, in parallel, in any language.
For the last thirty years, companies have been told software would transform sales and marketing. In one sense it did. There is now a tool for almost every individual task:
They don't form a system. They form a pile.
Generating revenue is often the hardest, most expensive thing a company does. Tens of thousands of dollars a month on tools designed to find customers — and the actual work of finding customers is still overwhelmingly manual.
Each tool performs one narrow function. Each contains one fragment of the truth. Each requires someone to decide what happens next. The advertising platform can tell you who clicked an ad, but not what those people later said in a sales conversation. The outbound platform can tell you who replied, but not why one message resonated and another failed. The CRM can tell you an opportunity closed, but not which idea, objection, campaign or conversation closed it.
The salesperson knows what prospects are saying. The marketer knows which campaigns get attention. The founder knows why customers ultimately buy. The knowledge lives in different people and different systems.
And every specialist in the pile optimizes for what their piece can see. The ad agency — a monthly retainer plus ten percent of ad spend, the standard arrangement for twenty-plus years — optimizes cost-per-click and click-through rate. The email operation, in-house or hired, optimizes sends, open rates, positive-reply rates. All real metrics. None of them the one that moves the needle. Nobody is optimizing for revenue, because nobody can see all the way to it.
This is what companies call a sales and marketing operation. But it isn't an operation. It's a chain of disconnected tools, held together by people — expensive, slow, losing information at every handoff. For small companies, a true repeatable system often never gets built at all.
And the human glue fails in ways everyone has learned to accept:
Not a discipline problem — a structural one. A human cannot answer every lead in under a minute, follow up six times on every thread, and run a live demo at 3am for the prospect in Lisbon.
It's Tuesday, 6:40 in the morning. A founder with an exceptional product opens her laptop — not to build. To sell.
To grow past this, she's told to hire a marketer, an SDR, a salesperson, an agency, an ops person, and contractors. Not because the work is impossible — because no single system has been able to do it.
A company tells Sally what it sells, who should buy it, and how much it's willing to spend. Sally does the work required to turn that information into customers.
She researches the market. She identifies likely buyers. She creates and launches campaigns. She writes outreach. She monitors target accounts. She responds to prospects. She runs live, Zoom-like sales calls. She answers questions. She follows up. She qualifies opportunities. She learns which messages are working, sees which prospects advance — and, most importantly, learns which prospects ultimately become customers.
And she works like a hire, not a tool: you manage her entirely by email. No dashboard to maintain, no CRM workflow to keep current. You email her instructions; she does the work; she emails you back with what happened.
There is deliberately no CRM integration, because there is no CRM — Sally is an all-in-one system and her pipeline is the system of record. The CRM is legacy software, built on the premise that humans do the selling and someone has to monitor and manage their activity. Take the humans out of the loop and what you actually want is answers: email her "give me a Q4 forecast based on historicals" or "who's likely to close in the next three weeks" and the answer comes back the way it would from a great head of sales.
You also decide exactly where her job ends. Want her to run one qualifying call and hand every deal straight to you — no follow-up, no back-and-forth after? She'll do that. But the most powerful version is the full cycle, start to finish: every call, every follow-up, every objection, all the way through the close.
Reads your market, builds the ICP, finds and verifies real buyers — dropping the defunct, acquired, wrong-industry and wrong-size before anyone is emailed.
Campaigns on Meta, LinkedIn and X. Copy, images, short video, and an on-brand landing page. Nothing spends without your say-so.
Researches each company and writes the email around what they actually do. An honesty check rejects name-swap templates, invented details, manufactured urgency.
Spoken, Zoom-like, 24/7, in parallel, in any language. Handles objections, does the math on the call, books the next step.
Tracks whole buying committees, not contact lists. Tailors the pitch by role — the VP hears pipeline coverage, the CFO hears payback.
Proposals inside pricing bands you set; discounting past the band is refused at the tool layer. She sends your own Stripe or DocuSign link.
Watches your accounts month after month. A new VP lands in a role you sell to and the introduction goes out that week, not at the next campaign.
Only a cryptographically verified manager can direct her. The rules that must never break live where no email — including yours — can erase them.
You point Sally at your website on a Monday and go back to work. Before you've done anything else, she's researched your company on the open web — what you sell, who buys it, how you position, who you compete with — drafted a starter playbook, and built you a complete, professionally designed sales deck from your own site and branding, with a real problem-to-solution-to-proof-to-close arc. Pricing is left blank; she never guesses a price. Prefer your own deck? Email her the PDF and it replaces hers.
Before any prospect ever sees a demo, she reconstructs the Offer Model behind the deck — the product, the buyer job it owns, the outcomes, the capabilities — then runs ten realistic buyer calls, a deliberate share of them hard buyers: price-hagglers, one-word operators, competitor-anchored skeptics, executives with five minutes. Every proposed correction must measurably improve a replay of the same call, hold up against a different buyer, and two untouched buyer calls must then pass full quality review. Only then do you hear from her.
It's her finished understanding of your business — "Is this what we actually sell?" — for you to confirm or correct in plain English. Then the welcome email arrives with the deck and a live demo link, and connecting her mailbox is the entire go-live.
Want to teach her more? A thirty-minute voice onboarding call, sample call transcripts, or a "pitch and I'll copy" session where she absorbs your exact phrasing, cadence, and objection moves.
Autonomy is only sellable if it's governed, so the guardrails are engineering, not policy documents. Only the verified manager can direct Sally — instruction authority is bound to a cryptographically verified email identity, so a prospect claiming to be the CEO can't steer her. The rules that must never break — no fabricated pricing, SLAs, customers or metrics; unconditional opt-out; no invented urgency — live in code, where no email (not even yours) can erase them.
A brand-new Sally earns her autonomy. At first every prospect email comes to you as a one-tap approve / edit / reject, and she starts sending on her own only once your corrections fall below a threshold. Her first hundred real calls are each critiqued immediately by a hard-nosed sales-coach review — then nightly, forever.
Every action is auditable. Nearly every action is reversible by replying to an email.
Point Sally at your ad budget and she stands up campaigns on the Meta platforms — Facebook and Instagram — plus LinkedIn and X, writes the copy from your own materials, creates the images and short videos, and spins up an on-brand landing page whose "watch it presented live" button drops a visitor straight into one of her voice calls. A form fill becomes a lead in her pipeline within moments, worked like any warm prospect: reply, demo, qualify, close. The loop runs end to end — ad → lead → conversation → call → revenue — and what the sales calls teach her feeds back into the next campaign.
Normal campaigns get a heads-up window ("launching tomorrow at 10am, $50/day for two weeks — reply to hold"). Video ads, sensitive categories, and customer-list retargeting always need an explicit yes. You set monthly and daily caps she never crosses, a deterministic rules engine — not a guess — computes every budget change from real results, the platforms hold hard native caps on top, and an emergency stop freezes everything instantly.
Two channels, never conflated. Warm contacts — the lists a founder accumulates through conferences and intros — go from your own inbox, gently paced, with unconditional opt-out. Genuinely cold prospects go through dedicated, separately-warmed sending infrastructure, so cold volume never risks your domain's reputation.
She finds prospects herself, searching lead databases against your ideal customer profile — and because database filters are approximate, her per-prospect research doubles as a verification gate: defunct, acquired, wrong-industry and wrong-size companies are quietly dropped before anyone is emailed. Before a big speculative list spends a dollar, she probes a free sample and reports the true hit-rate.
Hand her a messy pile — a spreadsheet with a do-not-sell tab, a CRM export, one sentence like "go after managers at specialty paint stores" — and she comes back with a plan first. One plain-English yes covers the whole motion, exclusions locked in before anything else, paced honestly across months rather than blasted in a night.
Every active deal carries a structured qualification scaffold — economic buyer, identified pain, champion, competition, next close date — filled in conversationally, every field citing the email or call that supplied it.
She tracks whole buying committees, not flat contact lists: when a champion writes "let me loop in our CFO," the CFO becomes a tagged contact with their own thread state. She understands and navigates the nuances between roles — head of sales to VP of sales to CFO — and tests and tailors her pitch to each audience automatically, the way a good human rep would: the VP hears pipeline coverage, the CFO hears payback.
Proposals are drafted only within pricing bands you configure; discounting past the band is refused at the tool layer and escalated to you. Sally runs no payment or signature system of her own — when she closes, she sends your own Stripe, DocuSign, or signup link, exactly as you configured it, warm threads only, every send logged as a close attempt.
And a handed-off deal is never forgotten: the month-end review means every close gets recorded, and a deal you couldn't move can flow back into her pipeline instead of dying on the vine.
She doesn't work a list once and move on. She watches the accounts you care about — and the people on them — month after month, and moves when something changes.
You teach her the triggers that matter in your market — reach out whenever an account hires a new VP — and she watches for them across every account, acts on them without being asked, and tells you what she saw and what she did in the same evening email.
Straightforward deals she closes herself, with your own payment link. Deals that need you arrive as a structured Deal Brief. On the last day of the month she asks where the handoffs landed; you answer in a sentence, she books the outcomes. You spend your time on the conversations only you can have.
She doesn't merely know an ad received a click. She knows whether the person who clicked later took a meeting, what they cared about, which objections appeared, whether the opportunity progressed — and whether it became revenue. That creates a continuous learning loop across the entire customer journey.
The campaign improves because the sales conversations improve it. The conversations improve because the campaign attracts better prospects. The entire system improves because it can see what actually turns into revenue. Today, humans perform this coordination. Sally turns that coordination into software.
Underneath sits something no ad platform or email tool has: with Stripe and QuickBooks connected — Chargebee, Recurly and others to follow — she sees the full lifecycle, from "never heard of you" through closed-won, contract signed, invoice paid. A verifiable outcome, and to our knowledge something no AI sales or marketing tool has built. So she can tell you that dentists who see one particular image and call to action click through and ultimately buy, while physical-therapy practice administrators click and reply — but stall, and exactly what they say in the sales conversations when they do. And when the pattern points at the product itself — a gap she keeps hearing about on calls — she bubbles it up to you intelligently, so you can weave it back into what you build.
Every night she runs an internal review — which lines got real traction, which fell flat, what worked and what didn't — and builds the winners into the next day's calls, ads, and outreach. Like a great salesperson replaying the day on the drive home, except she never skips a night and never forgets the lesson.
One wall never moves: outcomes teach her how to sell, never what's true. Product facts and pricing only ever change through you — and every change she makes to her own memory is cited, versioned, and reversible with one reply. When a prospect asks something she doesn't know, she has a hard rule: never invent an answer. She tells them she'll find out, asks you, follows up — and files your answer into her corpus of knowledge, so it's asked once and answered forever.
That is the loop inside one customer's account. But the loop that matters most to an investor is the one that runs across all of them.
This is the part I'd underline if you only read one section. It's why this is a company and not a good tool.
A sales conversation is the richest training signal in commerce, and it has always been thrown away. It happens once, in one room, in front of one person, and whatever was learned in it stays inside that one person's head until they leave for another job and take it with them.
Maybe a thousand a year. They keep what they remember, lose the rest, and take everything with them when they go. Nothing they learned reaches the rep two desks over except by accident.
She reviews every conversation she had — what landed, what fell flat, which objection killed the momentum — and she knows how each one ended, because she can see all the way to the invoice. Nothing is remembered selectively. Nothing is lost.
What one Sally learns about pacing a hard buyer, holding a price, reading a five-minute executive, or knowing when to stop talking doesn't stay with her customer. It crosses to every other Sally, working for every other company. The craft is shared. The learning curve is one curve, and everybody is on it.
Play that forward. Hundreds of sales conversations a day, then thousands, all feeding one system that learns from all of them. There is a point on that curve where Sally is running more sales conversations than any sales organization on earth has ever run, and learning from every single one — more deeply, more consistently, and more fluidly than any of them ever could.
Past that critical mass, using anything else to sell for you becomes the irrational choice. Not because of a feature. Because the thing on the other side of the table has had more reps than your entire industry combined.
And this is the kind of advantage that cannot be bought late. An incumbent with a hundred times our capital can ship an agent tomorrow; they cannot ship the conversations. The gap is cumulative, and it only opens in one direction. That is the road from a hundred customers to thousands — and the reason the thousandth customer is easier to win than the tenth.
The obvious question, and the one every customer asks: does my pipeline teach my competitor's Sally?
No. The separation is categorical. Craft crosses. Content never does.
What crosses is how to sell: pacing, sequencing, when to ask for the close, how to handle a competitor-anchored skeptic, which structures of reply actually get answered, how to run a call with someone who's given you five minutes. General, transferable technique — the things a great sales coach would teach anyone.
What never crosses is what you sell: your prospects, your pricing, your positioning, your deal history, your customer list, your product facts. That lives in your tenant and it stays there. The same wall that stops her inventing a price stops her carrying your book of business into somebody else's account.
It's the difference between a rep learning a better way to handle an objection and a rep taking your client list out the door. We built for the first and engineered against the second.
Creative Sally produced for OpenVia, the founder's own access-control company — the copy, the design, and the per-platform formatting, shown as they run in-feed.
An AI SDR books meetings a human still has to take, from sequences a human still has to write, into a funnel a human still has to run. It's one more tool on the pile. And the blast tools? Ten thousand name-swapped emails a day is exactly the machinery buyers have learned to delete.
Sally is not a writing tool, not a chatbot on a website, not another dashboard. She is the operating system for acquiring customers — the world's first comprehensive system for go-to-market: campaigns, outreach, conversations, live sales calls, qualification, follow-up, and the close, coordinated by one intelligence that learns from revenue.
Even her cold outreach proves the point: before any cold email goes out, she researches that prospect's company on the web and writes the whole email individually around what the company actually does — then an honesty check rejects name-swap templates, invented details, and manufactured urgency.
Our target market is exclusively B2B — a uniquely tangible sale, and that isn't changing in the near term — and non-regulated industries, so not finance, banking, or medical, where licensure requirements apply. Beyond that, the screen is simple: do you sell B2B, mostly over email and Zoom? Then Sally can likely run your motion. She's not for the enterprise, where deals are done over steak dinners and golf — she's for the transactional end of the spectrum.
Our beachhead: founder-led B2B companies with contract values of $25K and under, where the founder is still doing the selling and trying to stop being the only salesperson. It's a genuinely painful point in time — and 95% of founders aren't great at sales, our own included. They're evaluating Sally against hiring their first or second rep; she's the alternative that costs a fraction and starts immediately.
The second core audience: companies selling into the US from abroad. For them the problem is structural — time zones that put every demo at 8pm local, an availability gap, and often a language barrier. Sally erases all three: she works every timezone and speaks any language, tailoring her outreach to prospects who speak Portuguese, Spanish, French — whatever it may be — in their native tongue.
That's already many hundreds of thousands of businesses — software companies, agencies, service providers — a huge market in itself. And the Stripe playbook applies from there: start with startups and small businesses, make money as some of them become big businesses, and work steadily up-market into larger accounts you never could have started with.
The obstacle in front of us right now is not price, and it is not features. It's belief. We are at a specific and temporary moment: CEOs and founders want this to work. They can feel what it would mean for their business. They are simply not convinced it actually will — not for them, not with their product, not against their buyers.
That is not an objection you can argue someone out of. A better pitch doesn't move it. A case study about somebody else's company doesn't move it. The only thing that moves it is watching it work on their own market, with their own prospects, in front of them.
So we buy the proof. We take the loss at the front of the funnel and put real deals in a stranger's pipeline before they've paid us anything.
When a prospect shows real interest, Sally goes to their website, ingests everything publicly available about their company, and generates a complete sample deck and live sales presentation — for their product, with their positioning and their branding. Then she offers to present it.
"Hey Jim, one of the best proof points is exactly what I can do. Here is me selling your product."
So the buying moment isn't "watch Sally sell Sally." It's watching Sally sell their own product back to them, before they've signed anything. It lands 85–90% right — close enough that a founder polishes rather than rebuilds. We are not aware of a single competitor selling itself this way.
It costs us about $15 in inference, deliberately, because our first foot out the door has to be exceptional and that still takes the more expensive models. At a skeptic's 1% hit rate that's a $1,500 cost of acquisition — but she only builds them for prospects already showing interest, with budget and authority, so we expect closer to 10%, and we'll learn the true number at scale.
That buys a real conversation. It doesn't yet buy conviction, because a great pitch about your own company is still a pitch. So there's a second step.
She researches their company, builds a complete deck for their product, and presents it to them live. Costs us inference, buys us a real conversation.
Free or deeply discounted, for qualified prospects only. She works their actual market — real research, real outreach, real live calls, real ad spend — and we absorb the cost.
By then the question isn't whether it works. It's whether they're willing to switch off a working flow of new deals. Almost nobody switches that off.
The pilot has one job, and it isn't to impress. It's to produce deals — genuine prospects in genuine conversations, meetings on their calendar, opportunities they can see and touch and follow up on themselves. Theory converts nobody. A pipeline with their competitors' customers in it converts almost everybody.
And it inverts the close entirely. We never have to talk a skeptic into a $3,499 monthly commitment on the strength of a promise. We ask a founder who is currently receiving qualified meetings whether they'd like that to stop. It's the cheapest possible way to buy the only thing that's actually scarce right now, which is conviction.
The arithmetic holds. A pilot capped at a thousand dollars of real spend, converting into a Growth plan at $3,499 a month plus commission on everything she closes, pays itself back inside the first month and every month after that is margin. Even at a punishing conversion rate this is the cheapest customer acquisition in the category — and the customers it produces have already seen revenue from the product before their first invoice, which is the best churn insurance there is.
It's disciplined, not indiscriminate: pilots go to qualified prospects only — right motion, real budget, actual authority to buy — and every one is metered and capped. We are deliberately buying belief while belief is still the expensive thing. It won't be for long. In two years this market will take autonomous selling as read, and the companies with the conversations, the case studies and the compounding will already be chosen.
She is compensated the way a salesperson is: a base that covers the acquisition motion itself, plus a cut of what she actually brings in. A customer who hires her and closes nothing pays us very little. A customer she makes rich pays us handsomely, and is delighted to.
The base scales with pipeline, not seats. Commission and ad management ride on top of every tier, so what we earn moves with what the customer earns.
Every tier gets every capability. The commission is the part that matters: most of what we make only exists once the customer has made money first.
Commission attribution is deliberately fail-closed: with the customer's permission the platform reads their own Stripe or QuickBooks — read-only — and only an airtight match between a closed deal and Sally's pipeline bills automatically. Uncertain matches ask first; no match is never billed; refunds claw back pro-rata. Trust in the billing is part of the product.
The margins underneath are software margins — 80 to 90 percent gross. We built and tested on the most capable frontier models from Anthropic and OpenAI, then progressively migrated the intelligence layer to far more economical open-source models — GLM 5.2 carries most of it today — with no quality loss we can measure. Ad-creative generation still runs on leading-edge models while the quality bar is the product; as each piece bakes, it moves down the same cost curve, and fine-tuning models of our own, custom-tailored to these use cases, is on the near-term list.
The comparison a buyer actually makes: an SDR runs ~$85K fully loaded and a demand-gen marketer ~$115K — and both draw that base regardless of results. Even hired outside the US, a human rep's monthly salary meaningfully exceeds the Growth plan. Sally's base is a fraction of one hire, works every timezone, never ramps, never churns, and most of her upside is earned only when revenue lands — and the customer also sheds thousands to tens of thousands a year in tooling and infrastructure, the CRM seats and point solutions she makes unnecessary. Software margins on a line item companies already accept as a percent of revenue.
Price the year you hire your first sales rep and point a 5,000-person funnel at the market — the tools, the people, and the glue in between.
Call it $90K a year that stays in the business — before counting that most of Sally's compensation only exists when revenue does. And nothing on her side of the table ramps for three months, quits after eleven, or forgets to update the CRM. Illustrative stack at typical published list prices, rounded; the pile varies by company, but rarely shrinks.
The system is live in production today. Sally's first customer is SallySells — she runs her own go-to-market, works her own pipeline, and the sales call on our website is her, selling herself.
Her second is a beta customer in proptech, a business nothing like ours — exactly the point of a platform built multi-tenant from day one — actively using the product with great early results: a $5K deal already attributed to Sally's first month of effort.
The product works. What remains isn't intelligence. It's the harness.
Everyone gets the same frontier models. They are a commodity we buy by the token, and they get cheaper and better every quarter whether we do anything or not. Being smart is not the moat, and it isn't the hard part any more.
The hard part is everything between a smart model and a salesperson you can actually hire. We call that the harness, and it is the difference between a demo and a business.
Nobody's data is clean. Nobody exports neatly. What actually arrives is a PDF of conference attendees, a calendar export, a CRM dump with three tabs and a do-not-contact list buried in the third, a spreadsheet somebody merged cells in back in 2021, a screenshot of a LinkedIn search, and a sentence of context in the email body.
A good human assistant handles that without being told how. They open it, work out what it is, notice the tab that matters, ask one clarifying question if they need to, and get on with it. That is the bar, and it is a much higher bar than answering questions well. Sally has to meet it — she has to sort through the pile the way a person does, and then act on it.
The second one looks like one instruction and is roughly nine. Parse the file. Work out which of four hundred events were sales calls and which were standups, school pickups and dentist appointments. Identify the counterparty on each. Cross-reference against what actually closed. Find the ones that went quiet. Then write to each of them in a way that acknowledges a year has passed without being strange about it — and pace the whole thing so it doesn't land as a blast.
That is the work. Handling the mess is not a feature around the edges of the product; for the founder we're selling to, it is the product, because the mess is the honest state of their business.
The rest of the harness is surface area: more lead sources feeding the top, more sending capacity underneath, and more channels than email at the point where a deal actually needs saving.
More databases, more signals, more ways to build a list — so the ceiling on who she can find isn't set by one vendor's coverage.
Sending infrastructure that scales with the fleet without ever putting a customer's own domain reputation at risk.
Actually handwritten, in pen, by machines built for it. The thing a rep does for their top three accounts because they haven't got time for more.
To the office of an engaged lead who's gone quiet. Triggered by the state of the deal, not by whoever the rep happened to remember on a Friday.
These are the classic high-touch human moves, and the reason they work is that they're rare and they cost something. A human rep can run them for three accounts. Sally can run them for every account that genuinely warrants one, fired by the actual state of the pipeline rather than by memory and good intentions — and she'll know afterwards whether the doughnuts closed the deal, because she can see all the way to the invoice.
That's the whole ambition of the harness: everything a human salesperson can do, at a scale no human salesperson can reach. It's unglamorous engineering, it isn't waiting on a model that doesn't exist yet, and it's exactly the kind of work that gets finished by a founder who isn't splitting his week.
Intercom sold software that helped human support teams answer customers — inbox, ticketing, knowledge base. Humans still read the questions, found the answers, resolved the tickets. Then it built Fin — and Fin crossed the boundary: it didn't help reps resolve tickets faster. It resolved them itself. Fin became the company's central product, gave the company its name, and led to an announced Salesforce acquisition valued at roughly $3.6B.
Almost everything sold as AI for revenue teams today sits in the middle band — copilots, assistants, AI SDRs. They make the human faster at operating the pile. That's a real improvement and a transitional one, and it is still priced against software budgets, because a human is still doing the job.
The lesson isn't the exit — it's the budget. Fin stopped competing for customer-service software spend and started competing for the vastly larger pool of money companies spend employing people to answer questions. Sally applies the same transition to revenue: a company stops buying software to assemble and manage a revenue operation — it buys the output of the revenue operation.
And the incumbents are structurally conflicted. Salesforce and HubSpot will ship agents — but agents designed to strengthen the CRM, because their economics depend on humans doing revenue work: more employees, more seats, more workflows. Kodak had the technical capability to build digital cameras. Sally is free to build the agent that makes much of the traditional CRM unnecessary. That freedom matters more than their resources.
Sales and marketing are too large, fragmented, and specialized for one winner. Selling a $3,000 SaaS subscription to a small business is nothing like selling a $500,000 enterprise contract, managing local-service demand, or running a channel business. Different autonomous systems will win across industries, deal sizes, and sales motions — there may be dozens of meaningful winners, and they don't need the whole market: only proof that they reliably replace a category of revenue-generating labor.
An autonomous commercial system competes against the total cost of SDRs, demand-gen employees, agencies, ad operators, sales and revenue ops, managers — plus the software stack those people use. For incumbents, acquiring autonomous revenue companies may become the fastest credible way to move from selling tools to selling completed work — and not one universal winner, but several, across segments.
The pattern Fin proved: a software company helps humans perform a function → an AI-native product performs the function itself → it moves from software budgets to labor budgets → incumbents see both a threat and a path into a larger market → multiple category leaders become highly valuable strategic assets. Fin demonstrated it in customer service. Sally is pursuing it in a larger, more valuable labor market.
Everything you've scrolled through — the platform, the infrastructure, the AI bills — I've paid for out of my own contracting gigs. That's also why, if you click Sally's sales call and she won't load, it's not a bug: she's hit the spend cap I can afford. The product works; the wallet has limits.
I have a wife, a mortgage, and three pre-school kids in childcare. For the past six months that has meant building Sally in fits and starts — nights, weekends, and the gaps between the client work that pays for all of it. The product you just read about got built anyway.
That's what this raise actually buys: me, full-time, with the confidence to deliver — instead of a founder splitting his best hours between the thing that pays the bills and the thing that could matter.
Imagine what fits-and-starts built. Now fund the full-time version.
And if you know me well, you know this isn't my first attempt at building a product. I've tried three to five different things over the past six or seven years — and I never believed in any of them the way I believe in SallySells, because this is exactly the product I wanted when I was trying to take those other products to market. At OpenVia we raised a million dollars, hired two salespeople and a sales leader — and the unit economics never worked. It's brutally hard to sell a $300-a-month product with human salespeople unless you're closing dozens of deals a month, and we never got there. With Sally, I believe that outcome would have been very different. The technology wasn't there for OpenVia. It is today — there are still a few edges where it isn't quite ready, but it's getting there while we build, and when it lands we'll be standing exactly where it lands. Nearly fifteen years into this industry, of everything I've ever built, this is my magnum opus.
Here's the part you may not know: I never shut the original company down. It's been going for seven years, and I kept all of my original investors on the cap table instead of wiping it clean, even though many other founders I know wasted their investor's cash and delivered an "oh well." So that's what you should know about me: I won't stop until I win. And if I win, you win — even if the product ends up something slightly different than what we set out to offer.
The instrument Post-money SAFE · $7.5M cap · 20% discount
The money does two jobs: finish the production harness, and fund the pilots that buy our first customers their conviction. Five customers at our blended ~$2,500 average is $12.5K of monthly recurring revenue — default alive, operations and growth funded from revenue, with commission riding on top; ten takes it to ~$25K. At up to a thousand dollars of real spend per pilot, this round buys a lot of proof. The window is now, and it doesn't take much capital to seize it.
This is not a raise to find the product. The product exists, in production, selling. This is a raise to stop it waiting.
And what's waiting is specific. There are companies I could put on a pilot tomorrow — the right motion, real budget, people who have already told me they'd take it. I can't make them the offer today, because every pilot spends real inference and real sending infrastructure before it earns a dollar back. Funded, we go from one external company running Sally to five or more within days.
And each one teaches her something none of the others can — a different market, a different buyer, a different pile of mess handed over in a spreadsheet. That learning doesn't stay in one account. It goes straight back into the harness, where it compounds for everyone who comes after.
The fastest diligence takes eight minutes and no scheduling: take Sally's sales call — she'll pitch you the company herself, handle your objections, and follow up in the morning. Then reply to the email that brought you here.
This is a market with real competition, and what we're building will inspire some who aren't competing with us today to start. A reasonable war chest might be the smart play. But there's a decent chance we never need one: a business growing 10–20% month over month at 80–90% gross margins can fund its own growth.
Here's the thing to understand about future fundraising: hit this round's stated goal — five to ten customers by end of year, each one a case study and a proof point — and we have optionality. Raise at a premium valuation, or just let the machine work and keep the company lean. We won't raise for the sake of raising money. We'll raise only when we know it buys acceleration.
And make no mistake — we're not raising to fund small ambitions. I'm expecting this to be a generational company. That said, dilution is a fair question for any early check — so the simulator below includes the follow-on round. Set its size and valuation yourself and watch what it does to your stake.
AI-native agent companies — software that does the work instead of assisting it — are commanding premium revenue multiples right now, and we expect that to hold for at least the next 12–24 months; Fin's ~$3.6B is the loudest example. Pick the multiple you believe, set the operating assumptions, and watch what a check becomes. Every number below is yours to change.
Calibration: a run-of-the-mill agentic-AI startup trades around 8× top-line today; exceptional, must-have assets with strategic interest reach 20–25× — our ability to target labor spend, not just software spend, is why we believe we land toward that end. The 12× default is deliberately conservative: 100 customers at our ~$2,500 blended average is ~$250K MRR, almost exactly $3M ARR — and 12× makes that a ~$36M exit.
Your SAFE converts to its full ownership first, then dilutes by the new round's share — the same haircut every existing holder takes. Slide it up to model a follow-on round.
Illustrative math, not a guarantee of results. Assumes SAFE conversion at the $7.5M post-money cap, with dilution from the follow-on round exactly as you set it above; real outcomes depend on execution, markets, and luck. But I'm betting my career on it — so I'm in.
Software turned a collection of ledgers into a coherent financial system. It let companies request computing without building data centers. Sally does the same for customer acquisition: a company should build something worth buying — and Sally should handle the machinery required to bring it to market.