There's a car dealership in southeast Calgary with six identical F-150s that need to move before month-end. The finance manager will do things on rate that he wouldn't consider next week. This isn't on the website. It's not on AutoTrader. It's not in any Google listing or social media post. It exists in the sales manager's head and in a Monday morning whiteboard meeting, and it will be gone by February 1st.
There's a restaurant in Kensington that does strong weekend business but can't fill tables on Tuesday and Wednesday nights. The chef is running an unannounced $55 prix fixe on those evenings — beautiful four-course menu, seasonal, the kind of thing that would sell out if anyone knew about it. But it's not on the website because the owner doesn't want to look desperate, and it's not on Instagram because the social media intern only works weekends.
There's a hotel in Banff that just had a wedding block cancel for next Thursday through Saturday. Fourteen rooms, suddenly empty, less than a week out. The revenue manager is quietly dropping rates on the OTAs but the new pricing won't show up in most search results for another day or two. By the time the algorithm catches up, the opportunity may be gone.
All of these businesses have something they want right now. A specific kind of customer, at a specific time, under specific conditions. This is seller intent — and it's the most valuable signal in local commerce that almost nobody is capturing.
What seller intent actually is
Seller intent is what the business actually wants at this moment — not what it advertises, not what its website says, not what its Google listing claims. It's the operational reality of what the business needs to fill, move, or attract right now.
It takes different forms across sectors, but the pattern is consistent:
Inventory urgency. The auto dealer with aging stock that needs to move before the next allocation arrives. The boutique with last season's inventory taking up shelf space. The florist with arrangements that won't last past tomorrow. Inventory that sits costs money — in carrying costs, depreciation, and opportunity cost. The business knows exactly what needs to move and how flexible it can be on price. This information is never published.
Capacity gaps. The groomer in Bridgeland who just lost a Tuesday regular and has a 2 PM slot open every week. The physiotherapist with three open appointments on Friday afternoon. The dog daycare that filled its morning group but has space in the afternoon. Unfilled capacity is perishable — once Tuesday at 2 PM passes, that revenue is gone forever. Businesses know their gaps intimately but have no channel to broadcast them to the right buyers at the right time.
Demand shaping. The restaurant that needs more weeknight covers, not more Saturday reservations. The hotel trying to fill a midweek gap while turning away weekend traffic. The dentist who wants more adult patients and fewer pediatric because the pediatric specialist just left. These businesses aren't trying to get "more customers" generically — they want specific customers at specific times. The specificity is the value.
Strategic positioning. The vet clinic in Marda Loop that just hired an exotic animal specialist and wants to build that side of the practice. The personal trainer who's pivoting from general fitness to post-rehab conditioning. The accounting firm that's trying to move upmarket from small sole proprietors to mid-size businesses. These signals represent where the business is going, not where it's been — and they're invisible to any backward-looking data source.
Why this data is so valuable for matching
Here's the core insight: seller intent turns a cold recommendation into a warm match where both sides benefit.
Without seller intent, an AI agent recommending a restaurant is guessing. It knows the restaurant exists, it knows the cuisine type, maybe it knows the star rating. It can say "Hillside Bistro is an Italian restaurant in Kensington with 4.6 stars." That's a search result, not a recommendation.
With seller intent, the same agent knows that Hillside Bistro is actively trying to fill Tuesday evening tables, is running a $55 prix fixe that isn't publicly advertised, and the chef is especially proud of the seasonal menu this week. Now the agent can say: "Hillside Bistro has an unadvertised $55 four-course prix fixe on Tuesday nights — the chef is running a seasonal menu and the restaurant is actively looking to fill midweek tables, so you'll likely get excellent attention."
The customer gets a better experience. The restaurant gets the customer it actually wanted. The agent looks like it has insider knowledge — because it does. Everybody wins.
This is the fundamental difference between discovery and matching. Discovery says "here are businesses that exist." Matching says "here's a business that wants what you're offering and is offering what you want." Seller intent is the signal that makes matching possible.
Why nobody captures it
If seller intent is so valuable, why isn't everyone collecting it? Three structural reasons.
It's ephemeral. Seller intent changes daily, sometimes hourly. The F-150s that need to move this week might be gone by next week. The Tuesday prix fixe might end when the restaurant hires another server and can handle walk-in volume again. The hotel's midweek gap might fill after a conference books a block. Any system that captures seller intent needs to handle data that expires quickly and updates frequently. Static databases, annual surveys, and periodic crawls can't keep up.
It's strategically sensitive. Businesses don't want their competitors — or their customers — to know they're struggling to fill capacity. A hotel doesn't want to broadcast empty rooms because it undermines pricing power. A dealership doesn't want other dealers to see which inventory is aging. A restaurant doesn't want to look like it can't fill Tuesday nights. Seller intent is shared selectively, with trusted partners, not published on the open web.
It requires a direct relationship. You can't scrape seller intent because it was never published. You can't infer it reliably from external signals — a half-empty parking lot on Tuesday could mean the restaurant is closed for a private event, not that it needs more covers. The only way to get accurate seller intent is to ask the business directly, in a relationship where the business trusts that the data will be used to send them the right customers, not to undercut their pricing.
This combination — ephemeral, sensitive, and requiring direct relationships — is precisely what makes seller intent so defensible as a data asset. Anyone can build a scraper. Building a network of businesses that trust you with their operational reality is a fundamentally different kind of moat.
How Pawlo captures it
The Pawlo model is built around one principle: make it trivially easy for a business to share what they want right now, and make sure that data reaches the right buyer within minutes.
The channel is SMS. A hotel revenue manager texts "14 rooms just opened Thu-Sat, willing to go 25% below rack" and it's structured, tagged with an expiration, and queryable by every connected AI agent within minutes. The groomer texts "Tuesday 2pm just opened up, great for a first-timer" and any agent fielding a grooming query in that neighborhood can surface it immediately.
No login. No dashboard. No CMS. The business shares intent through the most natural channel possible — a text message — and the system does the work of structuring, categorizing, and distributing it.
The trust model is critical. Businesses share seller intent with Pawlo because they understand the deal: your data goes to AI agents that are actively matching buyers to businesses. It doesn't get published on a website. It doesn't get shared with competitors. It reaches the people who are looking for exactly what you're offering, and nobody else.
The compounding effect
Seller intent has a compounding property that makes it fundamentally different from other data types.
When you have seller intent from one restaurant, you can make slightly better recommendations. When you have it from twenty restaurants in the same city, you can make dramatically better recommendations — because now you're not just matching the user to a good restaurant, you're matching them to the restaurant that most wants their specific type of business right now.
The more businesses share intent, the better the matches. The better the matches, the more transactions happen. The more transactions happen, the more businesses see value in sharing intent. This is the flywheel: data density drives match quality, match quality drives transactions, transactions drive more data.
And the flywheel has a geographic component. Seller intent is most valuable when you have coverage across a local market — not just one hotel in Banff, but many hotels, restaurants, outfitters, and tour operators. When an agent planning a Banff trip can access seller intent across the entire hospitality ecosystem, the recommendations become transformative. "This hotel has a midweek gap, this restaurant is running an unadvertised tasting menu, and this outfitter just had a cancellation on their Wednesday ice walk — here's a three-day itinerary where every business is actively happy to have you."
That's the vision. Not a directory. Not a search engine. A real-time matching layer where every recommendation comes from what both sides actually want, right now.
The opportunity window
Seller intent is being captured by no one at scale. Review platforms don't have it. Google doesn't have it. OTAs capture a narrow slice of it (pricing and availability for bookable rooms) but miss the nuance — the why behind the pricing, the flexibility that isn't in the rate card, the specific type of customer the business is seeking.
AI agents are creating demand for this data right now. Every agent making local recommendations would produce better results if it had access to seller intent. The agents know this. The businesses know this. The missing piece is the trusted infrastructure that connects them.
The business that texts "I've got six F-150s to move and I'll deal" and immediately gets matched with a buyer who's been shopping for exactly that truck — that's not a future scenario. That's the system we're building, and it works because seller intent is the one signal that makes both sides of the match happy.