Building Sprint AI
Why location intelligence needs to be address-level, multi-vertical, and recommendation-first
When I started Sprint AI, the question wasn't whether the location intelligence category needed another tool. It clearly didn't. RetailSonar, Placer.ai, CARTO, Kalibrate, and a handful of regional players already serve enterprise customers competently.
The question was whether the existing tools were actually solving the right problem.
Three observations from working alongside enterprise location decisions over fifteen years convinced me they weren't:
Observation one: Most tools score sites at zone level, but real decisions happen at addresses.
When a franchise expansion director evaluates a vacant retail unit, they need to know about that specific spot — not the cadastral sector it sits in. A 200-meter difference inside Antwerp's Meir corridor changes footfall by an order of magnitude. Existing tools that aggregate to statistical sectors lose that resolution. The decision-makers know it, but they've adapted to the limitation.
Observation two: Most tools are built for a single vertical at a time.
The same analytical engine — the same demographic inputs, the same competitive density modeling, the same accessibility scoring — should serve carriers placing parcel lockers, franchise chains opening retail units, and vending operators deploying machines. The economics differ across these verticals, but the underlying questions are isomorphic. Yet location intelligence platforms tend to be retail-only or telco-only or vending-only. Customers in adjacent verticals get nothing.
Observation three: Most tools score, they don't recommend.
A scout uses RetailSonar to evaluate locations. Then they spend weeks looking at fifty more spots themselves to find better candidates. The tool answers "is this site good?" but never answers "where should the next one go?" The hardest, most expensive part of the workflow — the search itself — gets pushed back to the human.
These three observations shaped Sprint AI's architecture. Not as a marketing position, but as engineering decisions made before the first commit.
Address-level granularity
Sprint AI analyzes specific geographic points, not zones. When you click an address in our platform, the system pulls footfall data, demographic context, competitive density, infrastructure quality, and commercial activity for that exact location and its catchment.
This sounds obvious, but it requires designing every data integration around point geometry rather than polygon geometry. Mobile panel data, Statbel demographics, OpenStreetMap infrastructure, Google Places POIs, traffic patterns from TomTom — all need to resolve cleanly to a coordinate, not be aggregated up to a postal code or municipality.
The payoff: a real estate director evaluating a vacant unit at Meir 80-82 in Antwerp gets analysis specific to that storefront, including how it interacts with the existing locker at the same address. Zone-based tools cannot do this.
Multi-vertical from one engine
Sprint AI serves three distinct customer types — parcel carriers, franchise retail, and vending operators — through the same platform.
Each vertical evaluates locations differently. A parcel locker cares about commuter flows and twenty-four-seven accessibility. A franchise café cares about purchasing power and dwell time. A vending machine cares about captive audiences and lunch-hour traffic. The same address scores meaningfully differently across these three lenses, because what makes a location good depends on what you're using it for.
We handle this through dedicated reasoning layers per vertical. The data inputs are shared. The analytical interpretation is not. This means one codebase serves three non-overlapping enterprise markets — and customers can compare a location across vertical use cases when they want to (a property owner deciding whether their site is better leased to a carrier, a franchise, or a vending operator gets all three answers in seconds).
Recommendation-first, not score-first
This is the architectural decision I'm most proud of. When Sprint AI evaluates a location and the score comes back low, we don't stop there. The system automatically searches the surrounding area for better alternatives and surfaces them with reasoning.
This transforms the workflow. Instead of:
- Scout proposes ten locations
- Tool scores them
- Scout looks for ten more, repeats
The flow becomes:
- Scout proposes one starting location
- Tool scores it and surfaces three to five better nearby candidates
- Scout reviews ranked alternatives, picks the strongest, moves on
The hard work — exploration, alternative discovery, comparative ranking — moves from human to machine. The human keeps the judgment.
We've extended this idea into network-level expansion planning. A customer can specify a region and a target — either "I want ten new locations across Antwerp province" or "I have a five-million-euro capex budget to deploy" — and Sprint AI returns an optimized portfolio of recommended sites, ranked by financial impact, with cannibalization against the existing network already accounted for.
That second mode is, I believe, novel in the category. Most tools require the user to manually propose every candidate. Sprint AI does the proposing, and presents the top set as a deployable plan with NPV, payback period, and IRR per location.
Why now
Two things made Sprint AI buildable in 2026 that wouldn't have been feasible three years earlier.
First, large language model reasoning has matured to the point where AI can produce genuinely sophisticated location analysis — not just scores, but coherent narrative reasoning about why a location works or doesn't, what risks to consider, and what alternatives might be better. That last layer is what separates location intelligence as commodity from location intelligence as decision support.
Second, the European data ecosystem has matured. Statbel publishes detailed demographic data. OpenStreetMap covers Belgian infrastructure with surprising fidelity. Mobile network operators are increasingly willing to license footfall panels to qualified buyers. The data needed to build credibly at address level is there, if you stitch it together carefully.
What we're building toward
Sprint AI exists to change the buyer's question from "is this location good?" to "where should our next one go?"
Those are different products. The first is a scoring tool. The second is a recommendation engine that can plan a network at scale. The first is a commodity in five years. The second changes how location decisions get made.
We're building the second.
Sprint AI is a Belgian location intelligence platform serving carriers, franchises, and vending operators. The platform is currently in private beta with select enterprise customers. For partnership inquiries, contact tim@sprint-ai.be.