Build Engine
sprinter.ai
AI-native product building. We work beside the operators who hold the domain knowledge, turn what they know into a specified wedge, and ship evaluated software with its review boundaries written down.
SPRINTER · EST. 2018 · AI-NATIVE PRODUCT BUILDING
AI products built with the people who know the problem from the inside.
Sprinter is an AI-native build practice for family offices and multi-entity operating companies: we turn inside knowledge into evaluated software and reliable workflows — and run our own products on the same system.
- Build engine
- Product and evaluated software
- Execution practice
- Setup and managed execution
- Venture studio
- Incubations and internal bets
§ 01Ecosystem
One practice, three surfaces.
Sprinter is organized around what a piece of work actually requires: a build engine that ships product, an execution practice that runs the work inside a company, and a venture studio that carries its own risk.
Execution Practice
Sprinter Consulting
Sprinter Consulting is the execution practice: workflow setup, managed execution, and implementation inside a single operating company, taken on after the need is proven rather than before.
Venture Studio
Sprinter Studio
Sprinter Studio is the venture studio: partner incubations and internal experiments — Sprinter's own bets, kept separate from client work.
§ 02Proof
The work we point to.
Public products, our own systems, and private client work — described plainly, without borrowed logos or numbers we cannot stand behind.
Cab-O-Matic
Vertical software
Quoting and configuration software for the cabinet industry, built around manufacturer catalogs, product lines, option rules, and pricing logic. Cited for catalog-scale infrastructure: the kind of domain complexity that has to be modeled correctly before an AI layer on top of it is worth anything.
Cited for the engineering. We publish no catalog, customer, revenue, or savings figures.
Praxium
Public product
A vertical workflow product for wellness and recovery operators, public at askprax.ai. Tyler Dreher is a co-founder. Cited as execution evidence — the practice ships and operates real software, not demonstrations.
Execution evidence, nothing more. We publish no customer, outcome, or revenue figures.
Amble
Self-use
The company brain Sprinter built to hold its own operating context. We run it on ourselves before the pattern would ever be proposed to anyone else.
Self-use only. It is not a product we sell.
MortgageQ
Prototype
A research surface built over wholesale mortgage lender guidelines and product data, and the clearest example of how a research question becomes a queryable domain model.
A prototype, published as one. No adoption or revenue figures attached.
Multi-entity family office
Anonymized client work
Private enablement work with executives and their teammates on their own finance, research, reporting, meeting, and SOP work inside a multi-entity family office.
Anonymized by agreement. The client's identity and numbers stay private.
§ 03Amble
Amble is the company brain, built by Sprinter.
Sprinter built Amble to hold its own operating context: decisions, standards, source material, and the reasoning underneath them, in a form the team and the models both work from.
It is the clearest statement of how this practice thinks about AI-native operating — the knowledge has to live somewhere durable before software or workflows can be trusted with it. We run Amble on ourselves. That is the whole claim.
Self-use only. It is not a product we sell — it is how we work.
§ 04Offers
A small number of focused ways to start.
Each is a defined scope with its own page and its own price. Nothing escalates automatically; stopping after one is a normal outcome.
Product Wedge Review
Start here when the product is unclear
Turn insider knowledge into a buildable product decision: the wedge, the owner, the data, and the proof line, scored rather than asserted.
Embedded AI Product Build
The build engine
One accountable path from a scored wedge to shipped, evaluated software, with the same people from decision through delivery.
Executive AI Accelerator
$2,500
Private working sessions that turn recurring executive work into repeatable AI workflows the executive owns and can run without us.
Portfolio Executive AI Accelerator
$10,000
Individually scheduled accelerator seats for five leaders, plus an aggregate sponsor readout instead of individual performance ranking.
AI Operating Partner
Only when it is earned
A portfolio-level operating relationship, proposed only after recurring demand, ownership, access, and economics are proven.
§ 05Founder
Tyler Dreher founded Sprinter in 2018 and still does the work.
Sprinter is a small, senior practice. The person who scopes the wedge is the person in the build, which is the only reason a practice this size can promise that inside knowledge survives the trip into software.
Tyler is a co-founder of Praxium and writes about the operating side of AI-native building.
The practice stays small on purpose. When a request is a better fit for the execution practice — or for no one at Sprinter — we say so.
§ 06Questions
The questions that come first.
What does Sprinter actually build?
AI-native products and workflows for family offices and multi-entity operating companies: quoting and configuration systems, document-grounded research and review surfaces, operator consoles, intake and triage, and the evaluation harnesses that keep them honest. The pattern library is the public version of that catalog.
Why build with the people who know the problem from the inside?
Because domain knowledge is the scarce input, not model access. The work starts with the operators who already hold the rules, the exceptions, and the edge cases, and turns that into a specification before anything gets built.
What is the difference between Sprinter and Sprinter Consulting?
sprinter.ai is the build practice: product decisions, software, and evaluated workflows. Sprinter Consulting is the execution practice: setup, managed execution, and implementation inside one operating company. Same people, different scopes.
Do you have case studies?
Not in the usual form. We name what is already public — Cab-O-Matic, Praxium at askprax.ai, and Amble as self-use — and describe private work anonymously and without figures. We would rather show a working system than a slide.
How does an engagement start?
With a scoped, paid piece of work rather than an open discovery phase: a Product Wedge Review when the product decision is unclear, an Embedded AI Product Build when it is not, or an Executive AI Accelerator when the need is leverage rather than software.
Next step
Bring the problem you already understand better than anyone.
We will tell you whether it is a build, a workflow, or neither — before anyone signs anything.