Why Prospry exists
AI is new. The problems that determine whether it works inside a business aren't.
The tools are changing fast. The hard part hasn't changed nearly as much.
Technology still has to fit the way people actually work. It has to connect to the right information, handle the exceptions nobody thought about at first, earn the trust of the people using it, and keep working as the business changes.
That's the work Prospry was built for.
Five years in the one system every part of the business touches
Before Prospry, Jared spent five years responsible for a company's Salesforce environment — the system sales worked from, support relied on, and leadership used to understand what was happening across the business.
It wasn't a piece of software sitting off to the side. It was where people, process, data, automation, and business decisions all met.
That seat teaches you things a certification or a slide deck never will.
You learn that the process someone describes in a meeting is rarely the whole process. That a small data problem today becomes a reporting problem six months from now. That an automation that handles the happy path but ignores the exceptions isn't finished. That a technically perfect system creates no value if the people it was built for don't trust it enough to use it.
And you learn what happens after launch — when the business changes, the edge cases arrive, and the thing you built has to keep working anyway.
Prospry came out of that experience.
That discipline, applied to AI
The goal isn't to make AI do something impressive. It's to make something useful enough that a real business can depend on it.
10–20 minutes became seconds
Answering a single inquiry meant searching across reservations, calendars, contracts, property information, and more before a response could even be written.
Prospry built an AI agent that brings that context together, understands what the inquiry needs, and prepares the response — leaving the owner to review instead of research and write from scratch.
Read the full case study →FreightWiseSeven days became minutes
Preparing for a trade show meant days of researching attendees, cross-referencing Salesforce history, and paying an outside vendor to help fill in the gaps.
Prospry built a workflow that brings the research and CRM context together automatically, prioritizes what matters, and gives the sales team useful talking points in minutes — eliminating the outside vendor in the process.
Read the full case study →What that seat taught us
Years spent building systems people actually depend on shaped how we approach every Prospry engagement.
Build for real use, not the demo
Getting something to work once is easy compared with making it dependable. We build around the way the business actually operates — including the exceptions, workarounds, and messy parts that appear after the happy path ends.
Sweat the details
Small problems rarely stay small inside a business system. Bad data spreads. Shortcuts become dependencies. Tiny points of friction turn into things people stop using. We pay attention early because we've seen what happens later when nobody does.
If the team won't use it, it doesn't work
Adoption isn't something that happens after the build. It's part of the build. The right system should make sense to the people using it, fit naturally into their work, and leave them feeling more capable — not like they've inherited another piece of technology to manage.
Launch isn't the finish line
Businesses change. Processes change. Tools change. AI will too. We don't think good work ends at handoff. What we build should be understandable, maintainable, and able to evolve with the business it was built for.
You should experiment with AI yourself.
Seriously.
Use ChatGPT. Try the new tools. Automate a few things. Learn where AI makes your own work easier.
For plenty of problems, that may be all you need.
The point where Prospry becomes useful is when AI needs to move beyond one person using a tool and become part of how the business operates.
When it needs access to company information. When it has to work across systems. When other people need to rely on it. When mistakes have consequences. When the workflow has enough exceptions that "it usually works" isn't good enough.
That's when the question stops being:
"Can AI do this?"
and becomes:
"How do we make this work reliably inside our business?"
That's the question we're built to answer.
Bring us the business problem.
You don't need an AI strategy, a list of tools, or a perfectly defined project before we talk.
Tell us what takes too long, what your team keeps doing by hand, or what feels harder than it should. We'll learn how it works, tell you plainly where AI fits and where it doesn't, and help you figure out the smartest next step.
Talk through your business →