We are a small team of engineers. We came out of logistics warehouses, hardware labs, and AI research. Everywhere we went, we found the same problem: the software sold to service businesses was built by people who had never spent a day inside one. So we built something different.
New York runs on service businesses. Moving trucks rolling before sunrise. Cleaning crews cycling through towers that empty out at night. Dispatchers managing a dozen jobs on WhatsApp because no CRM is fast enough to keep up. The city is a living demonstration of the work that keeps everything else running.
We are engineers who came from logistics operations, hardware systems, and AI research. The thread connecting those three worlds was always the same: the hardest problems were not technical. They were coordination problems. And the tools sold to the teams drowning in them were either too broad to be useful or too rigid to fit the actual job.
So we decided to build for the people we grew up around. The coordinators, estimators, dispatchers, and owners who keep things moving. Every product we ship gets used by people whose payroll depends on it working.
We are not an AI-first startup. We are engineers who have already solved hard problems in three different fields. Now we are pointing all of it at the same target: making the operational side of running a service business less painful.
The ground truth of service work.
Before we wrote software for service businesses, we worked inside them. Warehouses, moving crews, last-mile delivery, field dispatch. That background defines what we think a good product means: one that holds up when the job changes three times before lunch.
Where failure is expensive.
Hardware taught us that systems break in ways no documentation prepares you for. We carry that into how we build agents. Fail-closed by default. Honest about their own limits. Always readable by the person checking the logs the next morning.
Only useful when it is grounded.
We treat AI as an engineering tool, not a product category. The models are genuinely powerful. But unless they are wired into a real workflow with clear guardrails, they are just a demo. Everything we ship is built so the intelligence serves the operator.
Software should fit the way people already work.
01 / 04
The best automation is invisible. Our products connect to the WhatsApp groups, Telegram threads, and dispatch tools your team already uses. Forcing a new platform onto a busy operation is the fastest way to kill an automation project.
Small teams build better things.
02 / 04
We are small on purpose. Small teams hold the whole system in their heads. They move faster, own every decision, and do not need three meetings to ship a fix. We stay small because it makes the work better.
Agents that do not leave a trace are a risk.
03 / 04
A system that acts without logging what it did is not useful to any business that has to answer to clients, auditors, or regulators. Every decision our agents make is logged, attributable, and reversible. That is not optional.
The operator knows the job. The AI does not.
04 / 04
We build agents to handle the repetitive work so operators can focus on the judgment calls. The agent proposes, executes what it is approved to do, escalates the rest, and waits. That is the whole loop.
Everyone at Hoogalabs either writes code, reviews code, or talks directly to the operators using our products. There is no layer between the people building the system and the people it is built for. When something breaks, the engineer who built it is the first one to hear about it.
2022 founded in new york
3 core disciplines
100% engineers close to operators
0 layers between you and the build team
Book a free 20-minute call. We will look at the one workflow costing your team the most time and tell you honestly whether it makes sense. Most calls end without a pitch.
