For Business
AI for SMEs
Reduce repetitive work, connect your data, and give the team AI support
Explore AI for SMEs →AI FOR SMEs & EDUCATION
Making AI workin business and in learning
PATSMILE helps SMEs cut repetitive work and connect workflows with AI and automation, and helps education teams build AI learning environments that students and staff can actually use.
Practical AI Start from a real problem Build something that runs
Choose your path
Business or education —
start with the work you want to make easier
Both markets share one idea: put AI to work on real tasks. What differs is the problem and the environment.
For Education
AI for Education
Build a real environment for learning AI, coding and technology
Explore AI for Education →Use cases
Six things we help with most often
Pick the work closest to what you are dealing with, then read the detail on that market page.
Document Automation
Read documents and fill in the same fields, without the copy-paste
Learn moreReporting Automation
Collect the data and draft the report; the team still reviews it
Learn moreKnowledge AI
Make company documents and SOPs answerable in one place
Learn moreAI Learning Lab
Let students experiment with AI in a browser, with nothing to install
Learn moreAI Coding
Learn programming with AI help while still understanding the basics
Learn moreAI + Robotics
Connect sensors, cameras and robots to AI
Learn moreProduct philosophy
Build from real problems.
We do not build a product because a technology is trending. We build from problems that actually happen in the work.
For SMEs
PainPrototypeAutomationProduct
Start with one task that wastes time every day, then prove it before scaling.
For Education
Learning ProblemLabExperimentProjectReal-world Skill
Start from a learning problem, then design a lab where students do the work themselves.
How we work
Make each step count
before taking the next
- 01
Listen before proposing
We ask about the real work, the people who will use it, and what has already been tried, so we do not repeat the same approach.
- 02
Agree on a clear scope
We choose one task, define data boundaries and evaluation criteria, and explain what makes sense now and what can wait.
- 03
Prove it before expanding
We test with real work and measure together. If it does not fit, we adjust or stop before expanding.
- 04
Hand over with ongoing care
We train the team on the work, provide documentation, and agree on ownership and support after launch.
Projects
Work we build and run ourselves
Every project below is something we built or run ourselves — not a customer case study — so each one carries its category and status.
AI PlatformProduction Demo
AI Gateway — one endpoint for many models
Multiple AI providers behind a single endpoint, with user accounts, access control and usage tracking — so a team can call AI like an internal service.
BusinessProduction Demo
AI Chat — a team assistant you can configure
A team chat built on a Hermes agent — access designed by role and team, the agent's personality tuned to the job, and the model chosen per task. Everything is configured from one back office.
BusinessProduction Demo
Legal Workspace — legal document assistant
A drafting and search assistant for legal documents that runs on internal data under full control (Local Vault), citing its sources every time so answers can be checked.
BusinessInternal Project
Condo Rental Manager
An internal project for managing rental units, built with AI assistance and reviewed by people.
BusinessPrototype
Frozen Food Vending — machine control and sales
A frozen-food vending machine project where we deliver both the machine and the system — sourcing and importing the unit from a factory in China, adapting it to sell frozen food, and building the control and sales software that runs on it.
- Source the machine and factory spec in China, and handle the import
- Adapt the unit for frozen food — temperature, product slots and dispensing
- Machine control — temperature, status and fault alerts
- On-machine sales — product selection, payment and receipt, plus a back office for sales and stock
AI PlatformInternal Project
This website
The story, content and illustrations of this site are built and refined with AI and people working together.
About PATSMILE
AI Solution & Product Studio
built by people who ship
PATSMILE grew out of an interest in making AI genuinely usable. We bring experience in software, AI, infrastructure, automation and IoT to solutions for small businesses and education.
We believe a system needs someone to look after it, so you talk directly to the person who designs and builds it.
FAQ
Questions before the first step
We want to use AI but do not know where to start. What should we prepare?
You do not need to prepare anything. Tell us about the work you want to improve. We will ask about the data, users, and constraints, then see whether there is a suitable task to try.
Our team tried AI and stopped. How would this be different?
We start by finding out where the previous attempt got stuck, then involve the people who will use it in choosing and trying it with real work. Practice and feedback can help make the tool fit the team, but results need to be measured before deciding to expand.
Will our data leave the organization?
That depends on the deployment and connected services. Together, we define storage and access controls and review any data that would be sent externally before use. Security also depends on configuration, policies, and ongoing maintenance.
Our data is scattered. Do we have to reorganize everything first?
Not necessarily. We can start with the information needed for one pilot, checking its readiness, accuracy, and access permissions. If it is not sufficient, we will identify what needs attention before connecting the system.
How soon will we know whether it works for us?
Timing depends on scope, data quality, and the systems involved. Once we understand the task, we estimate a timeline and agree on a pilot and evaluation criteria before deciding whether to build further.
Who helps after handover if the team is still learning?
We can plan practice with real tasks and documentation suited to your team. Before launch, we agree on ownership, help channels, and the scope of ongoing support. If your team wants to maintain the system, we plan knowledge transfer together.
Let’s talk
Tell us about the work
that eats your time
If you are worried about choosing the wrong starting point, we can consider a small task first. If you are concerned about data, we can discuss boundaries. If your team needs help learning, we can plan that together. No preparation needed.
Tell us about the work that eats your time
