01
Web · Japan client
Kindergarten Action Camera — AI Photo Management
- Stack
- React + Azure Face API + PowerApps.
- Duration
- 4 months, plus ongoing.
- React
- Azure Face API
- PowerApps
AI & machine learning
AI that fits the way your team works — from proof of concept to production.
Face recognition, smart automation and analytics built on Azure Cognitive Services, Python and Power BI. We start small, prove the value on your data, and grow what works — in Japanese and English.
- 2 AI projects delivered for Japanese clients
- Azure Cognitive Services and Face API
- Since 2000 in software delivery
Recognise, sort, report
on your own data
What we deliver
AI applied to a workflow, not bolted on
Each solution starts from one task your team does by hand today, and is measured by how much of that task it takes over.
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Vision
Face recognition and automation
Custom AI workflows that recognise faces, sort photos and automate the steps around them.
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Azure
Cognitive Services integration
Azure Cognitive Services and the Face API connected to your applications and data.
- Face API
- Cognitive Services
- Logic Apps
- IoT Core
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Power BI
Analytics and dashboards
Reports, dashboards and business intelligence support built on Power BI.
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Prototype
Proofs of concept
Rapid prototyping and iterative delivery, so you see results before you commit.
- Proof of concept
- Pilot
- Production
Also in our toolkit: Python for machine learning and automation, Azure Logic Apps and IoT Core.
How we start
Prove it on one workflow first.
AI projects succeed when the first step is small enough to evaluate. We agree what success looks like before building anything.
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Step 01
Pick one workflow
A task your team repeats every day, with a clear measure of success — such as time saved or items sorted correctly.
A success measure you agree to
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Step 02
Prototype on your data
A working proof of concept you can try with real examples, not a slide about what AI could do.
A prototype you can try yourself
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Step 03
Grow what works
Iterate towards production in bounded releases, each tested and signed off by your team.
Releases signed off by your team
AI projects
AI doing daily work for Japanese clients.
Projects whose stack uses Azure Cognitive Services or the Face API.
02
Cloud · Japan client
Smile Check-In Attendance System
- Stack
- Azure Logic Apps + PowerApps + Cognitive Services.
- Duration
- 4 months.
- Azure Logic Apps
- PowerApps
- Cognitive Services
AI in our own delivery
We use AI the way we recommend it.
AI supports the review and testing steps of our delivery process — with engineers accountable for every change.
How we work-
AI
AI-assisted code review flags likely issues first.
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Engineer
An engineer does the final review and owns the change.
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AI
AI-assisted test generation drafts unit and automation tests.
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QA
Our QA team reviews and runs every test.
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Report
Detailed weekly reports in Japanese and English
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Release
Japanese QA sign-off before every release
FAQ
Questions we hear first
Where should we start with AI?
With one workflow your team repeats every day and a clear measure of success. We build a proof of concept on your data first, so you can judge the result before committing to more.
Do we need a lot of data?
Not always. Services like the Azure Face API come already trained, so a proof of concept can start with a small set of your own examples.
Can AI work inside the tools we already use?
Yes. Our AI projects connect to PowerApps, Logic Apps and web applications, so results appear where your team already works.
Can we work with your team in Japanese?
Yes. Status reports, documentation and QA sign-off can all run in Japanese, with weekly reports in both Japanese and English.
Request a callback
Talk to an AI specialist
Tell us about the workflow you want to improve — recognition, automation or analytics — and we’ll call you back.