— Selected work / systems that earn their place
Research made real.
Each project starts with something a business needs to improve, then moves through research, design thinking, engineering, and production support.
01 / Reading Room
02 / Wallpaper
03 / Computer Vision
— Introduction
The work is evidence of how a business question becomes a system people can use and own.
For a growing business, that can mean fewer customer dead ends, clearer data, less manual work, or a careful path into AI. The method stays the same: understand the pressure first, then build what earns its place.
— Featured case studies
Thinking before technology.
01 / FEATURED PROJECT
Conversational book discovery
SYSTEM STUDY
DIGITAL PLATFORMS
Case study / 01 / Digital platforms
Reading Room
Business problem
The library carried years of useful books and podcast context, but visitors had to browse a static list before they could find the perspective they needed.
Outcome
Turned a static reading list into an interactive discovery tool.
AI & Intelligent Systems
Digital Platforms
AWS
Case study / 02 / Retail & E-commerce
Wallpaper Recommendation Platform
Business problem
The company wanted to make generative design useful for customers without compromising visual quality, print requirements, commercial safety, or store operations.
Outcome
Delivered a custom wallpaper generation platform connected to the storefront.
AI & Intelligent Systems
Digital Platforms
Shopify
02 / FEATURED PROJECT
AI product experience
SYSTEM STUDY
RETAIL & E-COMMERCE
03 / FEATURED PROJECT
Operational intelligence
SYSTEM STUDY
TRANSPORTATION & LOGISTICS
Case study / 03 / Transportation & Logistics
Computer Vision Analytics Platform
Business problem
Organizations had camera infrastructure but limited ways to turn footage into useful decisions, monitoring trends, or timely operational alerts.
Outcome
Created operational trend dashboards and automated alerts from live and recorded video.
AI & Intelligent Systems
Analytics & Data
AWS
Case study / 04 / Retail & E-commerce
Fashion AI: Extract Infos
Business problem
Fashion data is visually rich but inconsistent. The work needed to test what current multimodal models can reliably infer across noisy, cross-store datasets.
Outcome
Built working fashion similarity retrieval, OCR validation, and model comparison workflows.
AI & Intelligent Systems
Analytics & Data
Cloud
04 / FEATURED PROJECT
Applied research experiments
SYSTEM STUDY
RETAIL & E-COMMERCE
05 / FEATURED PROJECT
LLM evaluation framework
SYSTEM STUDY
ENTERPRISE AI & RESEARCH
Case study / 05 / Enterprise AI & Research
LEXA
Business problem
Enterprise teams need stronger ways to assess model accuracy, hallucination risk, and retrieval quality before deploying customer-facing AI systems.
Outcome
Established an enterprise-relevant evaluation direction and scaled benchmarking foundation.
AI & Intelligent Systems
Analytics & Data
Strategy
Case study / 06 / Business networks
Turkish Canadian Business Network
Business problem
The business community needed more than a directory: a controlled, credible place for profiles, discovery, networking, communication, and institutional activity.
Outcome
Created a unified multilingual network platform with structured discovery and permissions.
Digital Platforms
Custom Software
AWS
06 / FEATURED PROJECT
Multilingual business ecosystem
SYSTEM STUDY
BUSINESS NETWORKS
— Complete project archive
All projects, in one quiet index.
Seven examples of customer experience, measurement, product thinking, intelligent systems, and careful deployment in practice.
— Contexts we have worked in
Different contexts. The same need for a more useful system.
These are examples of where Plumfind has learned. They are not limits on who the work can help.
01
Retail & commerce
Recommendation, storefront, product information, and customer decision support.
02
Operations & logistics
Visibility, monitoring, connected workflows, and decision-ready data.
03
Business communities
Discovery, participation, member experiences, and trusted digital operations.
04
Research & AI
Evidence, evaluation, structured knowledge, and responsible production paths.
— How we work
Every useful project follows the same logic.
01
Research before recommendations
02
Design around real behavior
03
Build for production ownership
04
Stay involved after launch
— Start with the business question
Bring us the complicated version.
The most useful work starts with a real operational question, not a preselected technology.