CUSTOM APPLICATIONS / UNIVERSITY LABS
Fill the gaps in your existing software. We build custom applications that help lab teams work together and connect tools that don’t usually talk to each other.
01 / A REAL ENGAGEMENT
Find the software gap.
Make a practical starting point.
We met with the professor leading a university lab to explore gaps in its existing software: how team members could work together and how tools that usually operate separately might connect. The engagement has reached the prototype stage.
An initial prototype gives the lab a concrete starting point for reviewing the proposed application against those needs.
The engagement has reached the prototype stage.
02 / WHAT WE BUILD
Extend what your lab can do with custom applications, connected workflows, and practical AI.
Build the capabilities your current software is missing, around the way your lab works.
Start with a task your existing software cannot support, or a new workflow that better software could make possible. We define a focused application around that need.
Scope, data access, and user requirements are defined together.
Help researchers share information and connect tools that usually work in isolation.
Explore shared workspaces, smoother handoffs, and connections between existing tools. We start by checking available APIs, data formats, and the access your lab can provide.
We agree which information should move between tools and where people need to review it.
Explore what becomes possible with better software, and where AI can make a practical difference.
Potential uses include searching approved lab knowledge, organizing documents, and assisting repetitive information tasks.
We assess feasibility, data permissions, and review requirements, then define a practical first prototype.
03 / THE WAY WE WORK
Meet the professor or lab lead. Understand the work, the people involved, and the steps that slow them down.
Turn the conversation into a focused application brief, with a clear purpose and practical boundaries.
Develop a prototype that gives the team a concrete starting point to review and discuss.
Review it against the intended workflow. Use feedback to decide what to improve and what to build next.
04 / THE TEAM
Experience in AI hardware, software engineering, and leading a university research lab.
AI hardware & data center chips
Cindy spent five years as a chip engineer working on AI hardware for data centers.
AI software engineering
Joseph is an AI software engineer who previously worked at AWS.
Research & laboratory leadership
Philip is a university professor who leads his own research lab. He has authored multiple papers published in Nature and Science.
SolveHub Technology Inc. is a federally incorporated Canadian company providing software development and AI consulting.
LET’S START WITH YOUR WORK.
Tell us what your current software can’t do, who needs to work together, and which tools you wish could connect.
cindy@solvehubtech.comStart a conversation