Please describe your company, your role, and the industry you operate in.
As Director of Platform at Daegu Tech Solutions I oversee technology spend and delivery across our Nonprofit & NGO operations out of Daegu, South Korea. We're a commercially minded business, so every technology decision gets judged on what it actually contributes to the bottom line, not on how elegant it looks on paper.
What specific problem or business challenge led you to hire this company?
Our previous vendor had maintained this platform for a few years, and the technical debt had piled up to the point where our delivery speed had dropped to a fraction of what it used to be. We needed fresh engineering eyes and an actual plan to deal with the underlying mess.
What services did the company provide for your project?
The scope ran the full AI & Machine Learning lifecycle — discovery, requirements, architecture, development across roughly a dozen sprints, integration testing, performance validation, deployment, and a four-week hypercare window afterward. They also handed over full documentation and ran a knowledge transfer session for our internal team.
Why did you choose this company over other providers you considered?
We shortlisted five vendors through a structured process. The technical review knocked two out right away. Of the remaining three, this team stood out because their AI & Machine Learning approach was specific rather than generic, backed by actual reference projects in Nonprofit & NGO — not just case studies pulled off a website. The reference calls confirmed everything the proposal claimed.
How clearly did the company understand your requirements and business goals?
Really well, partly because they already had Nonprofit & NGO experience that cut down on the amount of context we had to provide. They knew the terminology, asked the right questions, and translated our business requirements into technical specs accurately enough that development barely needed any clarification cycles.
How was your overall experience with their communication and project management?
Proactive and well-calibrated. Technical detail for the engineering side, plain summaries for the steering group, and risk flags that came with a proposed fix attached rather than just a problem statement. The biweekly sprint reviews kept our stakeholders in the loop without requiring them to sit in on every working session.
Did the company deliver the project on time and within your expected budget?
The project came in on schedule. Budget stayed within the agreed ceiling, including one scope addition we asked for ourselves, which was quoted fairly and handled without disturbing the rest of the timeline. Nothing about the invoice came as a surprise.
What tangible results or business impact have you seen since the project was completed?
Hard to isolate perfectly given everything else going on in the business, but the numbers we can attribute directly to the AI & Machine Learning work are solid: session duration up, conversion up, error rate down, and our NPS for that touchpoint has moved noticeably. Our account managers say it's coming up positively in client conversations now too.
What did you like most about working with this company?
The team stayed the same the whole way through. The engineers in the discovery sessions were the same ones who built the system. That continuity matters more than people expect, and you really only notice it when it's missing. Every conversation picked up where the last one left off.
Would you recommend this company to others, and would you work with them again?
Yes, no hesitation. I've already referred them twice within my own Nonprofit & NGO network, both times to people facing AI & Machine Learning problems similar to ours. I made those referrals confidently because I know the experience I had was repeatable, not a one-off stroke of luck on our project.