Please describe your company, your role, and the industry you operate in.
As VP of Data Science at Clearwave Analytics Inc I oversee technology spend and delivery across our Pharmaceuticals & Biotechnology operations out of Seattle, USA. 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?
A competitor move forced our hand and compressed a roadmap item we'd planned for the following year. The AI & Machine Learning investment we'd scheduled twelve months out suddenly needed to happen in six, and building it internally in that window wasn't realistic.
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?
A peer in the Pharmaceuticals & Biotechnology space had used them for something similar in AI & Machine Learning and recommended them without hesitation. Our own due diligence backed that up. Domain knowledge, AI & Machine Learning depth, and a track record of actually delivering — that combination made the decision easy.
How clearly did the company understand your requirements and business goals?
Really well, partly because they already had Pharmaceuticals & Biotechnology 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?
Genuinely excellent, especially given the time difference between Seattle, USA and the delivery team. Written updates were consistent and specific, anything needing a decision got same-day turnaround, and nothing slipped through the cracks across the whole engagement.
Did the company deliver the project on time and within your expected budget?
Yes to both. I'd quietly built in some contingency given the complexity and the number of integrations involved, and none of it ended up being needed. Delivery landed on the agreed date and the final invoice was within a rounding error of the approved budget — rarer than most people in this industry would admit.
What tangible results or business impact have you seen since the project was completed?
We went live about three months ago. User adoption beat the target we'd set by roughly 20 percent in the first month, and support ticket volume has dropped noticeably since. Features we'd shelved because the old architecture made them too expensive to build are now back on the roadmap.
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 Pharmaceuticals & Biotechnology 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.