Other Industries

A healthcare operations director wants to understand patient satisfaction trends. The data exists — hundreds of survey responses collected monthly through SurveyMonkey. But the analysis pipeline is manual: download CSV, open Excel, code responses, build charts, write summary, present to leadership. By the time insights reach decision-makers, they're describing the reality of two months ago. The operational changes those insights should drive are already late.

We built an AI-driven pipeline for a healthcare provider that transformed this process entirely. Survey responses flow through AWS Lambda and GCP functions into Snowflake, where a ChatGPT API integration classifies sentiment, extracts themes, and generates summaries automatically. What previously took weeks of analyst time now delivers insights in minutes. Scalable, repeatable, and live.

The method transfers. The context is yours. Clarivant's core capability isn't industry-specific knowledge — it's the discipline of building governed, automated data infrastructure and the experience to do it under real-world constraints. The patterns we've refined in retail, CPG, finance, and SaaS translate directly to other sectors because the underlying problems are structural, not industry-specific.

Consider what these problems look like across sectors. In healthcare, patient data sits in EHR systems, survey platforms, scheduling tools, and billing databases — each serving a different department's needs but never reconciled into a unified operational view. In education, student outcomes, enrollment patterns, and resource allocation live in separate administrative systems, making it difficult to connect investment to impact. In logistics, route data, fleet telemetry, and delivery confirmations generate enormous volume without producing the visibility operators need to optimize cost per mile or service level adherence. In nonprofits, donor management, program outcomes, and grant reporting exist in different tools — and the lean teams managing them rarely have time to build the connective tissue between them.

What we bring from enterprise work. We don't arrive with an off-the-shelf industry template. We arrive with a methodology refined across complex enterprise environments — Fortune 500 consumer goods operations, multi-market tech platforms, franchise networks spanning 100+ locations — and adapt it to your scale and constraints. That means:

Data foundation architecture that's right-sized for your organization. A franchise restaurant network needed a full Snowflake + dbt build with 231 quality tests. A healthcare survey pipeline needed lightweight cloud functions with AI classification. The tools differ. The discipline of governed, tested, automated data pipelines stays the same.

Phased delivery that produces value early. We don't disappear for three months and deliver a monolithic platform. We scope in phases — typically a 4-6 week first phase that delivers a functioning data pipeline and initial dashboards — so you see what the infrastructure does before committing to the full build.

AI integration where it matters. Not AI for the sake of it. Specific, well-scoped applications — like the survey-to-insight pipeline — where automation replaces manual work that's bottlenecking decisions. We identify where AI adds genuine value in your workflow and build it into the data infrastructure rather than bolting it on as an afterthought.

What the engagement delivers. A clear data architecture connecting your scattered systems. Automated pipelines that replace manual extraction. Dashboards your team trusts because they reconcile to source data. And a foundation that grows with you — whether you're a 20-person nonprofit or a 500-person healthcare network.

Diagnostic questions. How much of your team's time goes to assembling data versus interpreting it? When leadership asks for a number, how long does it take to produce — and how confident is the team in the answer? If you could automate one manual data workflow tomorrow, which one would free the most capacity for the work that actually matters?