Case Studies
Real production AI engagements with measured business outcomes — across fintech, healthcare, HR, e-commerce, and media.
Client Results
Verified42%
Cost reduction
180hrs
Time saved
99.1%
Accuracy
3.2x
Average ROI
What case studies do we publish?
Production AI engagements with named or anonymized clients and verified outcomes. Each study covers the business problem, the architecture and model choices, the integration and rollout work, and the metrics that moved. We publish only after the system has been live in production long enough for the outcome numbers to stabilize — typically a quarter or more.
Studies under named clients (MatchWise, Rodada) are published with client approval. Studies under anonymized names describe real engagements where the technical work and outcomes are accurate but the client identity is obscured for confidentiality. We do not publish fabricated examples.
Outcomes at a glance
Industry, problem, and the headline result from each engagement.
| Industry | Problem | Headline result |
|---|---|---|
| Human Resources | Manual CV screening at scale | 90% screening time reduction |
| Finance | Real-time fraud across digital banking | 99.2% detection rate, $18.4M prevented |
| Healthcare | Slow, inconsistent imaging diagnostics | 31% accuracy lift, 45% faster reports |
| E-commerce | Underperforming product recommendations | 22% revenue lift, 3.4x CTR |
| Media & Advertising | Manual OOH inventory operations | End-to-end OOH digitization |
Digital OOH Inventory Platform for Mobile Advertising in Mexico
AI-Powered Applicant Tracking System for Smarter Hiring
AI-Powered Diagnostic Imaging for Regional Healthcare Network
Real-Time Fraud Detection System for Digital Banking Platform
AI Recommendation Engine for Global E-Commerce Marketplace
Case studies: frequently asked questions
What kinds of AI projects has Clearframe Labs delivered?
Production AI systems across regulated and high-volume industries — real-time fraud detection in fintech, diagnostic imaging in healthcare, applicant tracking in HR, recommendation engines in e-commerce, and digital transformation of out-of-home advertising. Engagements range from greenfield ML systems to embedding AI into existing products and workflow automation for back-office operations.
Can you share specific business outcomes from these case studies?
Yes — every case study lists measured results. Highlights include 90% reduction in CV screening time and 60% AI inference cost reduction (MatchWise ATS), 99.2% fraud detection rate with sub-200ms latency and $18.4M annual losses prevented (NovaPay), 31% diagnostic accuracy improvement and $2.1M annual savings (MedVista), 22% recommendation revenue lift and 3.4x click-through improvement (ShopStream), and end-to-end digitization of OOH inventory operations (Rodada).
Are the clients real, or are these fictional examples?
Both formats appear. Engagements with named clients and public URLs (MatchWise, Rodada) are real and the client has approved publication. Studies presented under anonymized client names (NovaPay, MedVista, ShopStream Global) describe real engagements where the client has not granted public-naming rights — the technical work, architecture, and outcomes are accurate but the client identity is obscured for confidentiality.
How long does a typical engagement take?
Most production AI engagements run 8 to 16 weeks from kickoff to first production deployment, then a defined optimization period. Discovery and architecture take 2 to 3 weeks, build and integration 4 to 10 weeks, and validation, hardening, and launch 2 to 3 weeks. Long-running platforms with multiple model rollouts (fraud detection, recommendations) typically continue with retainer or staffing arrangements after the initial launch.
What industries do you have the deepest experience in?
Healthcare (HIPAA-compliant diagnostic imaging, clinical workflow), finance (real-time fraud detection, risk scoring, compliance), human resources (AI-powered ATS, candidate evaluation), e-commerce (recommendations, search, personalization), and media and advertising (OOH inventory platforms, audience analytics). We also work in legal, real estate, logistics, and education — see the industries page for the full list.
Can we get an outcome like these for our company?
The patterns transfer when the underlying constraints match — clean data, a clear business metric to move, and an executive sponsor empowered to act on the results. The first step in any engagement is a structured discovery to confirm fit and de-risk the build. We turn down projects where the data foundation or business alignment is not yet ready, because the resulting AI system will not move the metric.
How do you protect client IP and confidentiality?
All engagements run under mutual NDAs from first technical conversation. Code, models, and trained artifacts are owned by the client unless explicitly negotiated otherwise. Anonymized case studies are reviewed by the client before publication. We do not train models on client data for cross-client benefit, and we do not reuse proprietary client code or weights in subsequent engagements.
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