AI for Analytics & Data Science

Turn repetitive analysis, reporting, and review work into AI-powered workflows.

Below are examples of how we help analytics and data science teams. Each card links to a detailed case study you can use internally to share what's possible.

Consulting / PE

Due diligence document intelligence

Structured extraction and summarization across data rooms and deal documents—reducing analyst prep time while standardizing coverage across transactions.

  • • 50% reduction in manual review
  • • 35% faster deal evaluation cycle

View full case study →

Multi-industry

AI-powered BI analysis with VibeDecision

VibeDecision automated dashboard analysis and recurring reporting—turning metrics into decision-ready summaries so analysts spend less time pulling data and more time on decisions.

  • • Less manual dashboard prep and validation
  • • Faster access to trends, anomalies, and drivers
  • • More consistent business analysis
  • • Analysts freed for higher-value investigation

View full case study →

Mobile / consumer apps

AI-powered app review intelligence

Custom NLP and embeddings that classify app-store feedback into product-specific themes—with prioritization tied to sentiment, volume, and user-behavior impact.

  • • Product-specific themes beyond generic off-the-shelf tools
  • • Prioritization by sentiment, volume, and behavior impact
  • • Closed-loop refinement with product and engineering
  • • Insights embedded in sprint and analytics workflows

View full case study →

Retail media

Algorithmic multi-touch attribution for retail media

Causal, algorithmic MTA that estimates the incremental contribution of each paid touchpoint—so advertisers optimize beyond last-touch reporting.

  • • Moved beyond last-touch attribution
  • • Defensible channel contribution views
  • • Stronger advertiser budget allocation
  • • Differentiated measurement for the media network

View full case study →

E-commerce

Synthetic controls for multichannel revenue lift

A causal synthetic-control framework—and later a self-serve tool—to measure incremental revenue from multichannel engagement when RCTs were not possible.

  • • Isolated multichannel lift from selection bias
  • • More confident marketing and product investment
  • • Interactive tool scaled adoption across teams
  • • Ongoing foundation for lift measurement

View full case study →