Case studies

Work that saves time and cuts costs.

A look at how we take AI and data projects from idea to production, and the results our clients keep.

Fintech · Lending

Cutting support response times with an AI assistant

62%
faster first response
40%
fewer tickets escalated
3 wks
from kickoff to production
  • RAG
  • Claude + open-source fallback
  • On-prem deployment
  • Eval pipeline

The challenge

A fast-growing lender was drowning in repetitive support tickets. Agents spent hours digging through policy docs to answer the same questions, and response times kept slipping as volume grew.

What we built

We built a RAG assistant grounded in their internal policies and product docs, wired directly into their support tooling. It drafts accurate, cited answers for agents to review, with monitoring and evals so quality stays high as content changes.

E-commerce · Retail

One source of truth and automated reporting

~0 min
reporting, down from days
31%
lower cloud data spend
6→1
data sources unified
  • Snowflake
  • Automated pipelines
  • Managed cloud
  • NL analytics assistant

The challenge

An e-commerce brand pulled numbers from six disconnected tools by hand. Reports took days, often disagreed with each other, and nobody trusted the dashboards enough to act on them.

What we built

We consolidated their data into a single warehouse with automated pipelines, then layered on trustworthy reporting and a natural-language assistant so the team can ask questions in plain English instead of waiting on analysts.

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