Case Studies

What we built and what changed.

Each project below started with a specific problem and ended with a measurable result. Client names are withheld unless they have agreed to be named.

AI
80–90%
of support requests handled autonomously
Client name withheld

An AI agent that resolves most support requests on its own

The challenge

The client's support team spent most of its day on repetitive requests, and response times grew with every new customer.

What we did

  • Mapped the most frequent request types and the systems an agent needs to resolve them
  • Built an agentic AI assistant that answers from the client's own knowledge and completes routine actions
  • Added guardrails and a clear hand-off to a human whenever a request falls outside what the agent should handle

The result

The agent now handles 80–90% of incoming support requests end to end, so the team focuses on the cases that need a person.

Cloud
~22%
reduction in AWS spend
Client name withheld

Cutting an AWS bill without slowing the product down

The challenge

AWS costs were rising faster than usage, and nobody had a clear view of where the money was going.

What we did

  • Broke spend down by service, environment and workload to find where cost and value had drifted apart
  • Right-sized over-provisioned resources and removed idle and orphaned ones
  • Put tagging, budgets and alerts in place so the savings hold as the platform grows

The result

Monthly AWS spend dropped by around 22% with no impact on performance or reliability.

AI
Automated
defect detection on solar panel imagery
Client name withheld

Computer vision that spots defects on solar panels

The challenge

Finding damaged or faulty solar panels relied on manual inspection, which is slow and hard to scale across large installations.

What we did

  • Prepared and labelled image data covering the defect types that matter to the client
  • Trained and evaluated a computer vision model to detect and locate defects
  • Packaged the model so inspection images can be processed without manual review of every panel

The result

Inspection teams get flagged defects from imagery instead of checking every panel by hand.

Product
302
courses across 8 domains matched to each student's profile
Built and run by Bitlify

Edupath: our own AI product for choosing a university course

The challenge

Students choose university courses with limited guidance, often without considering their personality, interests or market demand.

What we did

  • A 15-question assessment covering personality (Big Five) and academic interests
  • A neural network model that builds a profile across eight interest domains, including medical studies
  • Recommendations of the best-fit courses from 302 options, ranked using yearly market-demand data

The result

A working AI product we designed, built and operate ourselves, open to universities and institutions.

Visit Edupath →

What clients say

Feedback from teams we have worked with, published with their permission.

“The impact of Bitlify was immediate and significant. Their expertise in cloud cost optimisation led to a 15% decrease in our AWS bill. They also fortified our security posture and automated critical processes like our database backups and CI/CD pipelines. Their work has allowed our team to focus more on development and less on infrastructure management. The results speak for themselves”
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