Case study · AI root-cause analysis platform
- Industry
- Fintech
- Services
- AI Solutions, Custom Software, UI/UX Design
- Technology
- Gemini
01 · The challenge
What stood in the way
Teams running multi-service platforms such as wallets and payment gateways lose hours digging through raw logs across services to find out why a request failed.
The same failures come back again and again, and each one is triaged from scratch, often without the context the next engineer needs.
02 · The solution
What we built
Each error log is analysed as it lands, following the “Caused by” chain to the deepest cause. Every report gives a one-line, plain-English summary next to the full technical detail, ordered fix steps, the timeline, cascading failures and a confidence level, and pins the issue to one level: app, infrastructure, database, connectivity, external or config.
Failing endpoints are grouped by reason with real request and response bodies. A Jira issue can be created or linked in one click, and the filterable error stream, raw-log viewer, Markdown export and Flow Recorder help QA find anything fast.
Admins choose their own AI per project as an ordered failover chain, or run fully offline with a local Ollama model or the built-in rule engine. Card numbers and personal data are masked before storage or any AI call, and repeat issues reuse the saved analysis, so they cost nothing.
03 · The results
Seconds
From error log to root-cause report
6
Issue levels classified automatically
0 AI calls
For repeat issues
Next project
Resource Engagement Planner
Want results like these?
Tell us about it. We reply within 24 hours with next steps and a free consultation.