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CASE STUDYFINTECH

40% faster API response via RAG document analysis

Zero-downtime migration plus a RAG Q&A engine over secure financial archives — a home-finance platform that got faster and smarter in one motion.

FINTECH · ZERO-DOWNTIME MIGRATION + RAG

The challenge

The platform's legacy APIs were straining under growth: response times dragging product experience down, and a modernization long deferred because downtime was unacceptable in a live lending business. Meanwhile, teams burned hours manually searching secure financial archives — agreements, statements, KYC records — for answers the documents already contained.

Two problems, one root cause: the system's knowledge was locked in places its software couldn't reason about.

The approach

01

Estate map before migration

Code intelligence mapped services and dependencies; the migration was specced move-by-move with acceptance tests.

02

Zero-downtime cutover

Strangler-pattern dual-running kept the lending business live through the entire migration.

03

Docs connector on secure archives

Financial documents parsed and indexed with clause-level structure, permissions preserved.

04

RAG with receipts

A Q&A engine grounded in the archive — every answer cited to document and clause, gated where consequential.

05

Measured from the graph

API latency and search-hour metrics reported live, proving the outcome rather than asserting it.

The results

40%Faster API response
90%Less manual search
0Downtime during migration
100%Answers with citations

Why it worked

API responses came in 40% faster on the modernized services, and manual archive search dropped by 90% — compounding wins, since faster APIs improved product experience while the RAG engine turned the archive from a cost center into an answer engine.

The migration proved a broader principle: modernization and intelligence aren't sequential programs. Done on one graph, each pays for the other.

Your function could be next.

Every case here started as one scoped conversation about one painful workflow.