How a finance company cut data ingestion from months to days with Coresix
A 35-person team augmented a costly in-house engineering effort with Coresix — reducing infrastructure costs by 75% and improving RAG output quality by 11%.
The institution
This customer is a finance company that relies heavily on internal data to power its operations and client-facing services. Like many firms in the sector, their competitive edge depends on the quality and speed of their data pipelines — and on the ability of their teams to query and trust the outputs those pipelines produce.
What sets this team apart is that they took AI seriously before it was obvious to do so. They invested in building internal capabilities, hired engineering talent, and developed real data infrastructure. By the time AI became a strategic priority across the industry, they were already ahead of the curve — with proprietary systems, institutional knowledge, and a genuine understanding of what production-grade AI operations actually require.
When knowledge lives in people, not systems
The decision to build in-house was the right one for its time. It produced real AI readiness and genuine competitive advantage. But as the ambition scaled, so did the burden — and the team found itself navigating a set of tensions that every serious finance organization will recognize:
- High security requirements driven by compliance obligations and the sensitivity of the data they handle
- The fundamental trade-off between locking data down and making it accessible enough to be useful
- The pressure to adopt AI while maintaining the ability to verify outputs and trace every answer back to its source
- A make-vs-buy question that had quietly shifted: they are a finance company, not a software company
The in-house build had required 3 full-time engineers and 2 part-time technical consultants, at a total yearly cost of €200k. The output was technically real — but maintaining and scaling it was pulling the team further from their core business, not closer to it.
An intelligent layer on top of what already works
The team deployed Coresix on a Team plan for 35 users — not to replace the in-house build, but to work beside it and enhance it. Coresix connected to their existing infrastructure and extended its capabilities without requiring a rebuild or migration:
- Existing data ingestion managed by Snowflake, with Coresix connecting directly to the existing warehouse
- Existing internal RAG plugged directly into the Coresix workflow via its modular RAG interface
- Coresix platform and agents activated on top of the unified data layer
Rather than replacing what the team had already built, Coresix wrapped around it — preserving prior investment while immediately closing the gaps in speed, reliability, and output quality that had made the system unusable.
How we deployed
- 1
Move existing files to the cloud through a trusted, security-certified partner
The first step addressed the security question head-on. By leveraging Snowflake — a partner that meets the compliance and data protection standards finance teams require — the team could move their existing files to the cloud without compromising on the controls their environment demands. What had taken months was completed in days.
- 2
Plug in the existing RAG without rebuilding it
Coresix's modular RAG interface allowed the team to connect their internal retrieval system directly into the Coresix workflow — no migration, no data loss, no additional engineering required.
- 3
Activate agents on top of a trusted data layer
With data ingested and RAG connected, the team activated Coresix agents across their workflows — giving users consistent, auditable answers grounded in their own data, and giving management the confidence to act on the outputs.
Results
"Management now has higher confidence in what the system produces. That trust is what makes the whole investment worthwhile."— Finance company leadership
What we learned
Build time is not a moat
Investing in in-house infrastructure builds capability — but at some point, maintaining it pulls focus from the core business. When a platform can deliver the same outcome in days, the make-vs-buy calculus changes decisively.
Modularity preserves prior investment
The team didn't have to discard their existing RAG or data warehouse. Coresix's modular connectors wrapped around what was already built — turning a sunk cost into a working asset.
Trust is the real output
An AI system that management doesn't trust produces no value, regardless of its technical quality. Improving RAG output by 11% on an internal benchmark wasn't just a technical win — it was the moment the system became usable.