Do we need to replace our core banking system?

No. Fluree creates a semantic layer over your existing core, LOS, and ancillary systems. They stay in place while Fluree connects to them and unifies data for AI and analytics.

How long does implementation take?

Most community banks are running initial queries within 4–6 weeks. Full deployment is typically 8–12 weeks depending on source systems — a fraction of a core replacement or warehouse build.

Do we need data scientists or AI specialists?

No. Business users can query the data in natural language, so your credit analysts, lenders, and operations leaders don’t need to write code or wait on a specialist team.

How does Fluree AI avoid hallucinations?

Fluree only answers from actual data relationships inside the unified knowledge graph. Each response is grounded in verified operational data with lineage back to the source records.

What about regulatory compliance?

Fluree is built for regulated industries with immutable audit trails, policy-aware data access, and full traceability for FDICIA, CRA, and BSA/AML workflows.

How is Fluree different from our core vendor’s analytics?

Core-vendor analytics only sees the data inside that vendor’s environment. Fluree unifies the core with LOS, document management, treasury, and other systems so your teams work from the full picture.

Can Fluree help with M&A integration?

Yes. Fluree creates a unified data layer across both institutions immediately, giving leadership day-one insight while both cores keep running through a deliberate integration plan.

How does pricing work?

Pricing is based on deployment model and data volume. For most community banks, the cost is a fraction of a core replacement or a ground-up data warehouse program.

Your core wasn’t built for AI. Your data layer can be.

Community and regional banks are using Fluree to unify siloed data across core, loan, treasury, and ancillary systems — creating an AI-ready foundation that makes bankers smarter without replacing the systems that work. Jack Henry. Fiserv. FIS. Legacy. Whatever your core is, Fluree connects to it. Your data doesn’t move; intelligence does.

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What Is Core Banking AI

Modernize without replacing the core.

Your core banking system was installed before the iPhone existed. The board wants AI insights; your data still lives in silos that don’t talk to each other. Your lenders toggle across four screens and still miss the full customer relationship.

Meanwhile, 44% of new checking accounts go to digital banks and fintechs. You can’t afford a $2–5M, 18-month core replacement. You also can’t afford to wait.

Fluree creates a semantic layer over your existing core, LOS, and ancillary systems — one AI-ready graph in 4–8 weeks, not a rip-and-replace.

How it works

Three steps.
One platform.

From a raw source to a governed knowledge graph — with answers that trace back to the row they came from.

Connect any source.

CSV, API, Postgres, Snowflake, Salesforce — Fluree ingests it as-is. No schema migration. No pipelines to maintain.

The graph builds itself.

Entities resolve, duplicates merge, and relationships infer in place — no modeling marathon, no manual ontology.

Answers, with receipts.

Ask in plain language. Every answer traces back to the exact row it came from — for humans, agents, and apps alike.

Why Banks Choose Fluree

Six capabilities no core vendor
or warehouse gives you on its own.

Jack Henry, Fiserv, FIS, or legacy — Fluree connects, doesn’t replace

Your core banking system stays exactly where it is. Fluree connects through standard APIs and creates a semantic layer across the 4–8 systems most community banks already operate — no rip-and-replace, no 18-month core migration.

Jack Henry, Fiserv, FIS, and legacy systems supported
Core, LOS, treasury, docs, and ancillary systems unified
No data movement — intelligence moves, data stays

One graph across core, LOS, treasury, and document systems

Customer data lives across 3–5 reporting systems that compete for the same answer. Fluree resolves customers, accounts, loans, properties, and households into a unified graph — one durable identity with lineage back to every contributing source.

Customers, households, accounts, loans, properties resolved
Golden records with lineage to every source system
One truth across the enterprise, not per-screen

Credit, lending, ops, and compliance teams ask questions directly

Credit officers, lenders, and compliance leaders ask questions in plain English — no SQL, no analyst queue, no waiting for dashboards. Queries return cited, source-attributed answers in seconds.

Natural-language queries across every system
Dashboards generated on the fly, not waited on
No specialist training required for business users

95%+ accuracy with full data lineage on every answer

Fluree only answers from actual data relationships inside the unified knowledge graph. Every response is grounded in verified operational records with lineage back to the exact source. No generative guesswork.

GraphRAG — answers over declared paths, not fuzzy similarity
Lineage attached to every value returned
Defensible when the examiner asks why

Immutable lineage + policy-aware access for FDICIA, CRA, BSA/AML

As you cross $500M, $1B, or $10B, reporting expectations rise while the compliance team stays lean. Fluree preserves immutable data lineage, policy controls, and a queryable trail for every answer — scale regulatory reporting without scaling headcount.

Immutable audit trail across every source
Role-based access logged for every query
FDICIA, CRA, and BSA/AML workflows supported

Proof-of-value without an 18-month core or warehouse project

Most community banks run production queries in 4–6 weeks. Full deployment is typically 8–12 weeks — a fraction of a core replacement (18–24 months, $2–5M+) or a ground-up warehouse (6–12 months of ETL pipeline work before any insight lands).

Production queries in 4–6 weeks
Full deployment in 8–12 weeks, typical
Incremental value — every week, more answers unlocked

Side by Side

Core vendor analytics or warehouse builds vs.
Fluree.

See how a semantic data layer compares across the dimensions that matter most to community and regional banks.

Capability	Traditional Core vendor or warehouse	Fluree Semantic layer
Data scope	Core only, or siloed warehouse	All banking systems unified via semantic layer
Cross-system queries	Not possible, or months of ETL	Native — day one
AI accuracy	N/A, or ~70–80% vector RAG	95%+ via GraphRAG, every answer cited
Natural language	SQL or analyst queue required	Plain English for bankers and compliance
Entity resolution	Manual or not supported	Automatic — customers, households, properties
Audit trail	Core-only or partial lineage	Immutable, source-to-answer
Time to value	12–18+ months	4–8 weeks
Scope of change	Rip-and-replace or warehouse rebuild	Connects to the systems you already have
Security model	Application-layer only	Embedded in the data graph

In Production

Live at a community bank — 330,000+ customers unified.

Fluree helps us with Master Data Management by getting quality into and out of our information. The more quality we have within our data, the more we can help anticipate the needs of our customers.

Brent Wilke · Chief Data Officer, First Bank (Nasdaq: FBNC)

Customer case study

330,000+

Customers unified into AI-ready golden records

How a community bank built AI-ready golden records to power proactive customer outreach

Read the case study

Go Deeper

The core banking AI playbook.

Webinars, whitepapers, and practitioner articles on building AI-ready data foundations for community and regional banks.

From Defense to Offense — The Data Strategy Shift Redefining Financial Services

Webinar replay

Julia Bardmesser & Eliud Polanco

From Defense to Offense — The Data Strategy Shift Redefining Financial Services

Turning data from a compliance cost center into a revenue engine — playbooks from Citi, Deutsche Bank, and Voya Financial.

Watch replay

Why it matters

Why Your Enterprise AI Hallucinates — and the Science Behind Fixing It

The architecture choices that keep generative AI defensible in regulated banking environments.

Read the article

Banking playbook

AI-driven customer MDM for banks.

Download one-pager

Practitioner deep-dive

Improving Master Data Management With Supervised Machine Learning

How ML-assisted matching beats brittle string-rule MDM — and what to look for when you evaluate it.