FinoraAI

Governance that
proves itself.

Every data pipeline, quality rule, lineage link, privacy policy, and human decision gate in one connected system. Gaps and anomalies surface on their own.

Quality: 41,200 null records held at Gate 04
9
Modules
30
Named Gates
Decision Gates
6+
Tools Replaced
app.finora.ai/orchestrator/dashboard
Enterprise Data Governance Cockpit ✦ AI Advisor Active
Quality Index
99.4%
Lineage Mapped
100%
Gate Holds
1
Awaiting Human Gate Decisions
[M04 DQ] 41,200 null customer risk ratings held Sarah Jenkins
[M02 Masking] National ID tokens masked Privacy DPO
[M01 Reconcile] General ledger variance 0.00% Controller
Change · M04
Approve fix on 41,200 nulls in customer.address
Advisor confidence 0.94 · sandbox validated · downstream: IFRS 9 stage-2
GATE HOLD M04-04
Awaiting Quality Lead sign-off before committing to production.
✦ Open Gate View

The 9-Step Spine

Every module runs the same nine steps. Eight AI agents own them — a named person owns the fifth, and nothing moves until they decide.

01 / 09
Step 1

Detect

Data Profiler, Issue Detector

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AI automated

What Finora is

An AI-based data governance platform for banks, spanning nine modules from the moment data arrives to the moment someone is allowed to read it.

Finora watches your pipelines, scores the quality of every dataset, traces where each field came from and what depends on it, finds the personal data hiding in your columns, protects it, controls who can see it, checks it agrees with your reference systems, and records every decision along the way.

What makes it different is not the list. It is what happens when something is wrong. Finora does not raise a ticket and step back. It proposes a fix, tests that fix on a copy, and then stops — because no data changes until a named person has looked at what will change and said yes.

Thirty of those approval points sit across the nine modules. They are the connective tissue between them, not an add-on to any one.
M01
Data observability
M02
Data masking & tokenization
M03
Pipeline governance
M04
Data quality
M05
Lineage
M06
Catalog and metadata
M07
Access and entitlements
M08
Stewardship
M09
Reporting and audit

Why Finora

It's all here.

Pipelines, data quality, lineage graphs, masking vaults and human approvals on one single data model. Nine modules, AI in every one of them.

01

Governed remediation, not just governed data

Finora doesn't stop at telling you what's wrong. It governs what happens next.

Detection and repair live in one platform, under one approval model, on one audit trail. AI proposes. A sandbox shows what would happen. A named person approves, modifies or rejects. The safety layer checks the operation is permitted. Only then does anything get written.

02

Cross-Domain Decision Context

No change is approved before its full impact is on the screen.

Before a change happens, Finora brings together quality + lineage + privacy + ownership + policy + reconciliation context to determine the impact of that change — so the person deciding is looking at every domain it touches, not just the one that raised the alert.

03

One evidence chain, source to certified outcome

When the auditor asks why a number changed, the answer is already assembled.

Not reconstructed afterwards from seven systems. What happened, why, who decided, what they were shown, what changed, whether it worked, and who signed it off — held together as one record.

The Whole Platform. Organized by Solution.

Organized by the critical operational and regulatory outcomes you achieve, not by internal module codes. All nine modules share one unified data model and the 9-step spine.

CONTROL

Data Observability

One screen for the whole estate — pipeline run health, dataset freshness and volume, six-month trends, and continuous reconciliation against the reference system.
PROTECTION

Data Masking

Finds and classifies PII, then protects each field with format-preserving masking, tokenization or encryption — with coverage you can prove.
DATA

Pipeline Governance

Stop schema drift and automatically quarantine bad data at source.
DATA

Data Quality

Detect and fix quality issues before they spread with AI recommendations.
DATA

Data Lineage

See the full journey of your data and trace regulatory model impact before making changes.
DATA

Data Catalog

Discover, understand and govern your assets. A searchable inventory of all enterprise data assets.
PROTECTION

Access & Entitlements

Controls and certifies who can access which data, under what conditions across RBAC & ABAC workflows.
CONTROL

Data Stewardship

Route critical mutations to accountable stewards across 30 role-isolated decision gates.
CONTROL

Reporting & Audit

Provides real-time, role-specific visibility into the health, quality, compliance and stewardship of all governed data assets.

Mapped to the frameworks your regulator names.

Every module writes into one evidence chain, so alignment is something you can show rather than assert. Here is which module produces the evidence for what.

Risk data

BCBS 239

Risk data aggregation & reporting
Accuracy and integrity through source-to-target reconciliation, plus completeness, timeliness, adaptability, frequency and distribution — evidenced on every run.
Principles 12–14 cover supervisory review and are not addressable by a platform. We say so rather than claim them.
Data Observability · Data Quality · Reporting & Audit
Quality

DAMA-DMBOK

Six data quality dimensions
Completeness, consistency, validity, timeliness, uniqueness and accuracy — each scored live from the rules that actually cover it.
A dimension no rule covers is reported as uncovered, not quietly dropped from the score.
Data Quality · Data Observability
Governance

SBP BPRD Circular 05 / 2017

ETGRM — technology governance & risk
Every rule change carries a stated reason and a permanently attributed actor. Propose and approve are distinct grants, and self-approval is refused.
Append-only audit with before and after values; schema changes ship as numbered, additive migrations.
Data Stewardship · Access & Entitlements · Reporting & Audit
Privacy

GDPR Article 32

Security of processing
Personal data is found and classified on its own across languages, then protected by policy — format-preserving tokenisation, deterministic pseudonyms or encryption.
Every reveal of a protected value is audited, with the justification attached to the record.
Data Masking · Access & Entitlements
Payments

PCI DSS

Cardholder data protection
Card and payment fields are classified and protected at rest, with masked previews standing in for raw values everywhere a value is displayed.
Coverage is reported by field, environment and regulation rather than asserted once at go-live.
Data Masking · Data Quality
Security

SAMA CSF

Cyber security framework
Access is certified by role, attribute and purpose — and when access is refused, the platform names the rule that refused it.
Evidence packs are hash-chained and exportable for supervisory review.
Access & Entitlements · Data Masking · Reporting & Audit
On wording. Finora supports and evidences these frameworks — it does not certify an institution against them. Meeting a principle is a judgement made by people with the whole institution in view; a platform can only supply the evidence toward it: what ran, what it found, who decided, and what changed.

The 360° Linkage Model

Scattered across six tools. Connected in one. Not six products bolted together, but one record moving through six governed states in real-time.

KYC Risk Register4.2M records
Ten questions, one dataset, one place to look
1 of 5

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See your data architecture in it, not ours.

Demos are scoped to your regulatory stack. Pick a slot, name your frameworks, and we'll run the governed remediation live.

Book a live demo Schedule Architecture Briefing