TRUFLOW • BUILT BY BANKING PRACTITIONERS

TheNo-Touch

AIBackbone

forRegulatedBanks

Trusted data. Business-aware context. Governed control layer

Exam-Ready, Always

Regulatory Confidence, Built-In

Rejections, Minimized

What TruFlow is

TruFlow is the data trust platform purpose-built for empowering banking AI. With built-in finance domain intelligence, regulatory knowledge graphs, automated governance and many more features, it transforms how banks discover, trust and operationalize their data.

Trusted Data Foundation

Certifies source data for accuracy, lineage, and auditability before AI access.

Context-Aware Bank AI

Applies banking semantics and relationships so AI decisions are explainable.

Governed Data Products

Controls what data AI can use through approved, fit-for-purpose datasets.

Regulatory Posture Intelligence

Continuously evaluates outputs against regulatory rules and produces evidence.

What TruFlow does

The path from banking data to governed AI decisions

Data Sources

Cataloging

Traceability

Quality

Trust Score

Knowledge Graph

AI / Regulatory

Operating layers

Preparing data, context, and controls before AI decisions occur.

Certifies data reliability through validation, lineage tracking, and approval gates

Data QualityTrust ScoresCatalogObservability

Autonomous data quality

Continuous validation across all sources AI operates on reliable, accurate information.

Traceability & audit trail

Full lineage from source to AI output transparent, auditable records of every transformation.

Trust scores & certification

Every element gets a trust score. Certification gates ensure only validated data reaches AI.

Catalog & observability

One catalog for discovery, ownership, monitoring know what exists and its quality status.

Business Outcomes

Scale bank-wide AI with confidence, exam readiness, and lower regulatory cost

AI you can trust and explain

Grounded AI decisions with clear lineage you can defend to regulators.

Faster AI time-to-value

Deploy AI use cases faster without waiting on data validation and controls.

Fewer rejections, fewer fines

Catch issues before submission to prevent rejections and penalties.

Evidence when regulators ask

Produce audit evidence instantly instead of scrambling during reviews.

Where TruFlow Applies

Regulatory submissions without last-minute firefighting

Fewer rejections. Ready evidence. Faster inquiry response.

A regulated institution needed to stay audit-ready across transaction reporting, risk data, and swap reporting. End-to-end traceability, validation at submission, and a single audit trail were put in place across MiFID II/UK MiFIR, BCBS 239, Dodd-Frank/CFTC, and related regimes.

Regulatory submissions without last-minute firefighting

MiFID II / UK MiFIR - Transaction reporting

•UTI, ISIN, LEI, MIC validation at submission

•T+1 monitoring and exception tracking

•Best-execution audit trail and communication linkage

BCBS 239 - Risk data

•Ownership and accountability mapping

•End-to-end risk traceability and completeness checks

•Trust scores and ad-hoc report readiness

Dodd-Frank / CFTC - Swap reporting

•USI and LEI validation

•24-hour SLA monitoring

•Lifecycle traceability with product and entity hierarchy

SEBI - UPSI & insider trading

•Surveillance audit trail and beneficial ownership tracking

•Exchange reporting readiness and retention compliance

AML / KYC

•Customer traceability and KYC completeness tracking

•Sanctions screening coverage and false-positive controls

•SAR documentation readiness

DPDP / Data protection

•Personal data discovery and flow mapping

•Purpose linkage and breach impact assessment

•Data principal requests and retention compliance

Stop fines and rework - fix problems before they reach the regulator

Measurable drop in rejections and rework - trust scores instead of manual checks.

A bank faced rejections and rework from invalid identifiers and incomplete submissions. Pre-submission validation and certification gates meant only fit-for-purpose data reached reporting or AI.

Stop fines and rework - fix problems before they reach the regulator

•Agentic pre-trade and pre-submission validation : Dedicated validation agents run UTI, ISIN, LEI, MIC, USI checks, completeness and consistency gates applied automatically before data reaches downstream systems or reports

•Autonomous data quality, certification, and quality gates : Dedicated quality agents continuously monitor and score; only certified data feeds AI and reporting; early warning on drift without manual checks

•Governance and ownership : Clear accountability, certification workflows, and autonomous data quality SLAs so the bank knows what is fit for purpose

•Downstream failure prevention : Agentic monitoring and real-time alerts so quality and completeness gaps are fixed before they become rejections or fines

Prove it when it matters evidence for every inquiry

One source of truth for lineage - respond to inquiries with evidence.

An institution needed to prove best execution and answer inquiries with evidence. Order-to-report lineage and a single audit trail gave one source of truth; teams respond quickly and run root-cause analysis at source.

Prove it when it matters - evidence for every inquiry

•Order-to-report lineage : Dedicated lineage agents maintain complete traceability from order through execution to reporting; one source of truth for where data came from and how it was transformed

•Best execution and inquiry response : Audit trail to prove execution quality and respond to regulatory or internal inquiries with evidence

•Root cause analysis : When something breaks or a number is wrong, trace back to source and fix at the root

•Report-readiness and ad-hoc questions : Pre-mapped context so the bank can answer "which data answers which question" quickly and with evidence

One set of numbers everyone can trust

One set of numbers - reporting and AI use the same context.

Trading, risk, finance, and compliance had inconsistent definitions. A shared banking knowledge graph and glossary gave one view of products, entities, and which data answers which question.

One set of numbers everyone can trust

•Consistent definitions and taxonomy : One banking-grade glossary so trading, risk, finance, and compliance use the same terms and hierarchies

•Product and entity context : Product classification, entity hierarchy, and business context so data is interpretable without tribal knowledge

•Which data answers which question : Ownership and mapping to regulatory and business questions; audit-ready evidence for exams and inquiries

•Accurate reporting and analytics : Reports and dashboards that use the same context as AI, so numbers are consistent and explainable

AI you can rely on and explain to the board

Grounded, explainable AI - trust scores show when to rely or escalate.

A bank scaled AI without sacrificing explainability. Trusted data, a governed knowledge graph, and full traceability grounded outputs in certified data; users see where answers came from.

AI you can rely on - and explain to the board

•Grounded RAG and retrieval : AI answers backed by trusted, certified data and the banking knowledge graph, not just schema names

•Explainable and contextualized answers : Users see where the answer came from and what definitions and context were used

•Confidence and reliability : Trust scores and traceability so the bank knows when to rely on AI output and when to escalate

•Agent-ready data : APIs and data products that agentic workflows and downstream agents consume with full context, lineage, and autonomous data quality metadata for safe automation

Find the right data, know it’s approved - no more guesswork

Single source of truth for data products - only approved data to AI and reporting.

A large institution had data products scattered across teams with unclear ownership and no single view of supply and demand. Discovery and a banking knowledge graph mapped data products to underlying assets, dependent reports, and consuming users. Ownership and stewardship were clarified; trust scores and certification were applied at product level. The outcome: a single source of truth for what exists, who owns it, and how it’s used with only approved, fit-for-purpose data feeding AI and reporting.

Find the right data, know it’s approved - no more guesswork

•Identify and discover data products : Catalog and surface data products across the estate so teams can find what exists, who owns it, and how it's used

•Banking knowledge graph for data products : One graph that models data products, their definitions, relationships to assets and reports, and consumers

•Map to data assets, reports & users : Data products linked to underlying assets, dependent reports, and consuming users for one connected view of data supply and demand

•Ownership and accountability : Clear ownership and stewardship for each data product, with traceability to sources and downstream consumers

•Governance and quality at product level : Trust scores, certification, and quality metadata attached to data products so AI, reporting, and analytics consume only approved, fit-for-purpose data

What makes TruFlow Different

Not just a catalog. Not just lineage. Not just governance.

One unified, agentic backbone: trust and knowledge infrastructure so you scale AI with confidence, stay exam-ready, and avoid the cost of rework and fines. Dedicated agents for cataloging, autonomous data quality, traceability, and compliance - with Regulatory Posture Intelligence built on the same pillars, not a separate stack.

Prebuilt regulatory & compliance:

Regulatory Posture Intelligence is built in - not an add-on. Validation, monitoring, and proactive remediation so you get to compliance faster and stay audit-ready without building from scratch.

Built for the whole bank:

Autonomous data quality, complete traceability, and a governed banking knowledge graph - so AI is accurate and explainable, and compliance has the evidence it needs.

Trust score & autonomous data quality:

Quality agents continuously monitor and validate so AI runs on reliable data; pre-submission validation and alerts prevent rejections and downstream failures.

Banking knowledge graph:

Business context, definitions, and ownership so AI and reporting use one version of the truth - and you can answer "which data answers which question" for exams and inquiries.

Our Partners

Collaborating with industry leaders to deliver excellence.

Technology Partner Partner 1

Implementation Partner Partner 2

Scale AI with confidence grounded, accurate, regulator-ready

One backbone for trusted data, compliance, and Banking AI. Talk to us.

Request demo How it works