Vyntra was formed in June 2025 through the union of two established fintechs: Intix, a Belgian fintech recognised as a leader in Transaction Observability, and NetGuardians, a Swiss fintech specialising in AI-driven fraud prevention and AML monitoring. Together, the combined entity operates under the Vyntra brand and is positioned in the domain of Transaction Intelligence.

Vyntra employs approximately 150 people across more than 25 nationalities, with offices in Belgium, Switzerland, the United Kingdom, Singapore, Poland, and Kenya. We serve over 130 financial institutions across 60+ countries, including four of the top ten European banks. Vyntra is backed by Summa Equity, a Stockholm-based private equity firm focused on purpose-driven, thematic investments.

Transaction Intelligence is the domain that sits at the intersection of Transaction Observability and Financial Crime Prevention. It means turning raw, fragmented financial messages into structured, real-time intelligence, so that financial institutions can see where every transaction is, understand what happened to it, detect anomalies or criminal behaviour, and act before issues reach customers or regulators.

Our sweet spot is financial institutions that process payments in-house across multiple legacy systems and payment rails. This includes Tier 1 and Tier 2 banks, clearing houses, central banks, payment processors, and market infrastructure providers. Ideal customers typically process 500,000 or more transactions per day, operate across multiple payment formats and geographies, and face regulatory pressures around operational resilience, AML, or ISO 20022 migration.

Financial institutions typically face fragmented transaction data spread across multiple legacy systems, formats, and siloes. This leads to slow and costly investigation processes, limited visibility into payment flow health, reactive rather than proactive operations, and difficulty meeting regulatory and audit obligations. The Vyntra platform addresses all of these by providing a single window of access to all transaction data, real-time flow monitoring, proactive alerting, and audit-ready reporting.

The platform is modular and delivered as three cumulative solutions:

• The Vyntra Message Warehouse solution centralises and indexes financial messages from multiple sources and formats into a single, searchable repository with long-term retention (up to 15 years or more) and high-speed retrieval. Ideal for audit, compliance, and investigation use cases.
• The Vyntra Transaction Analytics solution builds on the Message Warehouse by extracting transaction events from the financial messages and correlating those individual transaction events into virtual transaction representations. It enables deep transaction analytics, KYT (Know Your Transaction) enrichment, and structured transaction-level reporting.
• The Vyntra Transaction Track & Trace solution adds real-time, end-to-end tracking of transactions across the full internal and external payment lifecycle. It includes Business Activity Monitoring (BAM), value-at-risk calculations, SLA management, and proactive alerting on bottlenecks or anomalies.

Customers may start with the Message Warehouse solution and expand to the Transaction Analytics or Transaction Track & Trace solution as their needs evolve.

IT monitoring tools such as Datadog or Splunk are designed to monitor the technical health of systems, whether servers are running, processes are active, and infrastructure is available. The Vyntra platform operates at the business layer, monitoring the health of payment flows themselves: which transactions are stuck, what value is at risk, whether SLAs are being breached, and whether anomalies are occurring within the transaction data. The Vyntra platform is therefore complementary to traditional IT monitoring, not competing.

The platform is format-agnostic and supports any financial message format.

Out-of-the-box support includes SWIFT MT, ISO 20022 (all versions and message types), SIC, Swift SN, Swift TRCK (gpi), and Swift standard gateway envelopes (SAA and AMH). Optional industry packages are available for European payment systems (SEPA, TARGET2/T2, TIPS, RT1, EURO1, STEP2), UK payment systems (CHAPS, BACS, FPS), US payment systems (FedWire, CHIPS, FedNow, RTP, FedACH), Israeli payment systems (Zahav RTGS) and CIPS (Cross-border Interbank Payment System). Proprietary and institution-specific formats can also be onboarded through the Format Factory using configuration tooling, without any coding.

Vyntra maintains all standard format dictionaries, including Swift MT and ISO 20022, and delivers on a yearly basis updated packages to all customers as part of the standard support agreement. Customers do not need to manage dictionary updates themselves. This significantly reduces the total cost of ownership compared with in-house solutions or tools that require manual standards maintenance.

The competitive landscape varies by use case. For message warehousing, the most common incumbent is Swift Alliance Warehouse, which is Swift-only and lacks business-level analytics.

For transaction analytics and transaction track and trace, some institutions have attempted to build in-house solutions on top of data platforms or BI tools, which typically lack the payment-specific data model, format support, and real-time capabilities of the Vyntra platform. Some process monitoring solutions are also considered, but these are general-purpose and do not come with payment industry knowledge, pre-packaged format support, or the real-time ingestion capabilities needed.

Vyntra’s key differentiator across all these alternatives is its exclusive focus on financial services payments, its format-agnostic architecture, and its business-layer observability model.

The platform is sufficiently generic to support any type of financial message or transaction data, not just payments. It has been used in production for securities transactions, including implementations for central securities depositories. That said, payments are the primary focus and the area where the platform delivers the most out-of-the-box value, with pre-packaged format dictionaries, correlation logic, and business views tailored specifically to payment workflows.

Beyond standard payment messages, the platform supports several additional data ingestion patterns. Non-standard or proprietary message formats can be onboarded via the Format Factory. KYT (Know Your Transaction) enrichment data, such as AML screening results, fraud scores, or sanction check outcomes, can be injected directly into the transaction record. It is also possible to load non-transactional reference data such as customer or account information into the platform to enrich transaction views, though this requires additional configuration. In short, if a financial institution can deliver the data in any format via a supported interface, the platform can capture, index, and correlate it.

The Format Factory is the capability within the platform that manages the definition, ingestion, and normalisation of financial message formats. It includes pre-configured dictionaries for all major industry standards and provides tooling to define proprietary formats using a declarative, configuration-driven approach, supporting XML, JSON, fixed-length, and delimited formats. No coding is required to add a new proprietary format. The Format Factory ensures that all messages, regardless of format, are mapped to a uniform index model, enabling consistent search and analytics.

Business Activity Monitoring (BAM) is a capability within the Vyntra Transaction Track & Trace solution that provides a real-time, business-layer view of all transaction flows. Rather than showing whether systems are running, BAM shows whether payments are flowing correctly, detecting bottlenecks, broken flows, SLA breaches, and anomalies as they occur. BAM uses a visual “Tube Map” interface that displays the health of each step in the payment lifecycle using colour-coded indicators, with drill-down capabilities to individual transactions. It operates in a non-intrusive way, outside the critical payment flow.

The platform supports rule-based alerting on any indexed attribute or statistical pattern. Alerts can be configured for message anomalies, volume or value deviations, SLA threshold breaches, and processing delays.

A threshold matrix system allows multiple threshold values to be defined on multiple dimensions (e.g. currency, customer segment, time window…). Thresholds can be automatically calculated and updated based on historical transaction patterns, adapting dynamically to business cycles, holiday calendars, and seasonal variation. Alerts are delivered in-application, by email, or via API integration with external case management systems.

Alerts are configured directly in the platform’s user interface using a combination of rule definitions and threshold matrices, i.e. no coding or deployment is required. However, alert configuration does require a working knowledge of the platform’s data model and scripting syntax, so it is typically performed by a designated power user or super user rather than a general business user.

Vyntra’s Professional Services team configures an initial set of alerts as part of the implementation, and power users are trained to create and modify alerts independently thereafter.

Not currently. Dynamic alerting is based on configurable statistical models rather than AI or machine learning. Threshold values can be calculated automatically from historical transaction data, adapting to business cycles, seasonal patterns, and time-of-day variation, but the underlying logic is deterministic and transparent, which makes it predictable and auditable. This is a deliberate design choice: statistical thresholds are easier to configure, explain, and validate in a regulated environment than black-box AI models. AI-based alerting capabilities are on the product roadmap (see the question on AI below).

Not necessarily. Threshold matrices can be set manually when the platform first goes live, based on known business norms or estimates. Dynamic auto-population of thresholds from historical data can be activated once sufficient live data has accumulated, typically after a few weeks in production. It is also possible to pre-load historical transaction data to seed the threshold calculation, though care should be taken to ensure that historical data is still representative of current business volumes and patterns.

Yes. Because the platform captures events at multiple interception points across the payment lifecycle, it can detect payments that appear mid-flow without the expected preceding events, a strong indicator of injection. Similarly, payments that enter the flow but never produce downstream events can be flagged as potentially suppressed. Alerts can be configured to trigger when a transaction is missing expected lifecycle events, enabling compliance and operations teams to investigate rapidly. This capability is particularly relevant for insider fraud detection and payment integrity surveillance.

At each interception point, the platform captures a timestamped event for the transaction. The Transaction Track & Trace solution calculates the elapsed time between consecutive events and compares it against configurable SLA thresholds. If a transaction remains in a given status for longer than expected, i.e. no subsequent event is received within the defined window, an alert is triggered. The more interception points that are configured, the more granular and precise the detection: the platform can identify not just that a payment is stuck, but exactly where in the flow it has stopped progressing.

Yes, with considerable flexibility. Any attribute indexed in the platform (including amounts, currencies, counterparties, remittance information, processing times, statuses, and custom fields) can be queried individually or in combination. Beyond individual field searches, the platform supports statistical and behavioural pattern detection through its dynamic alerting framework, enabling detection of anomalies such as abnormally high or low volumes, unusual value distributions, atypical counterparty activity, or out-of-pattern processing times. For more advanced search scenarios, the platform’s scripting language (Daisy) supports complex conditional expressions, wildcard matching, fuzzy search, and dynamic date ranges.

The platform is a read-only analytical system and does not participate in the operational payment flow. It will never modify, block, or cancel a payment autonomously, this is a deliberate architectural and liability boundary. However, alerts can be distributed via API to external operational systems (such as case management platforms, workflow engines, or notification gateways), which can then trigger downstream actions such as stopping a payment, opening a case, or notifying a customer. The decision and action always rest with the operational system; Vyntra provides the intelligence and the signal.

The Correlation Engine is the core processing component of the Vyntra Transaction Search & Analytics and Vyntra Transaction Track & Trace solutions. It reconstructs complete transactions from individual transaction events captured at multiple interception points across the payment lifecycle. The engine supports complex correlation scenarios including out-of-order event delivery, multiple and composite correlation identifiers, and single events that affect multiple transactions. When no match is found for an incoming event, a new transaction is dynamically created. When new information links two previously separate transactions, a transaction merger is executed automatically.

This is a common scenario in complex payment environments, and the Correlation Engine is specifically designed to handle it. The engine supports multiple correlation identifiers per transaction, meaning a transaction can be matched and assembled from events carrying different reference fields at different points in the lifecycle. Composite identifiers (where a combination of fields forms the unique key) are also supported, as is out-of-order event delivery, where events arrive in a sequence different from their actual occurrence order. If two previously separate transactions are later proven to be the same transaction via a new event carrying multiple identifiers, a transaction merger is performed automatically.

The platform does not have a native connector for intercepting live REST API traffic between microservices. The recommended approaches are: configuring the sending microservice to also push a copy of the event to the platform via an API call or a message queue (e.g. Kafka); connecting to the database of the sending microservice via JDBC or a database change capture mechanism (e.g. Oracle GoldenGate); or routing events through an existing middleware layer that can be tapped non-intrusively. The most appropriate approach depends on the customer’s architecture and the feasibility of each option is assessed during the integration design phase.

The Tube Map is a visual representation of all payment flows within an organisation, modelled in a style analogous to a transit map. Each node represents a system or processing step; each connection represents a payment flow between systems. The Tube Map updates in near real time and uses colour-coded status indicators to surface delays, bottlenecks, broken flows, or threshold breaches. Users can click through to progressively more detailed views and drill down to individual transactions. Multiple Tube Maps can be configured and linked for hierarchical navigation from high-level overview to detailed flow.

Yes. The Transaction Search & Analytics and Transaction Track & Trace solutions support the injection of external KYT enrichment data , including AML screening results, sanction check outcomes, fraud scores, gpi tracker updates, and other compliance signals, directly into the transaction record. This creates a 360-degree view of each transaction, enabling compliance and investigative teams to see all relevant context in a single window without switching between systems. The platform does not replace AML or fraud engines, but it complements them by providing the observability and audit trail layer.

The platform includes a built-in, self-service dashboarding and reporting framework. Dashboards are composed of configurable portlets that can display data in tabular, chart, KPI, or map formats. A default set of dashboards is delivered out of the box covering message analysis, counterparty analysis, SWIFT insights, and system event monitoring. Users with appropriate access can create, share, and export custom dashboards and reports in PDF, CSV, or XML formats without IT involvement. All dashboards respect the user’s data profile, ensuring that data segregation is applied automatically.

Yes. The platform exposes a full RESTful Platform API documented via OpenAPI (Swagger), enabling external systems to retrieve transaction data, trigger report exports, and execute administrative operations. The API uses token-based authentication and enforces the same data segregation rules as the user interface. It is versioned, with backward compatibility maintained within major release cycles. Common integration use cases include feeding compliance engines, populating customer portals, driving automated regulatory reporting, and integration with case management systems.

The platform operates in near real time. End-to-end latency from message receipt to availability in the index is typically in the range of a few seconds, depending on integration method and infrastructure setup. For interactive user queries, search responses across billions of records are returned in sub-second to low-single-digit second timeframes under normal operating conditions. Report generation time varies depending on result size, query complexity, and whether raw data retrieval is required.

The platform supports long-term retention of both indexed data and raw message data, with no technical upper limit on the amount stored. Many customers retain 10 to 15 years of message history. Retention periods are configured per data type using purge labels and lifecycle start dates, and purging is handled automatically by the platform’s built-in purging mechanism. Indexed and raw data can be tiered across hot and warm storage layers to optimise cost while maintaining accessibility.

There is no fixed performance SLA for search and reporting, as performance depends on multiple factors including infrastructure sizing, index size, query complexity, result set size, and whether raw data retrieval is involved. Vyntra’s Professional Services team works with each customer to size infrastructure appropriately for their expected query patterns and volumes. Customers with properly sized infrastructure consistently experience near real-time search responses for typical operational queries.

The platform processes large files, including bulk payment files such as CAMT.053 files of up to 2GB, using streaming, meaning the file does not need to be loaded into memory in its entirety. A single-type file is stored as one message in raw data storage and indexed accordingly. The Transaction Search & Analytics and Transaction Track & Trace solutions then extract individual transaction events from the file. Files containing multiple message types are debulked into individual messages before processing. There is no file size limit for ingestion.

Yes. Because the platform is always deployed on infrastructure owned and managed by the customer, institutions have full control over where their data is physically stored. The platform supports geographically isolated deployments, enabling institutions to ensure that data for a specific entity or jurisdiction remains within a defined physical location. This is particularly relevant for customers operating across multiple countries with differing data residency regulations.

Prerequisites differ by deployment model:

• For a traditional on-premise deployment (bare-metal or virtualised), the platform requires Red Hat Enterprise Linux (RHEL) 8 or higher, Java 17 (JDK 17), PostgreSQL 9.6+ or Oracle 12c+ for the configuration database, and Elasticsearch 8.13 or higher for the index server. Windows installations are not supported.
• For a cloud or container-based deployment, the platform requires Kubernetes 1.25 or higher (or OpenShift 4.12 or higher), Helm 3.2 or higher, and a managed PostgreSQL 9.6+ or Oracle 12c+ database. The Kubernetes cluster must have outbound connectivity to Vyntra’s Docker image repository (hosted on Red Hat Quay.io) and Helm repository. Docker images are pre-packaged and maintained by Vyntra. Managed Kubernetes services such as Amazon EKS, Azure AKS, or Google GKE are all supported. AWS OpenSearch is not supported as a substitute for Elasticsearch.

In both models, Vyntra’s Professional Services team provides detailed sizing guidance based on transaction volumes, retention requirements, concurrency profile, and high-availability architecture. Infrastructure sizing is treated as a collaborative exercise, not a one-size-fits-all specification.

The dual storage approach (i.e. indexed data in Elasticsearch alongside raw data in file storage) is deliberate. Elasticsearch provides exceptional performance for structured search and aggregation across billions of records. However, it is not designed to store large binary objects or support the partial-stream retrieval required for raw message visualisation. The proprietary raw data layer stores every original message in its unaltered form, accessible within milliseconds, and linked to the corresponding Elasticsearch index entry. This design ensures both high-speed analytics and complete, audit-grade access to original message content.

The platform implements two tiers of storage for both indexed and raw data. The hot tier holds recently ingested data that is accessed frequently, stored on high-performance file storage. The warm tier holds older data that is accessed less frequently, stored on lower-cost options such as NFS disks or object storage (including AWS S3, Azure Blob Storage, and Google Cloud Storage). The transition between tiers is managed automatically by the platform. Both tiers are accessible in near real time from the user interface; the warm tier may have a slightly longer retrieval time for raw data in report generation.

Yes. The platform uses the open-source version of Elasticsearch for indexing, PostgreSQL or Oracle for configuration data, Docker for containerisation, Kubernetes or OpenShift for container orchestration, and Helm for infrastructure-as-code deployment. The application layer is built entirely in Java. Vyntra does not rely on Elasticsearch enterprise features, meaning customers are not required to purchase commercial Elasticsearch licences. All platform APIs are documented using OpenAPI (Swagger) standards.

Vyntra offers three options for Elasticsearch provisioning.

• First, Elasticsearch can be delivered as an embedded container alongside the Vyntra application stack. In this case the free, open-source version is used.
• Second, the customer can provide their own Elasticsearch instance, either self-managed or as a managed service (e.g. Elastic Cloud on AWS).
• Third, a fully managed Elasticsearch service from a cloud provider can be used.

In all cases, Vyntra uses only standard open-source Elasticsearch features and does not require the enterprise edition. Note that AWS OpenSearch (a fork of Elasticsearch) is not supported, as divergence between the two projects cannot be guaranteed over time.

The platform supports multi-tenancy, enabling multiple institutions or business units to be hosted on a single platform instance with complete data isolation. Data segregation is enforced at three configurable levels (i.e. entity, group, and system) and applied automatically to all search, reporting, dashboard, and API interactions based on each user’s data profile. Configuration objects such as reports and dashboards can be shared within communities of users, with data access always governed independently by the user’s data profile. For very large or high-volume tenants, separate Elasticsearch clusters may be recommended to prevent performance interference.

Each user is assigned two independent security profiles: a function profile that defines which features and capabilities the user can access, and a data profile that defines which transaction data the user can see. The data profile is enforced at three levels (i.e. entity, group, and system) and applied consistently across all channels: the user interface, reports, dashboards, and the API. This ensures that users only ever see the data they are authorised to access, regardless of how they interact with the platform.

Raw data is encrypted at the application layer using a two-key mechanism: an inner key encrypts the data, and an outer key, supplied by the customer’s key store, encrypts the inner key. Key rotation is supported. Elasticsearch index data can be encrypted at rest via the underlying infrastructure (supported in managed Elasticsearch environments). All communication between platform components uses TLS encryption. In cloud deployments, TLS is configured automatically; in traditional on-premise deployments, it requires customer configuration.

No.

The Vyntra platform is always deployed on infrastructure owned and managed by the customer, whether on-premise or in the customer’s own cloud account. Vyntra does not operate a SaaS model and does not have access to customer transaction data. This ensures full data sovereignty and compliance with local regulatory and legal data residency requirements.

Yes. Vyntra performs regular penetration tests on the platform to identify and address security vulnerabilities. Third-party software components embedded in the platform, including Tomcat, are fully managed by Vyntra and updated regularly to address known security vulnerabilities. In the event of a critical security vulnerability in an underlying component, Vyntra provides a patch on all supported platform versions as a priority. Vyntra is also in the process of obtaining ISO 27001 certification.

Data segregation in the platform supports multi-entity and multi-country deployments natively. Each message or transaction record carries entity, group, and system tags. Users are assigned a data profile that defines exactly which combinations of these tags they can access. This allows, for example, a user in one country or business unit to be restricted to only their own entity’s data, while a central compliance team may have cross-entity visibility. Externalisation of data profiles to LDAP or Active Directory group structures is also supported.

A clear distinction is made between configuration objects and data. Reports, queries, and dashboards are configuration objects owned by the user who created them. The owner can share a configuration object with a defined community of users, who can then execute it. However, data access is always governed independently by each user’s data profile, meaning two users executing the same report will see different results if their data profiles differ. It is not possible to share the output of a report, only the report definition itself. One controlled exception exists for aggregate dashboards: the owner may optionally configure a dashboard to execute using the owner’s data profile, allowing other users to see the same aggregated results. However, any drill-down from that dashboard reverts immediately to the individual user’s own data profile.

Yes. OpenShift, Red Hat’s packaged distribution of Kubernetes, is fully supported. It is treated equivalently to a standard Kubernetes deployment, and Vyntra’s Helm charts can be used for installation and configuration on OpenShift 4.12 or higher. Several Vyntra customers run the platform on OpenShift in production.

In normal operation, the platform manages Elasticsearch automatically, including index creation, partitioning, cluster health monitoring, and snapshot triggering, meaning day-to-day Elasticsearch administration is minimal. That said, for complex situations such as performance troubleshooting, cluster rebalancing, or index-level investigations, it is advisable for the customer’s engineering team to have working knowledge of Elasticsearch. This is one of the reasons Vyntra migrated from Apache Solr to Elasticsearch: most financial institutions already have Elasticsearch expertise in-house. Using a managed Elasticsearch service (e.g. Elastic Cloud) can significantly reduce the operational burden further.

No. Vyntra does not offer a SaaS model. The platform is always deployed on the customer’s own infrastructure, ensuring full data sovereignty and compliance with institutional and regulatory data governance requirements. This is a deliberate architectural choice reflecting the sensitivity of financial transaction data.

A cloud deployment requires Kubernetes 1.25 or higher (or OpenShift 4.12 or higher), Helm 3.2 or higher, and a managed PostgreSQL 9.6+ or Oracle 12c+ database. Kubernetes can be deployed as a managed service (e.g. Amazon EKS, Azure AKS, or Google GKE) or on customer-provisioned instances. The Kubernetes cluster must have outbound connectivity to Vyntra’s Docker and Helm repositories. AWS OpenSearch is not supported; standard Elasticsearch (open-source or managed via Elastic Cloud) is required.

The recommended high-availability setup uses a stretched Kubernetes cluster across three data centres or availability zones in the same region, with low latency between them. At least two zones host active Elasticsearch data nodes; the third hosts a smaller master node for cluster orchestration. The Vyntra application runs as a single active pod, with Kubernetes automatically rescheduling to another zone in the event of a zone failure.

For disaster recovery across geographically distant regions, a Dual Street configuration is supported: two independent clusters fed in parallel from the same data feed, with the secondary cluster in read-only standby and near-real-time configuration synchronisation enabling rapid failover.

The platform exposes all three observability pillars: logs are printed to standard output in structured JSON format and can be collected by standard enterprise log aggregation tools; metrics are exposed at a Prometheus endpoint and can be collected by customer monitoring infrastructure with Grafana dashboards provided by Vyntra; and traces are supported via OpenTelemetry and OpenMetrics. Customers can integrate these signals into their existing monitoring infrastructure (e.g. Dynatrace, Datadog, Grafana).

The platform includes a built-in online backup mechanism that captures a consistent snapshot of all persisted data, i.e. the Elasticsearch index, raw data storage, and configuration database, without interrupting end-user access. Users can continue searching, reporting, and viewing dashboards during a backup. The backup is stored in the warm raw data storage layer. Restore procedures are documented and supported by Vyntra. Customers are responsible for the underlying storage infrastructure and backup scheduling.

Vyntra supports the current major version and the two preceding major versions. For example, once version 5.0 is released, supported versions are 5.x, 4.x, and 3.x. Security patches for critical vulnerabilities are provided on all supported versions. Customers who require support for older versions beyond this policy can arrange extended support commercially.

Vyntra monitors and tracks all relevant industry standard releases and delivers updated format dictionaries to all customers as part of the standard support agreement, typically by end of August for the annual SWIFT release cycle. Customers do not need to manage dictionary updates themselves; the update is packaged by Vyntra and applied as part of the standard maintenance process. For formats specific to particular market infrastructures (e.g. domestic payment systems), updates are handled via the relevant industry package.

All incidents are reported via the Vyntra Service Desk, accessible online, by email, or by telephone. Incidents are logged, tracked, prioritised, and managed through to resolution. Because the Vyntra platform is an analytical system operating outside the critical payment flow, it is typically classified as a non-critical system from an operational standpoint, which informs SLA definitions. Enhanced support models, including extended hours, dedicated Customer Success Managers, development support credits, and defined response and resolution targets, are available under commercial support agreements.

Changes can be requested through the project manager during an active implementation or via the Service Desk during the support phase. Once the scope of a change is agreed, Vyntra provides a financial proposal and Statement of Work before proceeding. Changes to the Vyntra product itself follow the release cycle. Customer-specific configuration changes can often be delivered more rapidly, particularly where SDK-based customisation allows the customer’s own team to implement changes directly.

The Software Development Kit (SDK) provides documented Java extension points that allow customers to extend and customise the platform, creating custom enrichments, correlation logic, alerting rules, and interface adapters, without modifying the core product codebase. Extensions developed via the SDK are isolated from the product layer and remain compatible with future platform upgrades, protecting the customer’s investment in customisation. This gives customers the flexibility of a bespoke solution with the maintainability and upgrade path of a packaged platform.

Third-party components embedded in the platform, including Tomcat and other runtime dependencies, are fully managed by Vyntra as part of the product lifecycle. Vyntra monitors component versions and releases updates regularly to stay current with security patches. These updates are included in the standard quarterly release cycle. In the event of a critical security vulnerability, Vyntra provides an out-of-cycle patch on all currently supported platform versions as a priority. Customers are encouraged to follow the Vyntra release cycle closely to benefit from the latest security updates. For Elasticsearch, security maintenance depends on the provisioning model chosen by the customer (embedded, self-managed, or cloud-managed).

Yes. Vyntra has significant AI and machine learning expertise within the group, particularly through the heritage of NetGuardians in AI-driven financial crime detection. AI-based capabilities, including more sophisticated anomaly detection and pattern recognition, are on the product roadmap, with design and analysis work ongoing. Customers with specific AI use cases are encouraged to share their requirements directly with Vyntra, as commercial demand can influence prioritisation. The current dynamic alerting capability, while statistics-based rather than AI-driven, is already highly configurable and performs well in typical Gaussian transaction distributions.

Implementation effort is primarily driven by four factors: the number of payment flows to be covered, the number and complexity of interception points within each flow, the diversity and complexity of the message formats involved, and the correlation logic required to link events from different systems into coherent transactions. A simple Transaction Search & Analytics implementation covering a well-defined SWIFT flow can be completed in under three months. A full Transaction Track & Trace implementation across multiple flows and proprietary formats will typically require six to nine months for an initial phase, with further phases extending over time as scope grows. Vyntra typically contributes around 80% of the total implementation effort; the customer provides approximately 20%, covering infrastructure provisioning, requirements definition, format specifications, integration enablement, and testing.

A typical Vyntra implementation encompasses project management, requirements analysis and design, platform configuration and customisation, integration setup, testing, deployment, go-live support, and an initial period of after-care.

Vyntra’s Professional Services team operates primarily on a fixed-price basis, with scope agreed upfront via a Project Definition Study and formalised in a Statement of Work. The team provides expertise in both financial messaging standards and the Vyntra platform, reducing the learning curve for the customer’s own team.

Customer involvement is essential for: defining business requirements and use case scenarios; providing specifications and sample data for any proprietary message formats; setting up and provisioning the target infrastructure; enabling integration with
the relevant payment systems and middleware; and participating in testing and acceptance activities.

Vyntra typically contributes around 80% of the overall implementation effort, with the customer providing approximately 20% (split roughly between infrastructure and requirements/testing).

Vyntra’s integration approach is light-touch and non-intrusive by design. The platform connects to existing middleware and messaging infrastructure to receive a copy of messages already in transit, without modifying any application in the payment flow and without impacting processing performance or stability. Supported integration protocols include MQ, Kafka, Solace, file-based interfaces, JDBC/SQL, and REST API. Both push and pull models are supported. Vyntra’s Professional Services team brings extensive experience in designing integration approaches that minimise footprint and risk.

Yes. Vyntra supports historical data load as part of the initial implementation. For Swift messages, tooling is provided to ingest historical data from standard SWIFT archive formats (e.g. MEAR files from SAA and SDAR files from Eastnets).

For other formats, historical data can be loaded via the standard file-based interface. Historical data interfaces can run in parallel with real-time feeds and can be directed to separate storage partitions if required. The same format dictionaries used for live processing apply to historical data, including support for older message versions.

Yes. Vyntra typically proposes a paid MVP rather than a traditional proof of concept. An MVP is scoped to a defined set of payment flows and interception points, is deployed into a production-ready environment, and delivers genuine business value from day one. This approach provides a more meaningful evaluation than a lab-based proof of concept and ensures that the initial investment is not lost. A “try and buy” licensing model can be arranged commercially. Vyntra has also invested in establishing a demonstration environment for initial evaluations.

The subscription includes the right to use the licensed solution modules, access to the standard support agreement (including format dictionary updates for SWIFT and ISO 20022), and access to the Vyntra Service Desk. Enhanced support models, including a dedicated Customer Success Manager, extended support hours, and development support credits, are available as separate options.

Yes. Implementation and professional services are charged separately from the subscription, as a one-time service fee. The scope, deliverables, and cost of the implementation are agreed upfront in a Statement of Work. Vyntra’s Professional Services team typically works on a fixed-price basis for well-scoped implementations.

Yes. The platform’s modular architecture is designed to support progressive adoption. A customer may begin with the Transaction Search & Analytics solution and subsequently expand to the Transaction Track & Trace solution as additional use cases are identified. Expanding the scope of an existing implementation is significantly less costly than a standalone new project, as much of the integration and infrastructure work is already in place.

The subscription licence is primarily volume-based. Deploying across multiple environments (e.g. production, test, and disaster recovery) or multiple entities within the same institution is typically covered within the same licence framework, with volume being the primary commercial driver. Customers should discuss their specific multi-environment and multi-entity requirements during the commercial negotiation to ensure the agreement accurately reflects their deployment model.