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Vavoris Connect™ — Enterprise Intelligence Fabric

Connect to what you already have.
Nothing to migrate.

Vavoris reads signals from every operational system you already use. Your data stays where it lives. The decision loop starts from day one — no infrastructure changes, no data warehouse consolidation, no rip-and-replace.

0
Data migrations required
Day 1
First signals flowing into the decision loop
Any
Cloud, on-premise, or hybrid — all supported

Works with your existing stack

Vavoris connects to event streams, data platforms, cloud storage, operational databases, and file systems you already operate. No new infrastructure required to start reading signals.

Real-time and historical signals

Vavoris reads live operational events and historical data simultaneously — so the decision context includes what is happening now and what has happened before, in a single unified view.

Exactly-once, fault-tolerant

Every connection is designed for production reliability. On any failure — network, hardware, or planned restart — signal reading resumes from exactly where it left off. No duplicates, no gaps.

Event Streams
Real-time operational events flowing from your existing messaging infrastructure
KA
Apache Kafka
High-throughput real-time event streams. Commonly used for PMS events, order feeds, claim activity, and patient ADT.
KI
AWS Kinesis
AWS-native real-time data streams. Supports high-volume operational event pipelines already running in AWS environments.
PL
Apache Pulsar
Multi-tenant enterprise messaging. Typically used in large-scale, multi-cloud enterprise event buses.
RD
Redis Streams
Low-latency operational event feeds already flowing through Redis. Common in hospitality and e-commerce real-time stacks.
PS
GCP Pub/Sub
GCP-native message delivery. Connects directly to event pipelines running on Google Cloud infrastructure.
Data Platforms & Warehouses
Analytical and operational data platforms where your business history lives
BQ
Google BigQuery
Reads signals directly from BigQuery tables. Supports both incremental real-time polling and full historical snapshots for baseline context.
SF
Snowflake
Incremental reads from Snowflake tables via watermark queries. Access your data warehouse without copying data out of it.
CA
Apache Cassandra
High-scale time-series and operational data. Commonly used for IoT, telemetry, and large-volume historical records.
MG
MongoDB
Reads from MongoDB collections via change streams or incremental polling. Used for operational records in hospitality, insurance, and healthcare.
Cloud Storage
Object storage and file systems where batch exports, data lake files, and historical records live
S3
Amazon S3
Reads new and updated objects from S3 buckets. Supports CSV, JSON, and Parquet — useful for batch analytics exports and data lake ingestion.
AZ
Azure Blob Storage
Connects to Azure Blob containers. Supports managed identity and SAS token authentication for enterprise Azure deployments.
GC
Google Cloud Storage
Reads from GCS buckets in the same pattern as S3 — useful for GCP-native data pipeline outputs and historical file exports.
Operational Databases
The systems of record where your business runs — CRM, ERP, PMS, EHR, claims management
PG
PostgreSQL
Incremental table reads via watermark or change data capture. Used for CRM, case management, and most modern SaaS backends.
MY
MySQL
Standard relational database reads via incremental polling. Common in hospitality PMS systems and mid-market ERP platforms.
OR
Oracle Database
Enterprise-grade incremental reads from Oracle tables. Used for legacy ERP, insurance policy systems, and large healthcare EMRs.
SS
SQL Server
Incremental reads from Microsoft SQL Server. Common in Microsoft-stack enterprise environments and financial services operations.
File Feeds & Historical Data
Batch exports, secure file drops, and API-based historical data sources
FT
Secure File Transfer (SFTP)
Polls secure file servers on configurable schedules. Common for insurance batch feeds, bank extracts, and healthcare eligibility files.
API
REST APIs & Webhooks
Polls REST endpoints or receives webhook events from third-party systems. Supports API key, Bearer token, and OAuth authentication.
FS
Local & Network File Systems
Watches directories for new files. Processes CSV, JSON, and delimited formats from legacy system file drops and network shared drives.

How signal reading works.

Vavoris reads signals from your existing systems and streams them into the decision loop in real time. Your data never leaves its source system — Vavoris reads it, not copies it.

01

Connect your systems

Authenticate to each source — typically read-only credentials. Vavoris reads from your systems; it does not write to them or move your data.

02

Map your fields

Translate your system's field names to the Vavoris signal schema. Field mappings can be updated at runtime without restarting connections.

03

Signals flow in real time

Once connected, signals stream continuously into the decision loop. Vavoris tracks read position — on any failure, reading resumes exactly where it left off.

04

Decisions follow the signals

Each signal is scored against your decision context, passed through governance, and delivered as a recommendation — while the outcome is measured and fed back.

What stays where it is.

Vavoris is not a data consolidation platform. You do not migrate to Vavoris. Vavoris comes to your data.

Your data warehouse stays your data warehouse

Vavoris reads from Snowflake, BigQuery, and other warehouses without requiring a copy. Your existing analytics and BI tools continue working against the same data, unchanged.

Your operational systems are read-only

Vavoris reads signals from your CRM, EHR, PMS, and claims system. It does not write to them. Your source systems are never modified by the integration.

Your cloud vendor relationship is unchanged

Vavoris works across AWS, Azure, GCP, and on-premise. It is not a competing cloud platform. Your existing cloud commitments and contracts are unaffected.

Your existing AI tools keep working

Vavoris sits above your existing systems — including any AI tools you already use. It governs decisions, measures outcomes, and builds judgment on top of what you have, not instead of it.

Tell us the outcome you want to improve.

We start with the problem — claim leakage, readmission rate, guest recovery, approval consistency — and determine which of your existing systems hold the signals that matter. The integration plan follows from there.

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