Make your enterprise data AI-ready. Inside your own walls.
One governed platform for ingestion, transformation, ML, APIs, and operations, so your AI starts on a single trusted source instead of another data project. Runs on your cloud or fully air-gapped on your own infrastructure, under one contract.
Live platform, your data, no slides.
Nobody set out to build this stack.
It arrived one sensible purchase at a time. Every tool solved its own problem and handed you a new one: the seam between it and the next.
Two things a managed cloud service cannot do.
Most data platforms are delivered to you as a service, from someone else's cloud. DataByte runs entirely where your data already lives, and keeps itself running once it is there.
Your data and your AI never leave your walls.
The entire platform, from ingestion and transformation through ML and the AI agents on self-hosted models, runs fully air-gapped on your own infrastructure. Nothing calls home. No data egress, no dependency on a vendor cloud, no exception to negotiate.
The platform runs itself, and fixes itself.
The SMART framework watches every pipeline and Sherlock runs closed-loop root-cause analysis and remediation inside the platform. Self-healing operations, and a team that stops firefighting.
Retire six to nine tools.
One platform runs ingestion through delivery across 2,000+ systems, so your team ships for the business and stops maintaining the plumbing between vendors.
Start AI on data you already trust.
Governance applies from the moment data lands, so every model, dashboard, and API reads from the same governed source.
Pilots that survive production.
Spark and Kubernetes underneath, auto-scaling to enterprise volume. The environment that runs your proof of concept is the one that runs production.
Your infrastructure, your code, your formats.
Runs on your Kubernetes and your cloud. Pipelines are Spark, models are standard, storage stays in open formats. Nothing is trapped in a proprietary layer.
Audits become a report you run.
One security model, automated PII tagging, and lineage across every module, so GDPR, HIPAA, and SOX evidence is generated rather than assembled.
Agents that carry out the work.
Built-in agents build pipelines from plain English, summarise failures, and close incidents, so your people spend their time on outcomes.
Consolidate the stack without a two-year migration, on infrastructure you already run.
One governed source of truth, with lineage your AI initiatives can actually stand on.
Data residency you control, air-gapped deployment, and evidence you can hand an auditor.
Six to nine renewals, audits, and price negotiations collapse into one contract.
Three decisions make the whole thing work.
We replaced 'eight perspectives' with three pillars. Architecture, Intelligence, and Operations, the decisions every data platform has to make, made once and made well.
Unified by architecture, not integration.
One data model, one governance layer, one RBAC. An integrated platform, built together from the start.
AI that ships inside the platform.
ML Studio, Forecaster, and Anomaly Detector cover the full ML lifecycle. A growing library of agents lets teams converse with the platform in plain English.
Governed, observed, and self-healing by default.
The SMART framework, SLA, Monitoring, Actions, Rules, Traceability, is embedded in every module. Sherlock closes the loop with autonomous remediation.
One integrated platform. One contract.
Every module is purpose-built and production-grade on its own. Together they share one data model, one catalog, one security model, and one operational surface.
Move data in at any cadence: scheduled batch, on demand, or change data capture that streams every insert and update as it happens.
Connect any two systems without a custom project. Build the flow on a visual canvas, test it before it goes live, and run it with approvals attached.
Build transformation pipelines on a visual canvas or in a notebook, whichever suits the person doing the work. One engine for batch, streaming, and on demand.
Take a model from data preparation to production in one place. AutoML and experiment tracking, one-click deployment as a versioned API, drift monitoring after go-live.
Forecast demand, capacity, or cash with 25+ time-series algorithms. Scheduled runs, accuracy dashboards, and side-by-side backtests, so you can see which model to trust.
Catch the problem while it is still small. Continuous learning on live SQL, Kafka, webhook, and API streams, with alerts that carry the data that triggered them.
Root cause analysis that runs itself. No-code decision trees isolate the failing component, trigger the fix, and verify recovery before the incident is closed.
Automate the operational work nobody wants to own. Event and schedule-driven workflows with conditional logic, action blocks, and first-class integration with external systems.
Built for any data team that has outgrown the stitch.
DataByte is general-purpose by design. The same platform runs CDC, ML pipelines, API delivery, forecasting, process automation, and governance, whatever mix you need.
Batch, CDC, or streaming, all governed by the same catalog and RBAC from the moment data lands.
AutoML, drift detection, and 25+ time-series algorithms ship with the platform. One-click REST deployment.
Turn any SQL or NoSQL source into a versioned, secured REST API with rate limits and auto-generated Swagger.
Sherlock runs decision-tree RCA, ProcBot automates workflows, DataOps exposes live pipeline health.
Visual report builder with scheduled email, SFTP, or API delivery, governed end to end.
SMART turns GDPR, HIPAA, and SOX reporting into a scheduled report.
Connects to the stack you already have.
Two thousand plus connectors across databases, warehouses, cloud storage, streaming, SaaS, BI, and file formats. Drag-and-drop if you want it; custom code if you need it.
See it running on your stack.
Thirty-minute walkthrough. Your data, your connectors, real pipelines. No slideware.