Fast Data captures changes from your systems of record through CDC and serves them as governed Single Views, read in near real time without extra load on the source.
The data every AI initiative depends on sits in ERP, CRM, mainframe and legacy databases, and apps and dashboards read last night's batch of it. Every new integration risks straining the source system.
The next initiative starts from data kept current by CDC and events, seconds old, and the teams who own those source systems stop treating every request as a risk to production.
Numbers travel by export and nobody can prove where they came from, so you're accountable when a fragile pipeline breaks in production, or when an AI initiative stalls because nobody can vouch for the data behind it.
Applications and AI agents query one governed Single View, kept compliant by unified policies. You see a pipeline degrade before the business does, and when someone asks what an AI initiative is built on, you have an answer.
A team needs customer data, so someone builds them another point-to-point integration, a one-off script that answers today's request. It works once, then multiplies: another brittle connection, another maintenance burden.
The same team calls a Single View that already exists, as an API. Connecting to something that is there becomes the faster path, so the shortcut stops being tempting and the maintenance burden stops compounding.
Read changes from your systems of record in real time through CDC, event streams, APIs, webhooks or polling, without adding query load to the source.
Transform, validate, cleanse and standardize the data as it moves, through configurable, event-driven pipelines.
Projections and a Real-time Updater assemble the data into Single Views, governed data products decoupled from the source system.
Serve Single Views through SQL and NoSQL CRUD APIs, an API Portal, or event streams, with RBAC and multi-region replication.
Track pipeline health, performance and data-stream consumption from runtime dashboards.
Real-time ingestion from any source, CDC, event brokers (Kafka, RabbitMQ, NATS), APIs, webhooks, IoT and edge data.
Design, run and monitor event-driven pipelines that transform and aggregate data as it moves.
Projections and a Real-time Updater keep Single Views in sync with the source in near real time, decoupled and offloaded from the system of record.
SQL and NoSQL CRUD APIs, an API Portal, RBAC and multi-region replication.
Runtime views, health and performance metrics, dashboards and consumption tracking.
Agents and applications only act correctly on data that's current and trustworthy, and Fast Data is what keeps it that way in real time.
Scale Agentic Engineering
Fast Data underpins the context engineering behind it, keeping the data agents and applications act on current and trustworthy in real time.
ExploreOne retail cooperative's inventory close, before and after.
Unicoop Firenze, one of Italy's largest retail cooperatives (8,500+ employees, €3B+ revenue), closed its annual inventory by hand: a dedicated team wrote custom logic to reconcile over 600 million warehouse records into SAP. It normally closed by the end of the first quarter.
With Fast Data, the warehouse management system projects raw movement data into a Product Stock Single View that SAP consumes in real time through an API. Inventory now closes on December 31st at midnight, with no manual work.
Reported project figures
600M+
warehouse records reconciled without manual work
4M+
events a day into the Product Stock Single View
under 300ms
response time on the most complex views
Fast Data captures changes from your systems of record through CDC, event streams, APIs, webhooks or polling, processes them as they move, and assembles them into Single Views: governed data products, decoupled from the source, that applications and AI agents can query in near real time.
Fast Data moves, processes and serves it; the rest of the platform discovers, governs and consumes it. Here's what it plugs into, module by module.
Context Catalog
Discovers, documents and governs the Single Views it produces, instead of leaving them hidden.
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AI Foundry
Lets agents and Playbooks query its Single Views as real-time context.
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Flow
Prototypes against its Single Views, instead of an invented data model.
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Console
Deploys, configures and monitors its pipelines, like any other platform workload.
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AI-Ready API
Exposes the data it serves as governed endpoints agents can call.
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