Act on customer behavior the moment it happens
A streaming data fabric turns raw online and in-store signal into a continuously resolved identity and stable serving contracts — so AI decisions run on live truth, not last night’s batch.
AI runs on live truth — most enterprises still feed it yesterday's batch
The value of a customer signal decays fast. A cart abandonment, a store visit, a support call, a price check — each opens a window that closes in minutes or hours. Yet most enterprise data still moves in nightly batches into a warehouse built for reporting, not for action. By the time a model sees the event, the moment to act on it has passed. Real-time AI rarely fails on algorithms; it starves on stale, disconnected data.
Two structural gaps make it worse. Identity is fragmented across POS, e-commerce, app, CRM and service systems, so "the customer" is really a dozen partial profiles that mostly agree — and without governed, shared identifiers those unified profiles quietly break. And when storage or pipelines change, everything downstream breaks with them. It is why analysts keep landing on the same conclusion: data quality and trust, not model quality, are the number-one barrier to getting value from AI.
- By 2028, 50% of organizations will adopt zero-trust data governance as unverified, AI-generated data proliferates. — Gartner, Jan 2026 ↗
- Data quality is repeatedly ranked the top barrier to AI success, with a majority of companies reporting little to no value from their AI investments. — Industry data-quality research, 2026 ↗
- 88% of organizations regularly use AI, yet more than 80% report no tangible impact on enterprise-level EBIT. — McKinsey, State of AI 2025 ↗
Why this stays unsolved today
Batch latency: decisions on expired intent
Nightly ETL means a model acts on what a customer did yesterday. The highest-intent window — the minutes and hours right after a signal — is already gone by the time the data lands, so even a perfect model optimizes a moment that has passed.
Fragmented identity: a dozen partial customers
With no canonical identifier, records merge inconsistently across POS, app, e-commerce and CRM. Emails change, devices are shared, cookies expire — and unified profiles break silently, so segments and agents reason over a customer that does not quite exist.
Forrester Consulting found only 10% of CDP users said the platform met all their needs. — Forrester Consulting, via Uniphore↗Brittle contracts: a storage change breaks everything above
When the semantic, agent and application layers read directly from storage, any change to a pipeline or engine cascades upward as breakage. There is no stable contract insulating what depends on the data from how the data is stored.
Data you can't trust for AI
Without lineage, recertification and governance, AI faithfully amplifies stale and unverified data — and as models start generating data of their own, the trust problem compounds rather than resolves.
A streaming fabric with continuous identity and stable serving contracts
Kafka is the event backbone, capturing every online and in-store action as it happens. Flink computes over that stream with event-time semantics, windowing and state transitions, so the platform reflects what a customer is doing now — not what a nightly job last summarized — while StarRocks gives high-performance analytical access on top. A Connector Catalog manages integration with POS, e-commerce, CRM, service, advertising and warehouse systems.
Identity is resolved continuously into a Golden Record that evolves as events arrive, rather than a one-time cleanup that immediately starts to rot. Upper layers read through Serving Views — explicit data contracts — so storage engines and pipeline internals can change underneath without breaking the semantic, agent or application layers above. And because the fabric follows a minimal-copy pattern, your existing warehouse stays the authoritative store; the loop adds only event processing, identity resolution and a serving layer.
How it works
The mechanics behind real-time data foundation.
Event Stream
Every online and in-store action lands on Kafka as a continuous behavioral signal. Flink computes over it with event-time semantics and windowing, so state reflects what a customer is doing now rather than what a nightly job last summarized.
Golden Record
Identity is resolved continuously, not in a one-time cleanup. As new events arrive, the record evolves — merging fragmented profiles into a single, current view that downstream semantics and agents can trust.
Serving Views
Upper layers read through stable data contracts. Storage engines and pipeline internals can change underneath without breaking the semantic, agent or application layers that depend on them.
What good looks like
Directional outcomes grounded in the mechanism above and independent benchmarks — a target to design toward, not a guaranteed result.
Decisions on live intent, not stale batch
Because state reflects the current event stream, agents and campaigns act inside the window that still converts — minutes after a signal, not the morning after — instead of optimizing a moment that has already passed.
One trustworthy customer, continuously
Continuous identity resolution keeps the Golden Record current, so segments, reports and agents read one evolving profile instead of a dozen partial ones that silently disagree.
Change storage without breaking AI
Stable serving contracts insulate everything above the data, so pipelines and engines can evolve without downstream outages — the foundation of trustworthy, governable AI as data volume and machine-generated data grow.
By 2028, 50% of organizations will adopt zero-trust data governance as unverified data proliferates. — Gartner, Jan 2026↗Frequently asked
Does this replace our data warehouse?
No. The fabric uses a minimal-copy pattern — your warehouse stays the authoritative store. Kafka, Flink and StarRocks add event processing, continuous identity resolution and a fast serving layer on top of it, rather than becoming a new system of record.
How does real-time here differ from a CDP’s batch sync?
A CDP typically consolidates data on a schedule. Here the Event Stream is continuous and Flink computes over it with event-time semantics, so state transitions and windows reflect behavior as it happens — the input AI decisions actually need.
How do upper layers stay stable if the storage changes?
Serving Views are explicit data contracts. Semantics, agents and applications read through those contracts, so the storage engine or pipeline internals can evolve without breaking anything above.
More CIO solutions
Overview →See it on your own numbers
Book a walkthrough, or model the LTV:CAC upside with the ROI calculator.