Unified
heterogeneous enterprise data sources into a single queryable layer
Enterprise AI / SaaS
Kore.ai: Scalable Data Infrastructure for Enterprise AI
How we architected a unified, low-latency data platform to power analytics and reporting across Kore.ai's enterprise client base — turning real-time analytics into a competitive differentiator.
Scalable Data Infrastructure for Enterprise AI
At a glance
↓ Latency
end-to-end data delivery latency reduced significantly
Real-time
analytics delivered as a competitive differentiator
The challenge
Constraint 01
Kore.ai's enterprise clients were generating growing volumes of analytics and reporting demands. The existing data infrastructure had not scaled to match — tooling was fragmented across multiple systems, creating latency that slowed down insights delivery.
Constraint 02
The platform needed to serve dozens of enterprise clients simultaneously, each with heterogeneous data sources, varying schemas, and strict SLA expectations — making a one-size-fits-all approach unviable.
Constraint 03
Any solution had to integrate cleanly with Kore.ai's client-facing analytics layer via a Reporting API, without disrupting existing product workflows or requiring clients to change how they consumed data.
The approach
Decision 01
We began with a full audit of existing data flows, source systems, and reporting bottlenecks. This surface-level mapping revealed where the most impactful latency was being introduced — informing prioritization before any engineering work began.
Decision 02
We designed and built a Unified Platform that ingested data from heterogeneous enterprise sources using Kafka for event streaming and MongoDB for operational data storage — normalizing everything into a consistent, queryable layer.
Decision 03
The pipeline was engineered for SaaS-scale throughput using ClickHouse as the analytical store, with Bloom filters applied to reduce unnecessary data scans and Redis for caching frequently accessed reporting results.
Decision 04
A Reporting API Layer was built in Go and Node.js to expose clean, low-latency endpoints for Kore.ai's client-facing analytics — with Grafana and Prometheus providing full observability across the stack on a cloud-agnostic infrastructure.
The outcome
The architecture transformed Kore.ai's data layer from a bottleneck into a platform strength. Enterprise clients could now access real-time analytics with consistent performance regardless of data volume or source complexity.
The Reporting API gave Kore.ai's product team a clean, durable interface for analytics — one that could grow with their client base without requiring infrastructure overhauls.
Most significantly, real-time analytics became a competitive differentiator — something Kore.ai could point to as a product capability, not merely an internal concern.
Unified
multiple enterprise data sources into one queryable layer
↓ Latency
end-to-end data delivery across client reporting pipelines
Real-time
analytics as a competitive product differentiator
Tech stack used
GoNode.jsKafkaClickHouseMongoDBRedisBloom FiltersGrafanaPrometheus
Lessons
Unifying heterogeneous data sources at the infrastructure layer — rather than at the product layer — gives SaaS platforms the flexibility to serve diverse enterprise clients without bespoke engineering per account.
FAQ
How do you handle heterogeneous data sources without breaking existing client workflows?
We design ingestion pipelines that normalize at the infrastructure layer — clients and products interact with a clean API, insulated from the complexity underneath.
Why ClickHouse over a traditional data warehouse?
ClickHouse is purpose-built for high-throughput analytical queries at SaaS scale. It gave us the speed needed for real-time reporting without the overhead of traditional OLAP systems.
How do you ensure observability across a cloud-agnostic stack?
We standardize on OpenTelemetry-compatible tooling — Prometheus for metrics and Grafana for dashboards — which works consistently regardless of cloud provider.
Build analytics your enterprise clients can rely on
We can design and deliver a scalable data infrastructure layer that turns reporting into a product strength.
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