Real-Time Data Streaming Solutions for Analytics, AI & IoT

Live data pipelines for instant operational visibility — from event streaming and stream processing to IoT ingestion and real-time AI, we build the infrastructure that turns data in motion into decisions in action.

Batch data tells you what happened. Streaming data tells you what is happening — right now.

The difference between batch and streaming is the difference between knowing what happened and knowing what is happening.

When Batch Processing Is Not Enough:

With CaliberFocus Streaming Systems:

Event streaming platforms, stream processing systems, and IoT/edge pipelines — for every source and every latency requirement.

Event Streaming Platforms & Kafka Architecture

The backbone of real-time enterprise data flow

Apache Kafka is the standard for enterprise event streaming — but deploying Kafka at scale requires architectural decisions that determine reliability, cost, and operational burden for years. We design, deploy, and operate production Kafka environments that handle millions of events per second with the fault tolerance and delivery guarantees enterprise operations require.

Apache Kafka cluster design — broker topology, partition strategy, replication factors, and retention policies for your throughput and durability requirements
Managed Kafka deployment — Confluent Platform, Azure Event Hubs, Amazon MSK, and Google Pub/Sub for cloud-native event streaming
Kafka Connect ecosystem — source and sink connectors for ERP, CRM, EHR, databases, and cloud platforms without custom integration code
Schema Registry implementation — Avro, Protobuf, and JSON Schema management for contract-first event-driven architectures
Topic design and partition optimisation — event modelling, key strategy, compaction policies, and consumer group management
Kafka operations and monitoring — consumer lag alerting, cluster health dashboards, rebalancing management, and capacity planning

Stream Processing & Real-Time Analytics

Turn data in motion into decisions in milliseconds

Streaming data is only valuable when something acts on it. We build stream processing pipelines that filter, aggregate, enrich, and analyse data in motion — producing real-time dashboards, triggering automated responses, feeding live ML models, and routing events to the systems that need to act on them immediately.

Apache Flink and Spark Streaming — stateful stream processing with exactly-once semantics, windowing, and complex event pattern detection
ksqlDB and Kafka Streams — real-time SQL over event streams for lightweight transformations, filtering, and aggregation without a separate processing cluster
Microsoft Fabric Real-Time Intelligence — event stream processing, real-time dashboards, and KQL-based analytics natively integrated with the Microsoft data stack
Complex Event Processing (CEP) — multi-event pattern detection for fraud signals, clinical deterioration patterns, and operational anomaly sequences
Streaming feature pipelines — real-time feature extraction and serving for online ML models that require current data at inference time
Real-time dashboards and alerting — sub-second dashboard updates and threshold-based alerts integrated with PagerDuty, Teams, and Slack

IoT & Edge Data Pipelines

From sensor to decision at the speed of operations

Industrial IoT, medical devices, connected vehicles, and smart infrastructure generate data at the edge — where cloud latency is unacceptable and bandwidth is constrained. We architect IoT data pipelines that process at the edge, transmit intelligently, and integrate with enterprise data platforms for analytics and AI at scale.

IoT ingestion architecture — MQTT, AMQP, and HTTP-based ingestion from industrial sensors, medical devices, and connected assets at scale
Edge computing design — on-premise stream processing for latency-sensitive operations (factory floor, clinical bedside) where cloud round-trips are not viable
Time-series data platforms — InfluxDB, TimescaleDB, and Azure Data Explorer for high-frequency sensor data with efficient compression and query performance
Device management and telemetry — Azure IoT Hub, AWS IoT Core, and Google Cloud IoT for device provisioning, OTA updates, and fleet management
Edge-to-cloud architecture — intelligent data tiering that processes locally, transmits summaries, and syncs full data on schedule for optimal bandwidth utilisation
Streaming ML at the edge — lightweight model inference on edge hardware (NVIDIA Jetson, Intel NUC) for real-time anomaly detection without cloud dependency

From event sources to governed, AI-ready streaming storage. Fault-tolerant, low-latency, compliance-ready

Streaming Layer

What Gets Built Here

Event Sources

Applications, databases (CDC via Debezium), APIs, IoT sensors, medical devices, POS systems, and user interactions — every event-generating source connected to the streaming backbone via Kafka Connect or native producers.

Event Streaming Platform

Apache Kafka or managed equivalents (Confluent, MSK, Event Hubs) — the durable, ordered, replay-capable event log that decouples producers from consumers and provides the delivery guarantees enterprise operations require.

Stream Processing Layer

Apache Flink, Spark Streaming, or ksqlDB — stateful transformations, enrichment, windowing, aggregation, and complex event detection in real time. The layer that turns raw event streams into actionable signals.

Real-Time Serving Layer

Live dashboards (Grafana, Power BI Streaming), alert systems (PagerDuty, Teams), operational databases (Redis, Cassandra), and downstream consumers — the systems that receive and act on processed stream outputs.

Streaming Storage Layer

Real-time data landed into the lakehouse (Delta Lake, Iceberg) for historical analysis, model training, and compliance. Unified batch and streaming architecture using the same storage layer for both workload types.

Governance & Security

End-to-end encryption in transit and at rest, role-based access on topics and schemas, PII masking and anonymisation in-stream, audit logging, and HIPAA/GDPR/PCI-DSS compliance for regulated streaming environments.

Use cases where streaming delivers operational value that batch cannot.

Healthcare & Clinical Operations

Patient vital signs streaming — continuous monitoring with real-time deterioration alerts integrated with clinical decision support systems

RCM real-time eligibility — stream-based eligibility verification at point of service with instant coverage and benefit determination

Clinical event processing — EHR write events streamed to analytics, coding assistance, and care coordination systems in real time

Medical device data ingestion — IoT pipelines from infusion pumps, ventilators, and monitoring equipment feeding clinical AI systems

Financial Services

Real-time fraud detection — transaction events scored by ML models within milliseconds, with automated card block and customer notification

Market data processing — tick data ingestion and order book analytics at sub-millisecond latency for trading systems and risk management

AML transaction monitoring — streaming transaction graph analysis for pattern detection across millions of events per second

Real-time KYC and compliance — event-driven customer risk scoring and regulatory alert generation at account opening and transaction time

Manufacturing & Industrial IoT

Predictive maintenance streaming — sensor telemetry from production equipment processed at the edge with ML anomaly detection and maintenance triggers

Real-time quality monitoring — inline sensor data processed against statistical process control limits with automated line stop alerts

Supply chain event streaming — inventory events, shipment updates, and supplier signals processed for real-time demand and risk visibility

Energy consumption monitoring — utility metering and production energy data streamed for real-time efficiency optimisation and cost management

Retail & Logistics

Real-time inventory visibility — RFID, barcode, and WMS events streamed for live stock positions across warehouses, stores, and transit

Personalisation in real time — clickstream and behaviour events processed instantly for real-time product recommendations and dynamic pricing

Fleet and delivery tracking — GPS telemetry, ETA calculation, and exception detection for real-time logistics visibility and customer communication

Operational intelligence dashboards — store, warehouse, and distribution centre KPIs

<10ms

End-to-end event processing latency in production systems

99.99%

Uptime SLA for mission-critical streaming platforms

40%

Reduction in fraud losses via real-time detection

22%

Logistics efficiency improvement via real-time route optimisation

The complete modern streaming stack — vendor-agnostic, production-tested.

Domain	Tools & Platforms
Event Streaming	Apache Kafka · Confluent Platform · Azure Event Hubs · Amazon MSK · Google Pub/Sub
Stream Processing	Apache Flink · Apache Spark Streaming · ksqlDB · Kafka Streams · Microsoft Fabric RTI
IoT & Edge	Azure IoT Hub · AWS IoT Core · MQTT Brokers · NVIDIA Jetson · Apache NiFi
Time-Series Databases	InfluxDB · TimescaleDB · Azure Data Explorer · QuestDB · Amazon Timestream
Real-Time Serving	Redis · Apache Cassandra · Grafana · Power BI Streaming · Elasticsearch
Streaming Storage	Delta Lake (streaming) · Apache Iceberg · Apache Hudi · Snowflake Streams · BigQuery Streaming
Observability	Confluent Control Center · Datadog · Prometheus + Grafana · LinkedIn Burrow · Kafdrop

Batch platforms tell you what happened. Streaming platforms power what happens next.

Data Platform

Streaming lands real-time events into Delta Lake / Iceberg for unified batch + streaming analytics

AI & ML Systems

Streaming features fed to online ML models for real-time inference and continuous learning pipelines

Operations & Agents

AI agents consume streaming signals to take automated operational actions in real time

ML is most powerful when connected to the full pipeline

Data Engineering & Pipelines

Batch pipeline infrastructure that works alongside your streaming architecture

Cloud Data Platform & Architecture

The lakehouse platform that streaming data lands in for unified analytics and AI.

ML & Predictive AI

Online ML models that consume real-time streaming features for live inference.

AI Agent Development

AI agents that subscribe to event streams and take automated operational actions

The gap between a model that predictIf there is a business decision that should be made in seconds — not hours — streaming is the answer. Let’s design the architecture.s and a system that acts is where most ML projects stall. We close that gap.

Industries we serve

We collaborate with global technology leaders to deliver secure and scalable growth-driven digital solutions. Our partnerships strengthen our ability to innovate, accelerate transformation, and drive measurable business impact for our clients.

Case Studies

We used generative AI to automate documentation, compliance checks, and medical coding. The solution improves accuracy, cuts manual effort, speeds turnaround, and ensures regulatory compliance in clinical use.

0 +

200 +

When patient data was summarized clearly, documentation felt less burdensome. With CaliberFocus, clinician satisfaction rose from 58% to 81% without changing how teams work.

Dr. Rebecca HallCMIO, Healthcare

Better documentation and fewer audit issues delivered real savings. With CaliberFocus, billing compliance improved to 98.6%, reducing risk while easing the burden on clinicians.

Sophia WilliamsCTO, Clinical care

We gained clear visibility into student performance. Engagement rose, scores improved, and administrative effort dropped by nearly 30 percent, giving educators time to teach.

Andrew MillerDirector, Education Technology

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CaliberFocus delivers AI and machine learning development services that combine deep machine learning and deep learning expertise with production-grade MLOps. As a trusted machine learning service provider, we help organizations move models from experimentation to scalable production, delivering measurable business impact, accuracy, and long-term value.

Real-Time Data Streaming

Act on Data the Moment It Happens.

Batch vs. Streaming

When hours become seconds

When Batch Processing Is Not Enough:

With CaliberFocus Streaming Systems:

What we build?

Three real-time data streaming capabilities

Event Streaming Platforms & Kafka Architecture

Stream Processing & Real-Time Analytics

IoT & Edge Data Pipelines

The architecture

Production streaming architecture — six layers

Streaming Layer

What Gets Built Here

Event Sources

Event Streaming Platform

Stream Processing Layer

Real-Time Serving Layer

Streaming Storage Layer

Governance & Security

Where this works?

Real-time streaming in production — by industry

Healthcare & Clinical Operations

Patient vital signs streaming — continuous monitoring with real-time deterioration alerts integrated with clinical decision support systems

RCM real-time eligibility — stream-based eligibility verification at point of service with instant coverage and benefit determination

Clinical event processing — EHR write events streamed to analytics, coding assistance, and care coordination systems in real time

Medical device data ingestion — IoT pipelines from infusion pumps, ventilators, and monitoring equipment feeding clinical AI systems

Financial Services

Real-time fraud detection — transaction events scored by ML models within milliseconds, with automated card block and customer notification

Market data processing — tick data ingestion and order book analytics at sub-millisecond latency for trading systems and risk management

AML transaction monitoring — streaming transaction graph analysis for pattern detection across millions of events per second

Real-time KYC and compliance — event-driven customer risk scoring and regulatory alert generation at account opening and transaction time

Manufacturing & Industrial IoT

Predictive maintenance streaming — sensor telemetry from production equipment processed at the edge with ML anomaly detection and maintenance triggers

Real-time quality monitoring — inline sensor data processed against statistical process control limits with automated line stop alerts

Supply chain event streaming — inventory events, shipment updates, and supplier signals processed for real-time demand and risk visibility

Energy consumption monitoring — utility metering and production energy data streamed for real-time efficiency optimisation and cost management

Retail & Logistics

Real-time inventory visibility — RFID, barcode, and WMS events streamed for live stock positions across warehouses, stores, and transit

Personalisation in real time — clickstream and behaviour events processed instantly for real-time product recommendations and dynamic pricing

Fleet and delivery tracking — GPS telemetry, ETA calculation, and exception detection for real-time logistics visibility and customer communication

Operational intelligence dashboards — store, warehouse, and distribution centre KPIs

What you can expect?

Outcomes from production streaming systems

<10ms

99.99%

40%

22%

The tool chain

Tools & platforms we work with

Real-time streaming connects every layer of the data & AI stack.

Data Platform

AI & ML Systems

Operations & Agents

Why CaliberFocus?

What makes our streaming engineering different?

Production Streaming Engineering

AI-Integrated Streaming

Compliance-Ready Architecture

Unified Batch and Streaming

Connected Services

The data & AI systems streaming powers

Data Engineering & Pipelines

Cloud Data Platform & Architecture

ML & Predictive AI

AI Agent Development

Ready to act on data as it happens?

Industries we serve

Healthcare

Industrial Manufacturing

Banking and Finance

Retail and Ecommerce

Pharma & Life Sciences

Logistics and Supply Chain

Energy and Utilities

Media and Entertainment

Travel and Hospitality

Education & EdTech

Application innovation backed by deep engineering..

Act on Data the Moment
It Happens.

Enhancing
Clinical Care,
Fewer Readmits!