Data Points Processed Daily
ML Inference Latency
ML Model Accuracy
Insights Generated
The Challenge
Building a real-time intelligence engine from scratch
DataPulse AI had a compelling vision: a platform that could ingest data from dozens of sources, apply machine learning models in real time, and deliver actionable business intelligence to decision-makers within milliseconds. What they lacked was the engineering team and architecture to bring that vision to life.
Their initial prototype — built by a small internal team — collapsed under load. At 50,000 daily events it worked. At 500,000 it fell over completely. They needed a complete architecture rethink, a production-grade ML pipeline, and the ability to scale to millions of events per day without degrading latency.
Key Pain Points
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Architecture that could not scale beyond 50K events/day
The monolithic prototype used synchronous processing that created severe bottlenecks. Every ML inference blocked the ingestion pipeline, causing cascading failures at scale.
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Multi-second ML inference latency
Model inference took 3–8 seconds per request — completely incompatible with the near-real-time intelligence experience DataPulse's product required.
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No MLOps infrastructure for model lifecycle management
Models were deployed manually with no versioning, monitoring, or rollback capability. Model drift went undetected, silently degrading prediction quality over time.
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No observability into the data pipeline
When the system failed, the team had no visibility into where or why. Debugging required manual log inspection and took hours, delaying incident response dramatically.
The Solution
An event-driven ML platform built for millions
Techxil's ML engineering and data architecture team designed a completely new platform — built on event-driven principles, with decoupled ingestion, ML inference, and storage layers — capable of processing 1M+ events daily with sub-200ms end-to-end latency.
Real-Time Data Pipeline
Apache Kafka as the event streaming backbone — enabling high-throughput, fault-tolerant data ingestion from 40+ source connectors. Event producers and consumers are fully decoupled, allowing independent scaling of each pipeline stage.
ML Inference & MLOps
TensorFlow models deployed on AWS SageMaker with real-time endpoints. SageMaker handles auto-scaling inference compute, A/B testing between model versions, and automated model monitoring for drift detection. Model training pipelines run on a schedule with automated evaluation gates.
API & Data Backend
Python FastAPI for the high-performance REST API layer, with PostgreSQL for structured analytics data and Redis for sub-millisecond caching of frequently accessed insights. The API serves dashboard data, model predictions, and historical analytics with consistent sub-100ms response times.
Observability & Dashboards
Grafana dashboards provide real-time visibility into pipeline throughput, ML model performance, latency percentiles, and data quality metrics. Automated alerting on anomalies gives the DataPulse team immediate awareness of any issues — typically resolving incidents before users notice.
The Results
Real-time intelligence at massive scale
The new platform delivered on every dimension — scale, latency, accuracy, and business value. DataPulse AI's product went from a proof of concept to a commercially viable platform capable of serving enterprise clients at global scale.
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1M+ data points processed daily without degradation
The Kafka-backed event pipeline scaled smoothly from the initial 50K events/day to 1M+ — with linear cost scaling and no architectural changes. The system is designed to scale to 10M+ events with additional Kafka partitions.
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Sub-200ms end-to-end ML inference latency
SageMaker real-time endpoints combined with model optimisation (quantisation, TensorRT) reduced inference latency from 3–8 seconds to under 200ms — a 15–40x improvement enabling the real-time intelligence product experience.
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94% ML model accuracy — sustained over time
Automated model retraining pipelines and drift detection keep model accuracy consistently above 94%. The MLOps infrastructure ensures models remain current as data distributions evolve.
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300% increase in actionable insights generated per day
By processing 20x more data with better model accuracy and real-time delivery, DataPulse's platform now generates 3x more actionable insights for clients — directly translating to product differentiation and customer value.
Technology Stack
Tools & technologies used
"TechXil brought deep ML expertise and engineering rigour that we simply could not find elsewhere. Our analytics platform now powers real-time decisions for enterprise clients. The architecture they built is not just solving today's scale — it is ready for 10x growth."
James Okonkwo
Head of Engineering, DataPulse AI