From overwhelmed startup to industry challenger
How Revealed scaled real-time analytics 1000× faster, and cut infrastructure costs by 82%, with Enliven Systems.
Problem
Limited resources, growing data needs
Unsustainable cloud costs
Slow time to market
Solution
We designed a hybrid data processing platform that combined on-premises high-performance computing with cloud scalability, cutting costs while enabling rapid growth.
By implementing a real-time analytics pipeline powered by Apache Spark, Kafka, and advanced MLOps practices, we transformed raw social data into actionable insights within seconds.
Our research-driven approach and agile delivery model allowed the client to launch a production-ready MVP in just six months, five times faster than industry benchmarks.
Industry
6 months
Timeline
Location
Engagement model
Project management model
faster delivery
Delivered results to end users 1000× faster than a key competitor, setting a new benchmark for performance.
Costs by 82% through unparalleled scaling on an on-prem Kubernetes Intel High-Performance-Computing stack, outperforming cloud alternatives.
Accelerated feature iteration
Accelerated feature iteration by 5× compared to competitors thanks to standardized AI models and cutting-edge MLOps practices.
Expert adoption
Enabled expert adoption within just one week through a highly expressive query language that simplified learning and onboarding.
Eliminated vendor-lock risk entirely via our Open-Source Software adoption strategy, ensuring stable business continuity and high ROI.
“Enliven Systems quickly understood our vision and transformed it into a high-performing product that far exceeded expectations.”
- GDPR
- ISO 27001
Our solutions comply with GDPR and ISO 27001, trusted by clients across regulated industries.
Reinventing data architecture for continuous innovation
To maintain a long-term competitive edge, continuous innovation is essential. That’s why we moved away from conventional web crawler architectures and built a modern, modular, machine learning–ready pipeline powered by Apache Spark, Apache Kafka, Kubeflow, and FaaS.
This transformation enabled us to identify and deliver high-value opportunities to production in just a single week.
Orchestrating complex systems with precision and speed
Operating a vast ecosystem of interconnected technologies across development, staging, and production environments poses significant challenges, especially when striving to keep ownership costs low. To address this, we adopted GitOps methodologies early in their evolution.
We also introduced auto-scaling mechanisms that maintain physical separation between key development and preview environments while supporting rapid performance iteration and testing.
Building a hybrid, real-time analytics engine
We designed and deployed a fully integrated data processing ecosystem that bridged high-performance on-premises computation with cloud-based elasticity.
At its core, we built a custom Spark-based crawler that continuously ingested massive volumes of social and behavioral data in real time.
This crawler seamlessly interfaced with the Function-as-a-Service (FaaS) MLOps pipeline we built for the client, enabling machine learning models, such as GPT-2, GPT-3, and advanced sentiment classifiers, to annotate and enrich incoming data streams without human intervention automatically.
Continuous model evolution without downtime
A key breakthrough was enabling continuous ML model upgrades without interrupting Spark ingestion or Kafka streaming.
This was achieved through a modular deployment architecture in which model containers could be versioned, tested, and rolled out via GitOps and Flux CD, ensuring zero downtime during updates.
As a result, the platform could evolve rapidly, integrating new AI capabilities within hours instead of days or weeks.
Intelligent workload balancing and dynamic partitioning
To handle the uneven data distributions that caused straggler tasks, we implemented a dynamic partitioning module within Spark.
This component automatically detected “heavy hitter” partitions and rebalanced workloads on the fly, significantly improving throughput and reducing job latency across the pipeline.
The optimization allowed the system to process millions of records with predictable sub-second performance.
High-performance storage and search
Post-ingestion, curated datasets were stored in Couchbase and Elasticsearch, providing an exceptionally fast text-search and retrieval layer.
By integrating white-labelled Kibana dashboards, we delivered a real-time analytics experience that empowered users to explore insights visually, filter data intuitively, and generate ad-hoc reports within seconds.
GPU-accelerated AI inference at scale
To support high-throughput model inference, we deployed NVIDIA inference cards directly to Kubernetes nodes, managing GPU scheduling and allocation dynamically across the cluster. This design achieved over 80% GPU resource utilization, maximizing performance while minimizing cost.
The result was an ultra-efficient, horizontally scalable AI infrastructure capable of running multiple deep learning models concurrently.
Some of the more than 40 technologies we use.
Kubeflow
Seldon
Couchbase
Nvidia Operator
Flux CD
Pytorch
Elasticsearch
The Enliven Systems advantage
Enliven Systems helps ambitious companies turn data into a competitive advantage through cutting-edge AI engineering, research, and cloud optimization.
Distinguished talent pool
Predictable delivery
Experienced researchers
Success in a broad spectrum of applications
Take the first step to transform your data into intelligence that drives impact:
- Deliver results to end users 1000× faster than your competitors
- Reduce infrastructure costs by 82%
- Accelerate feature iteration by 5× compared to your competitors
- Enable expert adoption within just one week
- Eliminate your vendor-lock risk with us
