Build | Be Different

Thought leading insights and perspective from Isima and our customers.

Customer data is imported into bi(OS) through various mechanisms, some requiring us to expose a public HTTP endpoint. These endpoints are protected against bots. We also record these attack vectors within bi(OS). We built this multi-cloud bot detection capability in 4 weeks using this data. Further, this dynamic list of bots is also made available to all our customers.
The world’s largest eCommerce player uses zero third-party pixels. The second largest uses 20+. The second-largest player has 6X lower traffic and 2X worse conversion after 20+ years of eCommerce. Is it a coincidence?  We think not. The last decade of SaaS-based crutches to optimize the eCommerce funnel has failed. As consumers demand ever-increasing privacy, the walled gardens of Google and Apple are responding with tightening around third-party support. It's time for a new way to approach eCommerce analytics that optimizes conversions, customer experience (CX), and privacy.
Distributed systems are hard. Infrastructure SLAs, sluggish database queries, and microservices latencies must be stitched together to form an accurate view for any eCommerce business. Add agile CI/CD with a Kubernetes-esque control plane, and the fragmented observability toolchain becomes a tax. How does one answer a simple question: are the customer experience (CX) and business KPIs healthy?  Well, that’s what bi(OS) empowers eCommerce teams to do.
I am Saurabh, a Tech Lead at Pharmeasy. My team develops and maintains the ML infrastructure for serving customer recommendations. Last year, we deployed our first real-time recommendation engine for non-Rx product category recommendations on our home page.  This blog will explain how Isima’s bi(OS) helped with this initiative and the solution architecture for recommendations.
In the early 90s, Intel ran a campaign about spotting the very best PCs. This decade established Intel as the undisputed leader in microprocessors. 30 years later, engineers at Intel and Isima decided to attempt the same for data and analytics.

Too Long, Must-read The [Live] Multi-cloud-native architecture can deliver a 10X better TCO than the other variants.  Some other unique insights we found –  Azure’s NVMe drives beat AWS and GCP by a significant amount latencies. AWS delivered the most respectable IO latencies when using the best network-attached storage. bi(OS) flips compute utilization for […]

Introduction What happens if the machine, an AZ, or a region serving your customers fails? Nothing – if you rely on cloud-native deployments, stateless microservices, and multi-master databases. That’s the promise anyway.  How many IT teams have the luxury of relying on multi-AZ deployments within a cloud? And what about geo-redundancy within a single Cloud? […]