Defect detection for precision manufacturing
Computer vision, edge vs cloud inference trade-offs, and quality workflows that cut manual inspection load—architecture for real-time inspection pipelines.
Product outcomes and system design notes from Baaz: full case studies on shipped platforms, plus architecture guides you can cite for boundaries, reliability, and data integration.
Computer vision, edge vs cloud inference trade-offs, and quality workflows that cut manual inspection load—architecture for real-time inspection pipelines.
Assessment workflows, integrations, and scalable backend design for high-volume recruiting—product engineering from strategy through production.
A B2B SaaS reference architecture is an opinionated template for how web and mobile clients, APIs, identity, workflows, and data stores fit together so teams can ship predictably without redrawing the whole system on every feature. This guide gives a production-minded baseline: where to draw boundaries, when to call synchronously versus publish events, how to isolate tenants, and which failure modes to design for first. It distills patterns we apply at Baaz on product builds from MVP through scale-up—aligned with widely cited operational guidance from Google's Site Reliability Engineering practice on managing reliability via clear service boundaries and measured risk (Google SRE books, O'Reilly).
Service Level Objectives (SLOs) are internal reliability targets expressed as a percentage over a window—for example, 99.9% of API requests complete successfully in under 300 ms each calendar month. Service Level Indicators (SLIs) are the measurable good events divided by valid events; error budgets are the allowable unreliability (e.g. 0.1% in a 99.9% SLO) you can spend on launches, refactors, or aggressive rollouts. This guide shows how to choose SLIs, set realistic targets, tie alerts to SLO burn rather than noisy thresholds, and align product and engineering on trade-offs. The framing follows Google's Site Reliability Engineering book (Chapter "Service Level Objectives"), which popularized error budgets as a way to balance velocity and stability.
Reliable data integration moves facts between services, databases, and analytics systems without losing updates or double-applying them when networks retry. Three dominant patterns are domain events (publish from the app), the transactional outbox (atomic commit with state), and change data capture (CDC—stream changes from the database WAL/binlog). This article explains when each fits, ordering and idempotency requirements, and the operational checklist we use before recommending one on client builds. For transactionally consistent messaging, the outbox pattern is well documented in enterprise integration literature and Debezium's CDC documentation (open-source CDC platform) as complementary tools: outbox for application-intent events, CDC for DB-level change streams.