Blog

An analysis of Salesforce's rapid expansion into the U.S. public sector, tracing its path from cautious early government licensing deals in the 2010s through the launch of Government Cloud in 2012, its pivotal role in COVID-19 vaccine rollouts, and its 2025–2026 push into military and intelligence work via Agentforce and Missionforce. The piece covers major 2026 contracts — including a $5.6 billion Army deal, a $1.6 billion VA agreement, and Pentagon Impact Level 5 authorization — alongside real-world case studies like California's REAL ID processing and the UK's NHS back-office operations. It also examines the structural obstacles still facing Salesforce and other vendors in government tech: legacy IT systems decades old, outdated federal procurement rules, budget constraints, and organizational caution around AI adoption, plus the competitive pressure from Palantir, Microsoft, and Oracle in the race for public sector AI spending.

A field-tested breakdown of the 12 AWS services that make up a typical production architecture, presented in the order a team actually builds them: IAM and S3 first, then EC2, load balancing and Auto Scaling, networking via VPC, RDS for the database layer, and finally Lambda, SNS, SQS, Route 53, and Secrets Manager for async work and messaging. Along the way it covers the settings and defaults that commonly trip people up — public subnets in the default VPC, Multi-AZ vs. read replicas, Elastic IP charges, and SQS retention limits — plus the cost-control habits (budget alarms, tagging, Cost Explorer) that keep a project's AWS bill from creeping up unnoticed before launch.

A backend engineering team recounts how a routine production incident — a sync job that quietly slowed from two minutes to eleven — revealed a gap between knowing data structures for interviews and applying them in everyday code. The piece walks through the fix (dictionary lookups replacing nested loops), then traces the same pattern across N+1 API calls, deduplication scripts, rate limiters, caching decisions, and database indexes, closing with three practical questions engineers now ask before shipping code that touches meaningful volumes of data.

This article is the second in Jet BI's Python engineering series, moving from engineering culture and practices to the concrete technologies behind real production systems. It explains why FastAPI was chosen as an architectural mindset rather than just a framework — covering async architecture, dependency management, and clean API design. It covers SQLAlchemy ORM and PostgreSQL as a data layer that stays maintainable as products grow, Authentication and Authorization as a security-first engineering practice built into the architecture from day one, and Docker and Docker Compose as the foundation for consistent environments across local, CI, and production. The article closes by framing these five technologies not as a checklist but as a coherent engineering approach that scales with both the product and the team.

This article walks through a complete, real-world AI video production pipeline from concept to final edit, built around an actual short film project. It covers every stage: finding inspiration and building a moodboard in FigJam, generating a consistent character in Midjourney with a 360-degree character sheet, compositing scenes in Nano Banana with lighting-matched integration, animating clips in Kling 3.0 and 2.6 (and Seedance for complex movement), and assembling the final cut in CapCut with speed curves, music dynamics, and clip-by-clip color correction. The article includes two reference tables of prompt keywords for both the image and animation stages, plus a full Do/Don't checklist distilled from hands-on experience.

This article argues that the most underestimated risk in Agentforce deployments isn't data quality — it's the automation layer: years of overlapping Flows, Process Builder processes, Apex triggers, and managed package logic that no one has reviewed end-to-end. It explains why AI agents inherit automation complexity without the tribal knowledge human admins carry, why technical debt only becomes visible after an agent hits it in production, and why a clean demo is no indicator of production readiness. The article closes with a concrete, tool-by-tool inventory approach using Flow Trigger Explorer, Salesforce Optimizer, Setup Audit Trail, Apex Debug Logs, Agent Builder, and Health Check — scoped to the specific processes the agent will actually use rather than the whole org.

This article maps the real integration patterns that emerge when multiple Salesforce projects — both managed packages and unpackaged code — share a single org. It covers four concrete patterns: attaching custom triggers to package-owned objects, calling global members exposed by managed packages, writing directly into another project's objects, and runtime-guarded reads of package data. It then addresses access control for authenticated and guest users, including the Master-Detail wall and the without sharing elevation pattern. The piece closes with eight concrete risks (compile-time dependencies that block uninstall, upgrade coupling, silent cascade failures, access invisible to admins) and six actionable recommendations for keeping cross-project coupling manageable.

This article explains how the company builds a mature Python engineering culture that scales alongside its growing use of Python for backend services, automation, and AI-driven solutions. It covers the engineering practices behind maintainable code (clear module boundaries, readability over cleverness), testing as a core discipline using pytest, and shared code quality standards enforced through tools like Ruff and mypy alongside code reviews. The piece also outlines what's next on the engineering roadmap — PostgreSQL, FastAPI, authentication patterns, SQLAlchemy, and Docker — framing these as the next layer of engineering maturity rather than just new tools.

This article explains how GraphQL works inside Lightning Web Components, covering the query and mutation syntax developers need to fetch and modify Salesforce data efficiently. It walks through filtering, sorting, and pagination in queries, shows how to create, update, and delete records with mutations via executeMutation(), and details the lightning/graphql module setup with the graphql wire adapter. The piece also covers practical use cases (dashboards, record detail pages, mobile apps) and weighs GraphQL's benefits against its current limitations compared to Apex.

This article explains how to integrate Salesforce with external task and worklog tracking systems such as Weenvo. It covers the core architecture (Apex Triggers paired with Queueable Apex for asynchronous callouts), secure configuration management (Custom Metadata for IDs, Named Credentials for secrets), and the most common production issues: duplicate task creation, silent failures, and broken retries. The guide includes practical patterns for building task payloads, mapping Salesforce users to external contact IDs, handling parent-child subtasks, logging failures for observability, and implementing safe retry logic with backoff. While the examples reference Weenvo specifically, the architecture applies to any Salesforce-to-external-task-system integration.

This article explores how to replace hardcoded conditional logic in Lightning Web Components with a metadata-driven approach. Learn how Custom Metadata Types enable scalable, dynamic forms in Salesforce, reduce complexity, and improve maintainability across multiple form variations.

This article explores common challenges in Salesforce integrations caused by API limits and platform constraints. It explains how these limitations impact real-world projects and introduces a practical solution using FastAPI as an API layer. The guide covers key techniques such as caching, batching, and request optimization, helping teams build scalable, reliable, and secure integrations while reducing API usage and improving data consistency.