Our Tools & Infrastructure
The Infrastructure Behind Predictable Salesforce Delivery
Cumulus Vision replaces the traditional Salesforce consulting model with a delivery engine built for high-growth engineering teams. We use custom AI infrastructure with Model Context Protocol (MCP) to document, validate, and test decisions in real time.
Our infrastructure isn't for teams seeking a quick fix. It is for leaders building long-term assets on Salesforce. We catch errors in the sandbox, not on launch day. We prevent knowledge loss when people change roles. By automating validation, we eliminate the majority of manual errors before they reach production.
Delivery Philosophy
We Deliver the Security of Predictability
We turn Salesforce delivery into an auditable, repeatable engineering system.
Results Over Endless Reports
We optimize for results, not hours logged. Your business needs working systems that solve real problems, not endless status reports about activities that don't move the needle.
Disciplined Speed Avoids Risk
Our systems move fast because they reduce errors using AI and automation, not because they skip steps. We believe velocity comes from getting it right the first time.
AI Enhances Human Expertise
We use AI to handle the routine – analysis, validation, boilerplate code – while humans control strategy and stay accountable for architecture and decisions.
Smart Systems Beat All-Night Heroics
Heroics are symptomatic of bad architecture. We don't rely on developers pulling an all-nighter. We rely on automated regression testing that prevents the fire in the first place.
AI-Powered Delivery
How AI Fits Into Our Delivery Model
We use AI as a delivery accelerator. It helps our teams deliver better project outcomes, faster. We embed self-improving AI agents at every step of our processes, freeing our team to focus on high-value tasks where human judgment is a necessity.
Architecture, Not Chatbots
Our AI agents operate with direct access to your specific Salesforce metadata and knowledge bases. They're purpose-built assistants with explicit guardrails defining permitted API calls, approved code patterns, and validation requirements before any output reaches human review.
Consistency & Continuous Learning
By following the same workflows every time, routine work becomes reliable. Our AI agents catch what people miss while maintaining up-to-date knowledge bases that keep everyone informed, leaving human teams to focus on strategic architecture and novel problems.
Self-Improving Over Time
Our AI agents continuously update their knowledge based on real usage patterns and feedback. As time goes by, each engagement is more efficient than the last, driven by a living, self-updating system rather than starting from nothing with each new project.
Your Investment
What This Means for Your Investment
Our AI-enhanced delivery model combines man hours with AI agent jobs, so you get the strategic thinking and architectural judgment of senior engineers without paying for them to handle routine tasks that agents can do faster and more consistently.
A custom approval process that would traditionally take 7–8 hours of developer time now takes about an hour of human oversight plus 3–4 agent jobs handling tasks like intake analysis and code generation. The work gets done faster – and you're not paying senior developer rates for tasks that don't require senior developer judgment.
Existing clients working with our AI-enhanced approach are already seeing meaningful improvements in both delivery speed and output quality.
Example: Custom Approval Process
Senior developer judgment where it matters. AI agents handling intake analysis, code generation, and validation.
Under the Hood
How the System Works
We use custom-built MCP servers to bypass the token-limit amnesia that plagues standard AI tools, ensuring our agents remember architectural decisions made six months ago.
We orchestrate AI agents that operate with real customer data and context using business-specific Retrieval-Augmented Generation (RAG) knowledge bases together with controlled access to web search and relevant reference tools.
We built this system when we realized that the generic AI tools available on the marketplace lack the governance and context that production work demands. We needed strict control over three things:
What an AI agent can access
What actions the AI agent can take
How outputs are validated
That solution doesn't exist commercially, so we built it for ourselves and our clients. We built something we could control and trust, with strict guardrails and the flexibility to align with unique business constraints through self-updating, continuously maintained knowledge bases.
Our Toolchain
How We Deliver, Stage by Stage
Work Intake, Planning & Collaboration
- Eliminate status meetings with real-time project visibility
- Track every decision from request to deployment without interrupting teams
- Reduce unnecessary revisions caused by missing requirements
How work enters our system
Requests don't come in through scattered Slack messages. They aren't buried in email threads, never to be seen again. They enter through a structured intake process where our Intake & Triage Agent analyzes everything before work starts.
We clarify what's needed, identify dependencies and other complexities that could block progress, and prioritize based on what matters to your business. We do all this before work starts and issues are cheap to address, not after developers have already built the wrong thing.
Tools and systems we use
Jira centralizes the entire delivery lifecycle. Instead of chasing status updates across email threads, clients and engineers see the exact same reality, from sprint planning to product deployment.
Jira Service Management handles our intake and support. Requests enter our systems as structured data – we know who requested what, when they need it, and what depends on it.
Confluence documents decisions. From technical specifications and architectural choices to implementation guides, it's all stored here, so your team can reference them a year later without needing to call us to explain old decisions.
Bitbucket tracks every code change. We know who changed what, when, and why – and if something breaks, we can roll it back. If it works, we can find and replicate that pattern effortlessly.
Why we do this
Email threads and Slack channels lose context. "I thought you were handling that" wastes days. Our structured intake means nothing falls through the cracks as every request and decision comes with clear ownership.
Shared visibility means your team knows what is being built without interrupting engineers mid-work.
Development & Environment Configuration
- Get solutions built to standard patterns your team recognizes and can maintain
- Hand off code that your developers can modify without calling us to explain it
- Catch mistakes in sandboxes before they impact production systems
How we build
We keep development and production separate. Code moves through deliberate stages in a standardized development workflow, not the scramble-and-pray approach of untested deployments.
Our Development Assistant Agent handles routine configurations and Apex code generation, following established standards and catching syntax errors instantly so your human experts can focus on complex logic.
Tools and systems we use
We use Salesforce sandboxes to isolate development work from production. This lets us break things safely as we figure out and build working solutions.
Bitbucket gives us version control that makes debugging an investigation rather than guesswork.
We use standard IDEs like VS Code with AI assistance so our team can focus on working with your business' constraints and technical debt. Our senior engineers consider the tradeoffs between refactoring legacy code or working around it rather than hunting down syntax errors.
Why we do this
Standardized workflows lead to cleaner code and implementation for faster handoffs, while the separation of development and testing environments reduces risks in production.
We keep development agile while production remains stable, with version control providing oversight and faster debugging.
Testing & Quality Assurance
- Slash Mean Time to Detection from days to minutes with continuous validation
- Change existing functionality without wondering what else you just broke
- Ship features knowing edge cases were caught before UAT, not during it
Our testing approach
Quality is a continuous process, not an afterthought. We don't wait until the end of a project to find bugs when they are expensive to fix. We aim to catch issues as early as possible – ideally, the moment they appear.
We follow the Gherkin methodology for testing, writing test scenarios in plain language that are easy for all stakeholders to understand. Our test scenarios are defined before development starts so everyone understands what "it works!" means.
Tools and systems we use
We use a QA & Testing Agent to generate test scenarios and catch the edge cases that humans might miss, with automated validations running where they make sense and are appropriate, using Salesforce's testing framework for Apex code coverage and validation.
Our most powerful tool is human judgment. Our human QA focuses on areas like business logic, user experience, and using manual tests to identify the edge cases that come when real people use systems in unexpected ways.
Why we do this
Finding bugs in production costs more than catching them in development. Our continuous testing and quality assurance creates faster feedback loops when changes break existing functionality.
This focus on testing leads to fewer surprises late in development and minimizes costly issues after deployment.
CI/CD & Release Management
- Eliminate 95% of manual deployment errors with automated pre-release validation
- Roll back failed releases in minutes instead of scrambling to patch in production
- Ship faster as standardized pipelines eliminate manual deployment checklists
How we release
Our releases follow automated pipelines rather than a manual deployment checklist where steps can be forgotten. Before release, our Release Agent ensures all code moves through a pre-release validation designed to catch issues.
After release, rollback strategies are built into every deployment, enabling us to quickly reverse changes if something breaks.
Tools and systems we use
We use Azure DevOps Center to orchestrate our CI/CD pipeline. Integration tests run automatically on every commit, ensuring code quality before it ever reaches a staging environment.
Our deployment validation strategy ensures that metadata deploys cleanly and without errors. Rollback procedures are tested and ready to execute if needed.
Why we do this
Manual deployments introduce the risk of human error – the missed configuration, the forgotten dependency – whereas automated release pipelines make for predictable and repeatable releases, since we follow the same tried and tested process each time.
Pre-release validation reduces your deployment risk by catching problems in a controlled environment, with full auditability showing what changed and when, giving compliance teams the data they need to troubleshoot.
Knowledge, Documentation & Continuous Learning
- Stop paying to reverse-engineer your own systems when people leave
- Reduce manual research time by building on historical documentation
- Secure your institutional knowledge against team turnover and role changes
How we capture knowledge
We don't wait for handoff to write documentation. It happens continuously. Our Documentation Agent records all architectural decisions in real-time – the constraints, the tradeoffs, and the "why" behind the code. This ensures that the reasoning survives long after the original developer has moved on.
This documentation is fed back into the relevant knowledge bases and AI agents, benefiting future work. When we solve a problem once, that solution becomes part of the knowledge base, so future projects with similar challenges get a head start on solutions.
Tools and systems we use
We use Confluence as our central repository for technical documentation and implementation guides. These documents will be available to your team to reference, even years later.
Our RAG knowledge base captures this same information in human-friendly and AI-friendly formats. As projects evolve, this information is constantly updated and refined, providing documented solutions for common problems.
Why we do this
Knowledge walks out the door when people leave the company, leaving teams rushing to reverse-engineer undocumented systems, which can be a costly process.
By continuously maintaining a knowledge base starting on day zero, we make sure the documentation is accurate and up to date – and immune to team changes.
Security, Access & Governance
- Track every change with audit trails showing who changed what, when, and why
- Trust your AI-enhanced delivery with security controls embedded at every layer
- Turn compliance audits into non-events with governance baked in from day one
Our security practices
In production, access is read-only, with configuration and development restricted to a controlled sandbox environment where mistakes don't impact live systems.
All changes move through rigorous approval workflows before going to production, creating clear approval and audit trails that track who changed what, when, and why.
Tools and systems we use
We use Salesforce's role-based access model with granular permission controls, so users only see the data they're authorized to access, with OAuth2 authentication providing enterprise-grade integrations security.
All system transactions are logged using custom logging objects or Azure Application Insights, providing full visibility into your system. When integrations do fail, they trigger immediate alerts so issues can be addressed before they cascade into bigger problems.
Why we do this
Unrestricted production access is an outage waiting to happen. By building governance into systems from the start, we prevent problems rather than scramble to fix them later, providing a safety net against production disasters.
Our audit trails help you quickly understand what changed between the working version and the broken one, while integration monitoring lets your team catch failures in minutes, not hours after impacting downstream systems.
Built to Last
Cumulus Vision Builds Systems That Last
The tools we use and the systems we've built let us work faster without sacrificing quality. AI allows us to reduce errors, capture knowledge, and create efficiency without cutting corners.
We stay current with the latest updates and emerging technologies so your business can gain a powerful edge over competitors still using yesterday's approach. We evaluate new tools pragmatically, adopting only what adds real value to your project.
Our infrastructure is continuously improving. As we work on more projects, our AI agents get smarter with richer knowledge bases and more refined patterns. Every time you work with Cumulus Vision, you benefit from that accumulation of expertise and technology.
Let's talk about your Salesforce challenge
Ready to partner with senior engineers who understand complex architecture and solve problems others can't?