In AI Delivery, Context Is Queen

The context you give your AI IDE matters as much as which AI IDE you choose.

Summary

• Synechron delivered two Salesforce Experience Cloud portals for a U.K. utility in under five weeks against a traditional 12-week estimate, using context engineering and the Grok Build (formerly Cursor) artificial intelligence (AI) integrated development environment (IDE).
• Synechron's Context-Centric Delivery method captures project knowledge in a governed graph that Grok Build reads over Model Context Protocol (MCP), the same way it reaches Figma, Jira and Playwright.
• Synechron pilot engagements measured a 50% to 60% overall productivity gain and up to a 90% reduction in implementation time on well-scoped development tasks.
• Synechron's experienced Salesforce developers stay in the loop throughout, while AI shifts the delivery bottleneck up the funnel to defining what the business needs and why.

Earlier this year our Salesforce team built two Experience Cloud portals for a U.K. utility, one for customers and one for brokers. Our initial estimate, using traditional means and assumptions, was 12 weeks. The first portal took 2.5 weeks. It laid a foundation that meant the second portal was delivered two weeks later.

This was the first real test of our AI delivery method, built around two things: context engineering and a genuine AI integrated development environment (IDE), which for us is Grok Build.

Requirements still needed defining. Designs still needed designing. Those are conversations with people, and a conversation takes as long as it takes. What our context engineering platform changes is everything around them: writing them up, turning them into stories, holding the design detail, feeding all of it into the build. Define, design, build: all three now move faster. Build most significantly.

None of this happened on its own. The portals were built by experienced Salesforce developers using Grok Build who know what good looks like, and that's the part no tool replaces. AI produces something plausible for whatever you ask it. Our people review the requirements, the design and the end-state testing, and they remain in the loop throughout.

Why Grok Build

We've built our AI stack as a mix of built and bought, with our own context engineering platform as the intellectual property (IP) at its heart. We've kept it tool-agnostic, so we pick the best tool for each job rather than commit to one vendor's roadmap. Grok Build is the clear choice, on four counts.

It's a true AI IDE. Our engineers needed agentic capability inside the environment they already work in, behaving like the tool they know. They love it, which sounds obvious until you've tried to get a global delivery team to adopt anything. And recent releases have won over colleagues who don't live in an IDE at all.

It's model-agnostic. We choose the model that suits the task without changing tooling, and without losing our rules or our context when we switch. This gives greater optimization over token costs and allows us to use the best models from multiple frontier companies.

It learns. Grok Build builds vector embeddings from the work you do with it and keeps them locally. On one engagement, one of our developers walked Grok Build through a single OmniScript-based process. After that, a fresh session already knew what an OmniScript was in that estate, could find a comparable one and mirror it. Salesforce orgs run on local convention, and a tool that picks convention up from use beats one that needs it re-explained.

And it reaches our context. Grok Build reads our governed project graph over Model Context Protocol (MCP), the same way it reaches Figma, Jira, Playwright and the org itself.

The Problem Context Engineering Solves

The biggest barrier to people adopting AI at work is the answer they get back. Ask AI about your program without the right context and what returns is generic and two-dimensional. Traditional AI chat sits in its own silo. Ad hoc AI injected into every enterprise application sits in another. Context never joins up, not between the tools and not between the people using them, so whatever any single tool hands you is that same generic slop. It erodes trust rather than inspiring anyone to find the next use case. Meanwhile, the real picture of a program sits in people's heads, in emails and in Teams chats no one scrolls back through. Every new AI session starts from less than zero.

Our answer is Context-Centric Delivery. Project context gets captured progressively, into a governed knowledge graph, and everything downstream draws on it. Not only the engineering tools: our consultants, project managers, quality assurance (QA) teams and sales teams work from the same picture. From the first conversation with a client, we start amassing a structured account of what they need and why.

Alongside that, we have codified our collective experience of transforming businesses on the Salesforce platform. Patterns. Anti-patterns. The design decision that comes back to bite you in month nine. All of it reaches our AI tools, so they know not just the client but how to implement Salesforce and get it right the first time.

Grok Build is where all of it gets applied, in the place the work happens.

The Numbers

Measured across our pilot engagements: workshop to user stories and design to technical tasks both moved from four to eight hours down to two to four hours. Estimate to code delivery moved from two days to under an hour. Overall, a 50% to 60% productivity gain, and up to a 90% reduction in implementation time on well-scoped development tasks. That last figure covers development only, not end-to-end delivery.

While the work itself got considerably faster, what remains is the human alignment: decisions, signoffs, availability. None of which is a tooling problem.

Some Salesforce components come together very quickly with AI. Others take real effort, and that effort has moved up the funnel, into detailing the design, context and guidance well enough that the AI produces the right code and config.

What, Why, How

The bottleneck on capacity has moved up the funnel, where humans and organizations must decide what they need and why. It's always been important, but AI has a way of magnifying both our flaws and our strengths.

The what is what the business actually needs. The why is the reason it needs it, the problems to solve and the value it's going after. The how is the people, process and technology that execute the what and enable the why.

It's overdue. Too many transformations have languished because they were tech-led rather than business-led, and many reading this will have sat in painful steering committee meetings where someone finally asks what quantifiable benefit the program is going to deliver and answers are scarce. That question was always the right one. It just too often didn't get prioritized, because the “how” consumed a disproportionate amount of the time and budget.

The how is now much quicker. Which gives consultants and their clients both the ability and the obligation to spend that recovered time on the what and the why.

Content was king for a decade. In AI delivery, context is queen. And on this board, the queen does most of the moving.