How Crema works in an AI-accelerated world

We use AI to amplify creativity, accelerate delivery, and expand what's possible.

Our perspective

This is a pivotal moment! AI represents a cultural and technological inflection point comparable to the internet. It's democratizing capabilities that were once specialized. As information becomes universally accessible, the differentiator shifts from what you know to how you apply it. When everyone has access to the same models, expertise and judgment become the advantage.

Crema will leverage what's necessary to deliver results. We strive for pragmatism rather than reactivity. Just as we've championed emerging technologies throughout our history, we'll leverage AI, but not just for the sake of it.

As a team, we're facing this inflection point thoughtfully. Pace matters, but so does direction. Moving too fast risks instability; while too slow means missed opportunities. Success requires balancing speed with wisdom, understanding tradeoffs, and maintaining what makes work exceptional: the ability to navigate complexity and shape it into something brilliant.

Rest assured that we're still committed to our top-quality standards. ‍ Crema remains a premium, relational, strategic partner. Our cross-functional expertise, deep client relationships, and commitment to delivering innovative solutions don't change. They're what help clients navigate this transformation responsibly and successfully.

AI is here.

We can’t really go back from this moment. While some see an exciting frontier, we understand not everyone feels ready. At Crema, we’re leaning in because we see this as an inflection point. One that will amplify our potential in ways we're only just beginning to understand.

We’re committed to bringing you along this learning journey, optimistic about how it will improve our capacity and creativity so that we can do the best work of our lives.

This is a peek behind the curtain on how our craft teams are experimenting and ultimately implementing a variety of colorful use cases for AI.

Deciding where to experiment?

Instead of throwing AI at everything to see what sticks, we built a framework for it. If AI could help with any of these problems, then we proceed with an experiment.

• Scale: High volume or time intensive task
• Pattern: Repeatable structure or behavior
• Ambiguity: Needs perspective or ideation
• Redundancy: Been done before, will again
• Knots: Blocks or slows down people

If it checks all of the boxes, then we proceed with an experiment.

Staff AI experiments

At Crema, an explicit expectation has been set that every team member should be experimenting with and validating AI-assisted workflows.

We set a clear timeline for all craft teams to share learnings with the broader organization. Here are just a few examples of what was discovered.

Getting a complex app running locally without the headache

• Goal: Set up a full application stack (SQL Server, .NET API, Azure functions, React front end) to run locally on any developer's machine, eliminating dependency on slow virtual desktops and shared dev environments.

• Result: After spending most of a day working with Claude in Cursor, pointing it to config files and explaining how pieces connected, we got a fully containerized Docker setup that anyone can spin up. New developers onboard faster, everyone has their own test data, and teams can now make API and front end changes simultaneously without waiting on deployment pipelines. The time savings have been massive.

• Craft team: Development

• Tools used: Claude, Cursor

Building test cases for a natural language search feature

• Goal: Create a comprehensive test suite for a natural language search engine that needed to interpret real world user queries like "show me parcels over 200 acres in Austin, TX" or "I'm looking for big land near Austin."

• Result: Generated 150 test prompts covering basic queries, natural variations, edge cases, misspellings, and contradictory logic. Each prompt was tested by three different people. This organized approach helped us run a four hour team testing session that caught critical issues before client release. We plan to keep using this for features that rely on natural language interpretation.

• Craft team: Testing

• Tools used: Claude

Understanding team dynamics across organization levels

• Goal: Synthesize interview data from a product team assessment to identify how perspectives align or differ between organizational levels.

• Result: Used ChatGPT to organize interview notes, then prompted it to build a comparison matrix across org levels. This revealed gaps and patterns we might have missed or would have taken much longer to find manually.

• Craft team: Strategy

• Tools used: Claude

Reverse engineering and rebuilding an app from scratch

• Goal: Reverse engineer a messy, vibe coded project to understand its structure, then test whether a clean rebuild using better AI prompting could produce production quality code.

• Result: Used Cursor to map the original app into stories and epics, then started fresh with a data first approach. Basic development tasks were roughly 70% faster, especially for components, tests, and test data.

• Craft team: Development

• Tools used: Cursor, ChatGPT

Automating meeting registration and follow up workflows

• Goal: Learn to use Zapier to automate the repetitive, time intensive process of managing event registrations, which involved monitoring submissions, evaluating applicants, sending notifications, and updating multiple systems.

• Result: Built a workflow that handles the entire registration pipeline.

• Craft team: Operations

• Tools used: Zapier

Amplify human capacity

• Enable people to do more good work and more quickly
• Apply AI to improve contributions across many dimensions of humanity: creativity, productivity, perspective, accessibility, and more

Aim for impact

• Shorten the time it takes to shape, deliver, and learn
• Don’t simply use AI for the sake of itself
• Iterate more quickly
• Experiment often and find a way to tie it back to the work
• Prioritize solutions that deliver tangible business outcomes

Own the outcomes

• AI-enabled tools are not responsible for the outcomes provided to our clients - we are
• AI should not dictate our direction, but help us pursue it more clearly and quickly
• Ensure AI implementation enhances rather than replaces human judgment
• Be clear and forthright about where and how we’re using AI

Protect what matters

• Our clients trust us with crucial business details and we will treat that responsibility with the respect it demands
• Establish clear guidelines for data governance and update as needed
• Maintain consistent visibility and control over risk mitigation, ethical, security, and quality standards

Guided by our principles

These principles anchor us, ensuring AI amplifies our impact without compromising our standards.

Maintaining the same standards as before

Experimentation never comes at the expense of trust. Through this next wave of technology, we remain good stewards of our clients' businesses, data, and reputations.

Data protection comes first. We never input client specific or sensitive internal data into AI tools unless the tool is Crema approved, we have explicit stakeholder consent, and data is properly secured.

AI may assist, but Crema signs the work. Teams remain responsible for quality, accuracy, and ethical alignment. Final review and approval is always human led.

Everything is transparent to clients. We're open about when and how we use AI, and we love exploring the possibilities together. Intellectual property, user experiences, and critical business decisions are handled with care.

--- And that’s a promise.

Bright partnerships ahead

At this current phase, our goal is to experiment rapidly, understand the tradeoffs, prioritize what matters, and deliver work better. The reality is that nothing is set in stone with everything changing so quickly.

We invite you to explore this frontier with us. It’s going to be a worthwhile adventure.

Common questions around how we use AI tools

How do you know what you should use AI for?

We use the SPARK framework to figure this out quickly.

• Scale (high volume work)
• Pattern (repeatable tasks)
• Ambiguity (needs fresh perspective)
• Redundancy (been done before)
• Knots (team is blocked)

If yes to any of these, AI might help. We test it with a small experiment before committing. Not everything needs AI. Sometimes the straightforward approach is faster and better.

How do you spot AI hallucinations?

AI makes up stuff when it doesn't know the answer. It sounds confident either way. We verify anything that matters.

How do you find the right AI tool when there are so many options?

Finding the right tool takes experimentation. We run small tests internally before bringing anything to client work.