How Agence Scroll is using AI to deliver faster without lowering quality

We are often asked if AI is just about going faster. At Scroll, we mainly use it to deliver more quickly without making the product fragile.

What does it change for you

When AI is used well, it reduces two sources of time loss: blur at the start, and surprises along the way.

Clearly, you are winning by three points.

First, the framing is sharper. You cut through the noise more quickly. We come out with a clearer MVP perimeter, with more precise acceptance criteria.

Then, we see the blockages earlier. Not at recipe time, when everything costs more to fix. From the moment of framing, we push scenarios, including edge cases.

Finally, we iterate more quickly on the concrete. Prototypes, components, documentation, tests. AI accelerates material production, then the team turns it into reliable deliverables.

If I had to summarize with simple numbers, these are our target orders of magnitude for classic web projects and business apps:

• 20 to 40% less time on the framing phase, at an equivalent perimeter
• an MVP that often comes out 2 to 4 weeks earlier, because decisions come faster
• less rework at the end of the project, because the edge cases were treated before coding

They're forks. They depend on the context, responsiveness on the client side, and IS constraints. But the logic remains the same: we accelerate the critical path, not the quantity of deliverables.

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What AI doesn't do at Scroll

It's important to say it clearly, because that's where quality comes in.

AI does not decide the product. She proposes. It helps to explore. The arbitrations remain human, and they are traced.

AI is no substitute for business knowledge. A business app is about rules, exceptions, rights, responsibilities. It cannot be guessed.

AI does not validate quality. For us, quality is a process. There are checks, tests, reviews, end criteria. We don't “cross our fingers” hoping that it will pass.

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Our principle: speed up clarity before speeding up code

Many projects don't get out of hand because of development. They're out of control because of a fuzzy start.

We've all seen these symptoms: a validated model, then a forgotten business rule. An external integration that blocks at the last moment. A table that gets slow when it goes from 500 rows to 50,000.

AI is helping us get these topics on the table early on. Not to be scary. To secure.

We use a simple approach: we invest more energy on questions that, if they arrive late, are very expensive.

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The Scroll pipeline: where AI comes in, and where the human cuts

To be clear, here is our delivery chain. I describe it without jargon.

We start from a business need. It is transformed into decisions. Then in deliverables.

AI is mainly involved in four tasks: structuring, exploring, writing, assisting production. The team, for its part, remains responsible: choosing, validating, testing, delivering.

This division avoids two classic pitfalls: quickly producing useless things, or quickly producing unstable things.

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Frame faster, without framing at a discount

Framing is where the calendar is played out. If you win one week here, you often win three later.

Our aim is to quickly come up with four actionable elements:

• an MVP perimeter that holds up
• acceptance criteria that can be understood by all
• a clear list of dependencies and risks
• a batch delivery plan

AI helps us to condense information. After a workshop or a series of exchanges, it allows us to produce a structured summary in a short time. But the synthesis is not “automatic”. We reread it, we challenge it, we complete it.

In practice, on many missions, we aim for a framework in 3 to 7 working days. It's a target. What matters is the result: less back and forth and less ambiguity.

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Anticipate blockages from the moment of framing, by pushing the scenarios

It is one of our most useful uses, and yet the easiest to explain.

We take your needs, then we force the exploration of the scenarios. Not just the happy path. Cases that happen on a Friday at 6 p.m. Input errors. Access rights. Incomplete states. Imports. Duplicates.

AI helps us generate a list of plausible, very broad situations, very quickly. Then, we sort it out. We keep what is real for your job.

This work has two measurable effects.
The first is to reduce technical surprises. An external integration, for example, is no longer a line in the corner of the backlog. We know what it involves.

The second is to write better acceptance criteria. The result: fewer misunderstandings, so less rework.

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Release an MVP more quickly, without delivering a fragile product

At Scroll, “MVP” doesn't mean “sloppy.” It means “the smallest product that provides clear value, with non-negotiable quality at critical points.”

What cannot be negotiated is fixed early. For example: basic security, the reliability of business rules, acceptable response times on key paths.

Then, we reduce the perimeter. You don't reduce the fundamentals.

AI helps a lot with the prioritization and clarity of user stories. It helps to reformulate, to break down, to identify dependencies. But prioritization is still a decision. And we take care of it with you.

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Prototype faster to validate, not to look pretty

A prototype is used to decide. It is not used to reassure.

When using tools from rapid prototyping, like Lovable, the objective is simple: to make an idea testable. We want a version that allows you to say “yes” or “no” to a hypothesis.

We also avoid a common trap: the misleading prototype. The one that gives the impression that everything is ready, while the IS reality, data, rights, and performance have not been processed.

To avoid this, we keep a rule: every prototype has its limits. What is validated, what is not, and what depends on technical constraints.

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Code components faster, with safeguards

AI is used to speed up the production of components, especially when there is repetition. Forms, tables, empty states, modals, display variants.

But there is an important nuance: generating code quickly is not a gain, if it creates debt.

So we frame. Conventions are imposed. We do magazines. We test what is critical.

In our internal standard, a contribution is not “finished” because it is displayed. It is finished when it passes a set of checks, often simple, but systematic: readability, consistency, error management, basic performance, and tests on important logic.

On projects with a lot of UI, this approach often saves several days, sometimes more, because we avoid redesigning poorly thought-out components.

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Plugins and integrated AI: the gain is real, if you know where to use it

There are more and more plugins and tools with built-in AI. The danger is to use them everywhere.

We use them where they excel: accelerate combinations, enrich non-sensitive content, produce variants, suggest leads, document.

And they are avoided where the risk is too high: business decisions, calculation rules, security, management of sensitive data.

This positioning is voluntary. It protects quality, and it also protects your trust.

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Quality, security, confidentiality: our rules are simple

We are often asked about data. It is normal.

Our basic rule is minimization. We don't share what's not necessary.

We avoid sending sensitive information to external tools. We anonymize when we need to illustrate a case. We remove identifiers, names, personal data. And we're working with representative snippets, not with your raw data.

On the quality side, we also apply a simple rule: AI can speed up production, but responsibility remains human. Architecture choices, validations, and arbitration decisions are reviewed and assumed by the team.

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Three typical examples, seen from the field

Here are situations that we often encounter. I describe them without going into confidential details.

First case: a frame that could have gone into a tunnel.
The need seems clear. Then business exceptions are discovered late. By exploring scenarios from the start, we identify these exceptions before coding. In this way, you avoid a recipe redesign, which often costs more than a week.

Second case: an interface where the challenge is adoption.
Before developing, we prototype quickly and we get people to react. In a few days, we learn what would have taken a month to discover in production. The following code is simpler because it fits a validated use.

Third case: a back office with a lot of recurring components.
We accelerate the creation of components, but above all, we stabilize a design system and patterns. The result: the speed increases as the project progresses, instead of decreasing.

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What we need to go fast without exhausting you

Delivering quickly is also a way of working.

You progress better when there is a sponsor who referees. Not every day. But when you have to choose.

We make better progress when the constraints are put in place early. IS, security, existing tools, business rules. Even if it's uncomfortable at first, it saves time later.

You progress better when you validate in short batches. Validation at the end is expensive. Regular validation costs little.

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Why this approach increases quality instead of reducing it

You might think that AI encourages you to go fast and to botch. In reality, when used well, it encourages formalization.

It requires you to write down what you do. It makes visible what was implied. It encourages you to test scenarios that you otherwise forget.

And above all, it frees up time for what really matters: understanding your business, making your own choices, and delivering a product that lasts over time.

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To go further

At Scroll, AI is not a shortcut. It is a lever for clarifying earlier, deciding more quickly, and producing with safeguards.

If you are looking for a partner that accelerates without sacrificing robustness, our method is made for that. There is nothing magical about it. Above all, she is disciplined, measured, and results-oriented.

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