How can I scale an agency and break free from the billable hours model?

Over the past month, I’ve spoken with at least ten agency founders struggling with this exact question. As long as your business is tied to hours, growth means hiring more people and logging more time. That kind of scaling never feels truly scalable.

At the same time, a huge opportunity is emerging: AI can now reason incredibly well. It can recognize patterns, answer questions, and assess risks – often just as well as a consultant.

But there’s one thing AI doesn’t have by default: your unique data and experience. That’s what clients really pay you for: years of cases, mistakes, successes, and insights. And it’s exactly this knowledge you can embed in AI to make it scalable and accessible. In doing so, we democratize expertise – good news for both agencies and the organizations that rely on them.

In this post, I’ll show you how to turn your knowledge into a scalable business model through small steps and minimal investments. Almost all of these steps can be done yourself, without technical expertise or external help. We’ll use an example from foodtech (scaling up production processes), but the approach is applicable to any sector.

Example: Wok

A foodtech agency that supports companies in scaling up their production processes

Your clients ask you industry-specific questions. Your team helps them and bills for the hours spent. That works fine – but scaling the business only happens linearly, as your team grows. So you decide to capture your knowledge in an AI agent that can take over part of your work.

Step 1: Gather your data and experience

⏱ Investment: max one day

Consultants often think their value lies in conversations and reports. But the real value is in the experience and knowledge they’ve built up over the years. Let’s start by putting that knowledge on the table.

It’s great if you already have an internal knowledge base, but at this stage you can just gather some PDFs and spreadsheets. Think of white papers, sample input and output data, or materials from past projects. You can clean it up a bit, but it doesn’t need to be perfect yet.

You’ll naturally discover if the AI is missing important context – context that may still live in your head but hasn’t been made explicit yet.

Example case:

Wok has supported dozens of companies. Over the years, you’ve collected:

• Curated research papers on scaling fermentation processes

• An internal document with key lessons from projects:

  • which scale factors determine costs;

  • how long typical projects take;

  • which equipment combinations often cause delays.

• A spreadsheet of suppliers for critical components in production lines.

Step 2: Build a first AI demo

⏱ Investment: a few days

Instead of building a full platform right away, you can already test your knowledge and data with AI. Create a simple demo that works with parameters (input) and produces output similar to your advice.

I like using Anthropic’s Workbench, but you could also use the GPT editor from OpenAI.

• Add your data (context) and write a prompt so the AI agent can act like you normally would.

• Start a conversation and ask the kind of questions clients usually ask (using an anonymized project briefing).

• Decide in advance what a “good” answer looks like, based on your own past results.

• If the AI’s answers are 80–90% accurate, you’re ready to move to a Proof of Concept (see step 3). If not, start experimenting by adjusting:

  1. Context: add more or cleaner data

  2. Prompt: be more specific, or provide examples

  3. Model: try a different one (ChatGPT/Claude/Gemini, or a different version)

Example case:

Wok builds an AI model with the data from step 1 and a prompt describing your company and your role. Then you ask:

“How can I scale up our precision fermentation pilot to a factory producing 100 liters per hour? Process details: [batch size], [medium], [type of reactor].”

The AI responds with potential bottlenecks, a cost estimate, an expected timeline.

If it works well, it already feels like a digital consultant and shows your expertise can be scaled.

Tip: Also test the same questions with an AI without your custom data.

• If the AI still gives solid answers, your data may not be as unique as you thought – and clients could just ask ChatGPT themselves.

• If the AI falls flat, your data is clearly valuable context.

Step 3: Build a Proof of Concept web app and test with clients

⏱ Investment: 1–2 weeks

Once you have a working demo, translate it into a Proof of Concept web app. With tools like Lovable or Cursor, you can quickly create a simple version to show to clients.

The goal: to deliver a first platform-like experience that’s tangible and immediately testable in the market.

• Build a basic interface where users enter their parameters.

• Have the AI run the same logic you tested in step 2.

• Invite a handful of clients or prospects to try it.

• Ask how useful the output is, what they’re missing, and whether they’d pay for it.

Example case:

Wok builds a prototype web app where companies can ask scaling-related questions. The input and output are the same as in Step 2, but now it is a web app with the classic Wok branding all over it.

A cultivated meat scale-up tries the tool and says:

“If I had this earlier, I could’ve saved months in pilot runs.”

Another client asks:

“Can you also add supplier recommendations?”

With feedback like this, you can prioritize improvements and quickly refine your platform.

Step 4: Scale into a product

⏱ Investment: depends on scope and resources

Only once clients are enthusiastic and willing to pay does it make sense to invest in a scalable platform. At this stage, you can shape a new business model – for instance, through a subscription.

If you have an internal dev team, involve them. Otherwise, bring in a partner who can help you build at scale. (Feel free to message me – happy to think along.)

Example case:

Your PoC becomes the foundation of a new platform. Clients can log in and access your expertise, while you control the data and knowledge powering the AI agents.

• Simple questions can be handled directly in the platform, with you guaranteeing the quality of the output.

• For more complex cases, clients can upgrade to a “professional” plan that includes live sessions with your team.

• The platform can also serve as “aftercare” – once a project is finished, clients can still ask the AI questions about results and considerations.

In this way, your unique expertise helps more companies scale their alternative protein processes – and your impact grows.

Conclusion

Hopefully this gives you some practical steps to start turning your knowledge into a scalable business model.

AI can increasingly handle reasoning, but your data and experience remain unique. By packaging that knowledge into a platform, you can extend your impact, serve more clients, and unlock a new revenue stream alongside your consultancy.

The beauty: at the start, it only costs your time. Begin small with your data, build an AI demo, make a simple web app, test it with the market, and only scale once you have proof.

At Wolk, we help agencies and consultancies follow this journey in a smart and phased way – so you can unlock maximum value with minimal investment.

👉 Want to learn more or explore what this could look like for you? Feel free to send me a message.