Geeks AI Agent Lab

The Geeks AI Agent Lab is a hands-on program designed to rapidly transform AI potential into a tangible prototype within a 5 -10 day window. It’s an experimental environment where ideas are tested and refined, offering you actionable insights and progress, even if every experiment doesn’t yield complete success.

The Lab is built to tackle real business challenges by identifying high-impact AI opportunities and advancing them into a working Proof of Concept (PoC) or pilot-ready solution. Whether you end up with a clear win, a partial success needing further R&D, or even discover that traditional automation is more feasible for your case, every outcome provides progress toward your broader digital transformation goals.

The Lab is structured in clearly defined phases:

• AI Opportunity Discovery: A collaborative workshop to pinpoint your most significant challenge and high-value opportunities.  And turning that into a very clear idea and scope that can be researched, implemented and tested in a few days. 

• Proof of Concept (PoC) Design & Development: Focused sessions where your team and our experts develop a working prototype based on the chosen opportunity.

• Evaluation & Iteration: After the first two days, we evaluate progress against mutually agreed-upon target outcomes and adjust the plan if needed. Daily progress reporting ensures alignment.

By design, the Lab is a space for experimentation. Not every experiment will result in a complete success, but all results are progress even if it is to invalidate an idea/investment case:

• Success or Failure: A tangible, measurable outcome can be achieved, whether it confirms the effectiveness of the solution or indicates a failure.

• Alternative Routes: It may reveal that while AI isn’t fully ready for the specific use case, traditional or AI-enhanced automation offers a more practical solution.

• Partial Success: Sometimes, the outcome might show partial success which still indicates a good business case / roi, or warrants more time in the lab for R&D to build an even better solution.

This experimental approach means every step is progress, and each result—positive or negative—drives future decision-making. 

If another promising idea surfaces during the experiment, we can estimate the additional time and cost to explore it further. However, our aim is to remain sharply focused on the established 5–10 day window. Any adjustments or expansions will be agreed upon mutually and will include updated outcome target dates and budget estimates.

Active participation from your team is essential. You will:

• Engage in the AI Opportunity Discovery workshop.

• Collaborate closely during the co-design and development phases.

• Contribute insights and feedback during daily progress reports. This collaborative effort ensures that the solution is tailored specifically to your business environment and challenges.

We emphasise transparency and agility through:

• Daily progress updates and reports.

• A review session after the first two days, where we reassess the project plan and set mutually agreed target dates and outcomes.

• Continuous feedback loops to ensure any new ideas are critically evaluated and, if pursued, scoped out before extending the lab’s timeline.

By keeping experiments short and iterative, and using the design phase to be clear and tight on scope management, risk is effectively contained. Each PoC is designed to:

• Validate feasibility before any large-scale roll-out.

• Use predefined success criteria and KPIs to objectively assess outcomes.

• Allow for pivoting—if the AI solution isn’t viable, identify whether automation offers a better route or if further R&D is necessary.

Success is defined in several ways:

• Complete Success: A fully functional prototype that meets or exceeds business objectives and could be used in a pilot test with a small group of test users.

• Failure: Clear evidence that the approach is not viable (e.g. the AI models are not capable yet or if they were, the ROI just isn’t there), which is valuable information that prevents larger investments in the wrong solution.

• Partial Success: Positive elements are identified, but further investment in refinement or additional R&D is suggested.

• Alternative Outcomes: Discovering that automation or another approach is more beneficial in the short to medium term than an immediate AI implementation.

This layered understanding of success helps you make informed decisions for future investments.

Yes, the Lab’s flexible framework is designed to work across industries; it’s about real business leaders solving real problems or harnessing real opportunities, fast.  From proptech and construction to energy and beyond. Our collaborative approach means the process is tailored to your industry’s unique requirements, ensuring relevant, scalable solutions.

At the end of the Lab, you receive:

• A working PoC and/or a clear decision indicating the next steps.

• A report outlining success metrics, areas for further improvement/investigation, and recommendations, whether that means pursuing automation, additional R&D, or full deployment and integration of an AI solution.

• A next steps plan that details any expanded work if new ideas need further exploration, including updated timelines and guide estimates/pricing.