An up to 6-week, fixed-price proof of concept, built from day one for scalability, security and production.
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Ancient Greeks defined an axiom as the premise or starting point for further reasoning. Today, we follow the same principle as we borrow its name for our proprietary AI Proof of Concept process, allowing Engineering Leaders to validate thoroughly and safely their mission-critical activities before embarking on full-fledged development.
We have seen it time and time again. The board has given you an AI mandate, but as an Engineering Leader, you know the risks: 60% of leaders feel AI doesn’t boost productivity, and concerns about long-term code maintainability are rising. Rushed projects can create massive technical debt.
In a fixed-price, 6-week mission-critical process, we deliver an Enterprise-Ready AI PoC - built from day one for scalability, maintainability, and user experience.
The outcome: a functional core and a validated blueprint that ensures low risk, de-risks your investment and gives you a confident, data-backed roadmap.
Business Case: This stage lays the groundwork for the Proof of Concept (PoC). It begins with a structured workshop involving key stakeholders to surface the core business challenge that AI should address. Together, the team defines a single, well-scoped problem and clarifies what success would look like. A testable hypothesis is then formulated, along with measurable target metrics to track progress. By aligning stakeholders early on expectations, priorities, and limitations, this stage ensures everyone is on the same page before moving forward.
Data Assessment: Once the business problem is clear, attention shifts to the data that will power the POC. This involves identifying all relevant data sources and evaluating whether the data is sufficient, consistent, and complete. Any gaps or transformations required to make the data AI-ready are also mapped out. In parallel, performance metrics are defined so the AI model can be objectively evaluated later. This step ensures that the foundation is solid before development begins.
High-Level Architecture: With the problem and data defined, the next step is to outline the technical set-up. This involves selecting the most suitable technologies, tools, and platforms to support the POC. The architecture defines how data will flow from its source through storage and processing into the AI model, ensuring integration points are well understood. Decisions at this stage prioritise flexibility and speed of experimentation, making it easier to iterate quickly.
Training Data: Execution begins with preparing the data for model development. Relevant datasets are extracted, cleaned, and transformed into a format suitable for training. The dataset is staged in a way that supports iterative testing, ensuring it can evolve as the model improves. This step bridges the gap between raw business data and actionable AI-ready input.
AI Model: Once the training data is prepared, the focus shifts to building the AI model. Depending on the problem, this may involve selecting an existing algorithm, customising a pre-built service, or developing a bespoke solution. Domain expertise is woven into the design to make sure the model reflects real-world context, not just raw data.
Prototype: The model is then iteratively trained and refined. Hyper parameters are tuned, configurations adjusted, and performance continuously tested against the success metrics defined earlier. The goal here is not a polished final product, but a working prototype that demonstrates the potential of AI to solve the business problem in practice.
Feasibility Report: Once the prototype is in place, its results are carefully evaluated. Performance is compared against the original success metrics from the business case to determine if the POC objectives were achieved. At the same time, deployment options—cloud, on-premise, or hybrid—are assessed to understand what scaling could look like in practice.
Product Roadmap: The final step turns insights into action. A phased plan is developed for broader AI adoption, outlining the infrastructure, budget, and resources required for full-scale deployment. Team roles and skill requirements are also identified to ensure sustainable support. Beyond the initial use case, future opportunities are prioritised to create a strategic rollout sequence. This roadmap ensures technical feasibility, resource alignment, and long-term business impact.
NEED A QUOTE FOR YOUR PROJECT?
Our business developers, project managers and software engineers can help you to clarify any questions you have related. Feel free to chat with us anytime and get a quote for your project.
Developing your product with the Axiom process means gaining a reliable ally in your AI journey.
Accelerate innovation with a process designed to deliver impact fast, within a limited timeframe.
most to your business.
Enjoy full predictability and peace of mind with a clear, upfront cost structure.
Focus AI on the goals that matter most to your business.
Turn prepared and analysed data into smarter decisions.
Test prototypes quickly to confirm value and feasibility.
Benefit from models optimised for accuracy and impact.
Follow a clear roadmap to expand AI solutions seamlessly.
Reduce risk and strengthen readiness for full-scale implementation.
TOP SOFTWARE DEVELOPMENT
Goodfirms
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Clutch, 2023
TOP SOFTWARE DEVELOPERS
Acquisition Int., 2023
Bridging borders, serving clients in over 80 countries worldwide.
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With a 99% client satisfaction rate, Imaginary Cloud brings together a handpicked team of EU-based data science experts trusted by global enterprises. From machine learning engineers to cloud-certified data architects, our specialists combine deep analytical skills with a business-first mindset.
We deliver intelligent systems that are robust, explainable, and ready for scale. Whether you need end-to-end product delivery or a seamless extension to your in-house team, we lead every stage with speed, rigour, and strategic clarity.
Data Scientists
Experts in statistical modelling, machine learning, deep learning and optimisation. We translate business challenges into model-driven strategies.
Data Engineers
We build reliable pipelines and scalable infrastructure to ensure clean, timely and structured data for modelling.
ML Ops Specialists
Our team ensures that your models are reproducible, traceable and production-ready, with full lifecycle monitoring.
Project Managers
Agile-certified PMs ensure that data projects are delivered on time, within scope and with measurable impact.
Browse the Frequently Asked Questions and get your answers. Or better yet – get in touch with our team and let’s talk!
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Our team of business developers and project managers can help you to clarify any questions you have related. Feel free to chat with us anytime.
Artificial Intelligence (AI) has moved from being just a concept in science fiction to a major player in today's technology world. With predictions anticipating that the global AI market will hit a staggering trillion-dollar valuation by 2030.
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