Intelligent Tech Problem Solving: Better Technical Decisions, Faster and with Less Risk

Technical problems cannot wait. A manufacturing line shutdown, an electrical substation failure, or a conveyor belt breakdown has a direct impact on business operations, costs, and safety. Yet technical teams continue to face the same long-standing challenge: The hardest part is not finding solutions, but identifying the best ones that will actually work.

The real challenge is not finding solutions. It is selecting the best one that will actually work and identifying the providers with proven experience

With tools such as Google or a generic AI, it is possible to generate a list of potential solutions to a problem within minutes. But these proposals are not enough:

• Are the sources of information credible? Is there solid technical evidence, and is the technology mature enough to be implemented? 
• Are these solutions applicable to my specific operating conditions, constraints and integration requirements?
• Are those the best options available, including analogous industries and without supplier bias?
• Do they solve the problem with mitigated risk and a viable estimated cost?

Many professionals face the same challenge: AI generates endless conversations with duplicated efforts and little convergence, while decisions are ultimately made more on intuition than on evidence.

A structured Tech Problem Solving process

Based on our experience with AI since early 2025, we realized that running five chats in parallel does not provide the level of efficiency and reliability required to solve technical challenges.

“We understood that AI would be a powerful tool combined with the expertise of our global solver network, but also with a structured, guided process based on problem-solving methodologies.”

This led us to create a new intelligent engineering problem-solving process built around four phases:

1. Challenge specification

Before looking for solutions, it is essential to properly frame the problem. Our platform now helps define the operational context, analyze root causes, identify constraints, and establish technical and economic objectives.

“This phase is critical: when a challenge is clearly defined, finding the right solution becomes much easier.”

2. Search for multiple solutions

Rather than limiting the search to the most obvious results, our platform now generates a broad range of alternatives by combining emerging technologies, patents, global use cases, and analogous industries.

“The goal is to expand the search space and identify solutions that would otherwise remain off the radar.”

3. Rapid solution evaluation

One of the biggest challenges is evaluating solutions we have not tested before, or technologies we are not familiar with. We now evaluate each solution using objective criteria: fit with the challenge context, technology maturity level (TRL), technical and economic feasibility, integration complexity, and risks or dependencies.

“The result of our evaluation is a ranked list of alternatives that supports knowledge-based - and not intuition-based - decision-making.”

4. Supplier scouting

The final phase automates the scouting of experienced suppliers — companies and startups aligned with each selected solution — so that teams can move quickly toward implementation.

“Intelligent supplier scouting helps us reduce risk by identifying partners, sometimes scale-ups or even startups, with proven experience implementing the solutions or technologies we are interested in.”

Beyond generic AI: what makes the difference

The difference between generic AI and our platform is not only technological; it is methodological. While generic AI starts from scratch with every query, provides qualitative assessments without explicit criteria, and mentions suppliers without validation, ennomotive’s platform delivers:

• Verified and traceable sources: patents, papers, suppliers, and use cases analyzed in real time, with links to the primary source for each solution.
• Knowledge Layer: proprietary collective knowledge and access to ennomotive’s expert engineering network.
• Structured evaluation: technical and economic feasibility criteria, gaps, risks, and TRL level.
• Qualified suppliers: verified companies aligned with each solution, without the need for additional manual search.
• Actionable output: not ideas to explore, but technical solutions ready for implementation.

Our new platform can be used easily and independently through a subscription. In addition, search results for solutions and suppliers can be saved and consulted later.

It can also be used with specialized support from one of our team experts, for example when working on a complex technical challenge.

Use case: protecting backup batteries in BTS stations

In areas with a high incidence of theft, batteries at BTS sites are frequent targets. When this happens, the result is mobile service disruption, loss of coverage, and high replacement and penalty costs for the operator.

The challenge was clear: to find a solution that would prevent theft, avoid increasing energy consumption, and remain compatible with the existing BTS infrastructure.

The platform explored solutions from analogous industries — ATMs, railway transport, critical telecommunications — and generated many evaluated and prioritized alternatives, including:

• Thermal-secure container (TRL 8–9): an anchored battery cassette with a concealed locking system, tamper sensors, and LTE/NB-IoT alarm. High technology maturity, ready for implementation.
• Battery with “no value outside the BTS” (TRL 7–8): unique electronic identification, chemical marking, and automatic locking if disconnected without authorization. Scalable and compatible with existing batteries.
• Battery with chemical self-disabling capability (TRL 5–6): an irreversible reaction triggered by tampering that renders the material unusable. This removes the economic incentive for theft, although environmental regulation must be carefully considered.

For each solution, the platform also identified the most suitable technology suppliers, including their profiles, technologies, and reasons for fit.

That simple.

“From a recurring and costly problem to a prioritized set of solutions and suppliers ready to contact.”

More than a decade of experience in engineering and innovation

Since 2015, ennomotive has operated as an engineering hub for innovation, with a presence in Europe and Latin America. With more than 120 completed projects in sectors such as manufacturing, mining, energy, construction, automotive, and infrastructure, and a network of more than 25,000 engineers and multi-sector startups, the company has evolved into an AI-powered Tech Problem Solving platform that structures, accelerates, and scales the resolution of technical challenges.

Do you have technical challenges? Contact us and we will show you how to solve them through our new Tech Problem Solving platform.