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The Solver Case: How AI Turns Business Data into Gold

The Solver Case: How AI Turns Business Data into Gold

If there is one thing that any company anywhere in the world wants, it is to make the right decisions at the right time to make its business profitable and expand its horizons. Old-school methods relied on a blend of a nose for business, experience, leadership, and organization. Without relinquishing any of these factors, the age of artificial intelligence brings with it an additional linchpin: data.

In its most powerful form, data can be used to build predictive models. When will demand for a clothing brand’s e-commerce be highest? How likely is a factory machine going to break down in the next ten years? How can you better logistics by planning the best possible delivery routes? What price should a hotel charge for its rooms to attract more tourists?

Solver has the magic wand to turn thousands of statistics into business knowledge. Founded in 2016 as a spin-off from the Universidad Politécnica de Valencia (UPV), the startup began its journey through the hard work and vision of Jon Ander Gómez (CDO), Roberto Paredes (CTO), and Francisco Casacuberta.

“The idea was to market a technology that we knew was mature enough to help the business community,” said Jordi Mansanet (CEO). “AI is a very wide-ranging tool that can improve many processes, yet one common feature is extracting value from company data, automating operations that are not streamlined, and making the right decisions to attain levels of efficiency which would be impossible for humans to achieve.”

Solver’s software fits each customer like a glove. Today, with the current boom in AI, many companies are taking an interest in the Valencian startup’s solutions.

First of all, Solver explores the business model of the company in question to find out where it makes sense to apply artificial intelligence. “We are especially keen to learn what their challenges are and where they need to improve,” commented Mansanet. “Based on that we can then assess whether AI makes sense or not. Sufficient historical data is always a must for the answer to be ‘yes’.”

If industrial machinery is not fitted with sensors, there will also be no history to feed the algorithm’s knowledge. In contrast, if a retail business has previously digitally transformed its routine operations, AI can hone demand forecasts by 30% to 50%. The more data the platform receives, the better its predictions will be.

In tourism, which is highly digitalized in all its aspects (accommodation, flights, car rental, entertainment in the city visited), Solver moves like a fish in water. With its support, industry businesses can enhance the user experience and personalize purchasing processes by analyzing behavioral patterns and putting in place individualized strategies. Why didn’t the traveler book the hotel room they were looking at? At what point did they stop? What if they were emailed a deal? There are lots of options.

“If the project with a customer goes well, our AI usually spills over into other areas of the company,” pointed out Mansanet. “We normally set KPIs at the beginning of the job. This is how we quantify the project and how much the outcomes need to improve. We have extensive experience in a range of sectors: retail (fashion and food), industrial logistics, pharmaceuticals, and tourism.” Solver is also active in areas such as finance, insurance, and energy.

 

The AI chess board

Aside from the current crop of headliners (ChatGPT, DALL-E-2, Stable Diffusion, Midjourney), Mansanet said there are three types of companies competing on the artificial intelligence board. Some of them offer closed AI-based products. “This is not our business as it is extremely difficult to tailor these kinds of products to the customer’s specific needs.”

Another group are the more generalist, “almost consultancy” firms which are not specialists in this type of technology even though they are engaged in software development.

And then there are startups like Solver. “The fact that our niche is AI and we have a proven track record means we have a better chance of success” argued Mansanet. “There is not as much competition here.”

 

Entry barriers

Mansanet noted above that the secret of AI lies in the contracting company meticulously measuring its processes. These must be highly digitalized organizations. Once this requirement is met, the power of the algorithm is unleashed.

“A lot of companies put lots of resources into document analysis. If it takes a person 50 seconds to a minute to review a dossier and extract the fields by hand, AI will complete this task in two to three seconds.”

The algorithm cannot thrive without sufficient historical data. The other common mistake Mansanet identified has to do with the approach to AI projects. “It makes little sense for just the innovation department of a firm to be involved. Several departments in the company have to pitch in and everything must be geared towards addressing a business need.”

Next August Solver will be eight years old with sixteen professionals on its payroll (90% technology backgrounds). After consolidating its position in Spain with premium customers, the startup is now expanding internationally (finalizing agreement in Mexico) and taking part in two European Union flagship projects which might be joined by a third in 2024.

To round off two solutions it has successfully developed and tested in practice with numerous customers, the startup is soon to release two other tools: Forecaster (demand forecasting) and Docreader (document information search).

“AI has been around for a while even if it often works in a less showy way than ChatGPT,” said Mansanet. “This is no passing fad. We keep a close eye on what goes in universities, and we can see how fast this technology is evolving and the awesome impact it will have when it goes mainstream among users and companies.”

Solver is here to stay.

Apr 11 · 2024 GoHub Ventures