Shaping a Next-Generation Job Platform for Luxury Hospitality
14
Days for the prototype
The deadline set so the team could go looking for investors.
90%
Of CVs usually wasted
By recruiters, which is the inefficiency the platform attacks.
15
Years of market experience
Behind the founder’s reading of the problem.
The client
Barometa’s founder had an idea for a job platform of the next generation, aimed at career-minded people in luxury hospitality, and chose Netguru as the technology partner to build it.
The challengeWhat the teams actually needed
Existing job boards and LinkedIn are inefficient in ways the founder knew well, and the prototype had to prove a better model inside two weeks.
Challenge 01The inefficiency to solve
The problem was what existing online job boards and LinkedIn do badly, not a gap in features.
Challenge 02Limited budget, strict schedule
The project had to fit a constrained budget and an unforgiving timetable.
Challenge 03Fourteen days
The prototype had to exist inside two weeks so the team could take it to investors.
What we did
A prototype for investors, in a fortnight
Two weeks and thin resources left the team leaning on expertise and sheer engagement to get the prototype finished on time.
Turned into objectives
The founder’s product vision was translated into functional objectives that could actually be built.
Where LinkedIn falls down
The flaws in LinkedIn’s model and its UX were identified, since that is what the product is positioned against.
The plan, followed
The plan developed during the scoping session was executed consistently, which is what makes a two-week deadline survivable.
Three days late, and enough
The work ran three days past schedule and the platform was still ready to put in front of investors — with an elite development team on it proving a useful argument in the funding conversation.
Client testimonial
It was great to find out during the scoping session that the Netguru team and I are on the same page. We came up with a plan to build a prototype in just a few weeks. This efficiency makes it possible for me to tweak the model once we get feedback from the first users.
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