Tomorrow at a glance


The customer

Tomorrow is a fintech startup with an emphasis on sustainability. The app contributes to a greener planet by routing interchange fees to rainforest protection projects.


The market

HQ'd in Germany, Tomorrow serves over 85,000 customers.


The support team

20 agents provide support in German and English Tomorrow customers in Europe. They handle an average of 5,000 requests per month.


The channels

Queries are made via chat (45%), email (37%), and phone (18%).


The situation

Tomorrow was looking to build and grow a support team from scratch. They wanted to maintain a closely-knit team while meeting the demands of scaling their product.


The solution

Tomorrow's virtual agent halved ticket processing times, and fully automated 30% of queries. They were able to offer higher quality support while keeping their team small.

Getting started with Ultimate

When Ron Gerber joined Tomorrow as Head of Customer Support, he was aware of the responsibility that came with building and growing a support team from the ground up. Sustainability is a deeply ingrained value throughout the company, so Gerber knew that he wanted to maintain a family-style culture in the team. He anticipated that automated solutions could optimize support processes to keep a growing base of customers happy without overburdening his human agents.

Gerber approached Ultimate because his team was unhappy with their first AI provider: They had been struggling with long outages and imprecise intent management. Already impressed with a sales journey that couldn’t have run any more smoothly, Tomorrow collaborated closely with Ultimate’s customer success team to more deeply understand and anticipate their customer’s needs. Tomorrow’s virtual agents seamlessly integrated into Freshchat, the company’s CRM system, and put their multilingual skills to work across all channels to support customers in German and English.

"The sales process was an outstanding experience. The team at Ultimate tried to really understand our needs by asking all the necessary questions rather than just showing a standard demo."

Ron Gerber
Head of Customer Support, Tomorrow

CRM Integration Freshworks_Outline
Automation rate 30%
Reduction in AHT 50%

AI use case spotlight: Multilingual chat automation, powered by NLP

  • Gerber and his team learned to use - and love - suggested answers and assisted replies powered by the AI’s Natural Language Processing (NLP) technology, which made intents substantially more reliable.

  • Gerber explains that he chose Ultimate because of its NLP capabilities. In his words, "Many competitors use technology that just translates the input language and then feeds it back to the AI, and we didn’t want our customers to get lost in translation."

  • The virtual agent converses fluently in English and German. Tomorrow also plans to add more European languages in the near future.

AI use case spotlight: halving AHT without compromising quality

  • With only 20 customer support agents handling an average of 5,000 requests per month, response times averaged at about 20 minutes per ticket.

  • Tomorrow reached out to Ultimate because they wanted to fulfil their brand promise: excellent customer support, always and without exception. And automation helped keep that promise. 

  • Virtual agents not only reduced the support team’s average handling time by 50%, they also ramped up overall automation to 30%. This meant Tomorrow’s close-knit team of human agents was able to work healthily, effectively, and sustainably. 

The success story



Through Natural Language Processing, Tomorrow was able to offer support in an expanding array of languages.



Through customer support automation, Tomorrow can handle a high volume of requests without compromising quality or growing their team.



Tomorrow's virtual agent has cut AHT in half, effectively automated 30% of queries, and freed agents up to focus on more complex tasks.


Happier agents

With the help of a virtual agent to handle the most repetitive tasks, the support team's lives were made much easier.

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